f64 {\n 0.0\n };\n \n+ // Generation bonus: newer model generations get a quality bump\n+ let gen_bonus = models::generation_quality_bonus(model.architecture.as_deref(), &model.name);\n+\n // Quantization penalty\n let q_penalty = models::quant_quality_penalty(quant);\n \n@@ -1393,7 +1396,7 @@ fn quality_score(model: &LlmModel, quant: &str, use_case: UseCase) -> f64 {\n _ => 0.0,\n };\n \n- (base + family_bump + q_penalty + task_bump).clamp(0.0, 100.0)\n+ (base + family_bump + gen_bonus + q_penalty + task_bump).clamp(0.0, 100.0)\n }\n \n /// Speed score: normalize estimated TPS against target for the use case.\n@@ -1496,6 +1499,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n }\n }\n \n@@ -1684,6 +1688,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n };\n let mut system = test_system(64.0, true, Some(8.0));\n system.backend = GpuBackend::Cuda;\n@@ -1727,6 +1732,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n };\n let system = test_system(12.0, true, Some(8.0));\n \n@@ -1905,6 +1911,66 @@ mod tests {\n assert!(score_q4 > score_q2);\n }\n \n+ #[test]\n+ fn test_quality_score_generation_bonus() {\n+ // Qwen3.6-35B (gen 3.6) should score higher than Qwen2-72B (gen 2.0)\n+ // despite having fewer parameters\n+ let mut qwen36_35b = test_model(\"35B\", 20.0, Some(20.0));\n+ qwen36_35b.name = \"Qwen/Qwen3.6-35B-A3B\".to_string();\n+ qwen36_35b.architecture = Some(\"qwen3_5_moe\".to_string());\n+\n+ let mut qwen2_72b = test_model(\"72B\", 40.0, Some(40.0));\n+ qwen2_72b.name = \"Qwen/Qwen2.5-72B-Instruct\".to_string();\n+ qwen2_72b.architecture = Some(\"qwen2\".to_string());\n+\n+ let score_36 = quality_score(&qwen36_35b, \"Q4_K_M\", UseCase::General);\n+ let score_2 = quality_score(&qwen2_72b, \"Q4_K_M\", UseCase::General);\n+\n+ // Qwen3.6 (gen 3.5): base 89 + family 2 + gen_bonus 7.5 = 98.5\n+ // Qwen2.5 (gen 2.0): base 95 + family 2 + gen_bonus 3.0 = 100 (clamped)\n+ // With quant penalty (-5 each): 93.5 vs 95\n+ // The gen bonus narrows the gap significantly (was 89 vs 95 = 6pt gap,\n+ // now 93.5 vs 95 = 1.5pt gap)\n+ assert!(\n+ score_36 > score_2 - 3.0,\n+ \"Qwen3.6-35B ({}) should be within 3 points of Qwen2-72B ({})\",\n+ score_36,\n+ score_2\n+ );\n+ }\n+\n+ #[test]\n+ fn test_quality_score_generation_same_size() {\n+ // Same parameter count, different generation — newer should score higher\n+ let mut qwen3_8b = test_model(\"8B\", 5.0, Some(5.0));\n+ qwen3_8b.name = \"Qwen/Qwen3-8B\".to_string();\n+ qwen3_8b.architecture = Some(\"qwen3\".to_string());\n+\n+ let mut qwen2_7b = test_model(\"7B\", 4.0, Some(4.0));\n+ qwen2_7b.name = \"Qwen/Qwen2.5-7B-Instruct\".to_string();\n+ qwen2_7b.architecture = Some(\"qwen2\".to_string());\n+\n+ let score_3 = quality_score(&qwen3_8b, \"Q4_K_M\", UseCase::General);\n+ let score_2 = quality_score(&qwen2_7b, \"Q4_K_M\", UseCase::General);\n+\n+ assert!(\n+ score_3 > score_2,\n+ \"Qwen3-8B ({}) should score higher than Qwen2.5-7B ({})\",\n+ score_3,\n+ score_2\n+ );\n+ }\n+\n+ #[test]\n+ fn test_quality_score_no_generation_unchanged() {\n+ // Models without architecture info should score the same as before\n+ let model = test_model(\"7B\", 4.0, Some(4.0));\n+ let score = quality_score(&model, \"Q4_K_M\", UseCase::General);\n+\n+ // base 75 (7-10B) + family 0 + gen 0 + quant -5 + task 0 = 70\n+ assert!((score - 70.0).abs() < 0.01, \"Got {}\", score);\n+ }\n+\n #[test]\n fn test_weighted_score_composition() {\n let components = ScoreComponents {\n@@ -2432,6 +2498,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n }\n }\n \n@@ -2716,6 +2783,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n }\n }\n \n@@ -3002,6 +3070,7 @@ mod tests {\n } else {\n None\n },\n+ architecture: None,\n }\n }\n \ndiff --git a/llmfit-core/src/models.rs b/llmfit-core/src/models.rs\nindex 2cb25424..94d4980a 100644\n--- a/llmfit-core/src/models.rs\n+++ b/llmfit-core/src/models.rs\n@@ -111,6 +111,205 @@ pub fn quant_quality_penalty(quant: &str) -> f64 {\n }\n }\n \n+/// Parse model generation from architecture string and model name.\n+///\n+/// Returns a generation number (e.g. 2.0 for \"qwen2\", 3.5 for \"qwen3_5_moe\",\n+/// 4.0 for \"llama4\"). Returns `None` if generation cannot be determined.\n+pub fn parse_generation(architecture: Option<&str>, name: &str) -> Option<f64> {\n+ // Try architecture string first (most reliable)\n+ if let Some(arch) = architecture {\n+ let arch_lower = arch.to_lowercase();\n+ // DeepSeek: deepseek_v2, deepseek_v3, deepseek_v4, deepseek_vl_v2\n+ if arch_lower.starts_with(\"deepseek\") {\n+ if arch_lower.contains(\"v4\") {\n+ return Some(4.0);\n+ } else if arch_lower.contains(\"v3\") {\n+ return Some(3.0);\n+ } else if arch_lower.contains(\"v2\") {\n+ return Some(2.0);\n+ }\n+ return Some(1.0);\n+ }\n+ // Qwen: qwen2, qwen3, qwen3_moe, qwen3_5, qwen3_5_moe, qwen3_next\n+ if arch_lower.starts_with(\"qwen\") {\n+ let suffix = &arch_lower[\"qwen\".len()..];\n+ if suffix.starts_with(\"3_5\") || suffix.starts_with(\"3.5\") {\n+ return Some(3.5);\n+ }\n+ if suffix.starts_with(\"3_next\") || suffix.starts_with(\"3next\") {\n+ return Some(3.8);\n+ }\n+ if suffix.starts_with('3') {\n+ return Some(3.0);\n+ }\n+ if suffix.starts_with('2') {\n+ return Some(2.0);\n+ }\n+ if suffix.starts_with(\"1\") {\n+ return Some(1.0);\n+ }\n+ return Some(1.0);\n+ }\n+ // Llama: llama, llama4\n+ if arch_lower.starts_with(\"llama\") {\n+ let suffix = &arch_lower[\"llama\".len()..];\n+ if suffix.starts_with('4') {\n+ return Some(4.0);\n+ }\n+ // Architecture is just \"llama\" — fall through to name-based parsing\n+ }\n+ // Gemma: gemma, gemma2, gemma3, gemma4\n+ if arch_lower.starts_with(\"gemma\") {\n+ let suffix = &arch_lower[\"gemma\".len()..];\n+ if suffix.starts_with('4') {\n+ return Some(4.0);\n+ }\n+ if suffix.starts_with('3') {\n+ return Some(3.0);\n+ }\n+ if suffix.starts_with('2') {\n+ return Some(2.0);\n+ }\n+ return Some(1.0);\n+ }\n+ // Phi: phi, phi3, phimoe\n+ if arch_lower.starts_with(\"phi\") {\n+ let suffix = &arch_lower[\"phi\".len()..];\n+ if suffix.starts_with('4') {\n+ return Some(4.0);\n+ }\n+ if suffix.starts_with('3') || suffix.starts_with(\"moe\") {\n+ return Some(3.0);\n+ }\n+ if suffix.starts_with('2') {\n+ return Some(2.0);\n+ }\n+ return Some(1.0);\n+ }\n+ // Mistral/Mixtral: mistral, mixtral\n+ if arch_lower.starts_with(\"mistral\") || arch_lower.starts_with(\"mixtral\") {\n+ return Some(1.0);\n+ }\n+ // Cohere: cohere, cohere2\n+ if arch_lower.starts_with(\"cohere\") {\n+ let suffix = &arch_lower[\"cohere\".len()..];\n+ if suffix.starts_with('2') {\n+ return Some(2.0);\n+ }\n+ return Some(1.0);\n+ }\n+ // Falcon: falcon, falcon3\n+ if arch_lower.starts_with(\"falcon\") {\n+ let suffix = &arch_lower[\"falcon\".len()..];\n+ if suffix.starts_with('3') {\n+ return Some(3.0);\n+ }\n+ return Some(1.0);\n+ }\n+ // Granite: granite, granite4\n+ if arch_lower.starts_with(\"granite\") {\n+ let suffix = &arch_lower[\"granite\".len()..];\n+ if suffix.starts_with('4') {\n+ return Some(4.0);\n+ }\n+ if suffix.starts_with(\"moe\") {\n+ return Some(1.0);\n+ }\n+ return Some(1.0);\n+ }\n+ }\n+\n+ // Fallback: parse generation from model name\n+ let name_lower = name.to_lowercase();\n+\n+ // Qwen3.6, Qwen3.5, Qwen3, Qwen2.5, Qwen2\n+ if name_lower.contains(\"qwen3.6\") || name_lower.contains(\"qwen3_6\") {\n+ return Some(3.6);\n+ }\n+ if name_lower.contains(\"qwen3.5\") || name_lower.contains(\"qwen3_5\") {\n+ return Some(3.5);\n+ }\n+ if name_lower.contains(\"qwen3\") {\n+ return Some(3.0);\n+ }\n+ if name_lower.contains(\"qwen2.5\") || name_lower.contains(\"qwen2_5\") {\n+ return Some(2.5);\n+ }\n+ if name_lower.contains(\"qwen2\") {\n+ return Some(2.0);\n+ }\n+\n+ // Llama versions from name\n+ if name_lower.contains(\"llama-4\") || name_lower.contains(\"llama4\") {\n+ return Some(4.0);\n+ }\n+ if name_lower.contains(\"llama-3.3\") || name_lower.contains(\"llama3.3\") {\n+ return Some(3.3);\n+ }\n+ if name_lower.contains(\"llama-3.2\") || name_lower.contains(\"llama3.2\") {\n+ return Some(3.2);\n+ }\n+ if name_lower.contains(\"llama-3.1\") || name_lower.contains(\"llama3.1\") {\n+ return Some(3.1);\n+ }\n+ if name_lower.contains(\"llama-3\") || name_lower.contains(\"llama3\") {\n+ return Some(3.0);\n+ }\n+ if name_lower.contains(\"llama-2\") || name_lower.contains(\"llama2\") {\n+ return Some(2.0);\n+ }\n+\n+ // Gemma from name\n+ if name_lower.contains(\"gemma-4\") || name_lower.contains(\"gemma4\") {\n+ return Some(4.0);\n+ }\n+ if name_lower.contains(\"gemma-3\") || name_lower.contains(\"gemma3\") {\n+ return Some(3.0);\n+ }\n+ if name_lower.contains(\"gemma-2\") || name_lower.contains(\"gemma2\") {\n+ return Some(2.0);\n+ }\n+\n+ // DeepSeek from name\n+ if name_lower.contains(\"deepseek-v4\") || name_lower.contains(\"deepseekv4\") {\n+ return Some(4.0);\n+ }\n+ if name_lower.contains(\"deepseek-v3\") || name_lower.contains(\"deepseekv3\") {\n+ return Some(3.0);\n+ }\n+ if name_lower.contains(\"deepseek-v2\") || name_lower.contains(\"deepseekv2\") {\n+ return Some(2.0);\n+ }\n+\n+ // Phi from name\n+ if name_lower.contains(\"phi-4\") || name_lower.contains(\"phi4\") {\n+ return Some(4.0);\n+ }\n+ if name_lower.contains(\"phi-3\") || name_lower.contains(\"phi3\") {\n+ return Some(3.0);\n+ }\n+\n+ None\n+}\n+\n+/// Compute a generation-based quality bonus.\n+///\n+/// Each full generation above 1.0 adds a bonus to quality scoring.\n+/// This reflects the empirical observation that newer generations achieve\n+/// better quality-per-parameter than older ones.\n+///\n+/// Returns an additive bonus (0.0 if generation is unknown or <= 1.0).\n+pub fn generation_quality_bonus(architecture: Option<&str>, name: &str) -> f64 {\n+ let generation = match parse_generation(architecture, name) {\n+ Some(g) => g,\n+ None => return 0.0,\n+ };\n+\n+ // Each full generation above 1.0 adds +3 points.\n+ // Capped at +9 (gen 4.0) to avoid runaway scores.\n+ ((generation - 1.0) * 3.0).clamp(0.0, 9.0)\n+}\n+\n /// Model capability flags (orthogonal to UseCase).\n #[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]\n #[serde(rename_all = \"snake_case\")]\n@@ -311,6 +510,10 @@ pub struct LlmModel {\n /// Present in Qwen1.5-MoE, DeepSeek-V2, Qwen3.5-MoE.\n #[serde(default)]\n pub shared_expert_intermediate_size: Option<u32>,\n+ /// Model architecture string from HuggingFace config (e.g. \"qwen2\", \"llama4\",\n+ /// \"deepseek_v3\"). Used to infer model generation for quality scoring.\n+ #[serde(default)]\n+ pub architecture: Option<String>,\n }\n \n /// Composition of attention layers in a hybrid model.\n@@ -785,6 +988,8 @@ struct HfModelEntry {\n shared_expert_intermediate_size: Option<u32>,\n #[serde(default)]\n license: Option<String>,\n+ #[serde(default)]\n+ architecture: Option<String>,\n }\n \n const HF_MODELS_JSON: &str = include_str!(\"../data/hf_models.json\");\n@@ -940,6 +1145,7 @@ fn load_embedded() -> Vec<LlmModel> {\n vocab_size: e.vocab_size,\n shared_expert_intermediate_size: e.shared_expert_intermediate_size,\n license: e.license,\n+ architecture: e.architecture,\n };\n model.capabilities = Capability::infer(&model);\n // Auto-populate attention_layout from name heuristic for known\n@@ -1293,6 +1499,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n \n@@ -1374,6 +1581,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n assert_eq!(model.params_b(), 7.0);\n@@ -1409,6 +1617,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n assert_eq!(model.params_b(), 13.0);\n@@ -1444,6 +1653,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n assert_eq!(model.params_b(), 0.5);\n@@ -1479,6 +1689,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n \n@@ -1522,6 +1733,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n \n@@ -1571,6 +1783,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n assert!(dense_model.moe_active_vram_gb().is_none());\n@@ -1604,6 +1817,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n let vram = moe_model.moe_active_vram_gb();\n@@ -1645,6 +1859,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n assert!(dense_model.moe_offloaded_ram_gb().is_none());\n@@ -1678,6 +1893,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n let offloaded = moe_model.moe_offloaded_ram_gb();\n@@ -1721,6 +1937,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n assert_eq!(UseCase::from_model(&model), UseCase::Coding);\n@@ -1756,6 +1973,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n assert_eq!(UseCase::from_model(&model), UseCase::Embedding);\n@@ -1791,6 +2009,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n assert_eq!(UseCase::from_model(&model), UseCase::Reasoning);\n@@ -1844,6 +2063,7 @@ mod tests {\n vocab_size: None,\n moe_intermediate_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: Some(\"apache-2.0\".to_string()),\n },\n // Entry 2: higher params, higher context, ToolUse capability, MoE\n@@ -1879,6 +2099,7 @@ mod tests {\n vocab_size: None,\n moe_intermediate_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n },\n ]);\n@@ -2008,6 +2229,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n let caps = Capability::infer(&model);\n@@ -2046,6 +2268,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n let caps = Capability::infer(&model);\n@@ -2083,6 +2306,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n let caps = Capability::infer(&model);\n@@ -2119,6 +2343,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n };\n let caps = Capability::infer(&model);\n@@ -2291,6 +2516,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n }\n }\n@@ -2398,6 +2624,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n license: None,\n }\n }\n@@ -2544,4 +2771,113 @@ mod tests {\n assert!(q8_total < fp16_total);\n assert!(q4_total < q8_total);\n }\n+\n+ // ────────────────────────────────────────────────────────────────────\n+ // Generation parsing tests\n+ // ────────────────────────────────────────────────────────────────────\n+\n+ #[test]\n+ fn test_parse_generation_from_architecture() {\n+ // Qwen family\n+ assert_eq!(parse_generation(Some(\"qwen2\"), \"\"), Some(2.0));\n+ assert_eq!(parse_generation(Some(\"qwen3\"), \"\"), Some(3.0));\n+ assert_eq!(parse_generation(Some(\"qwen3_moe\"), \"\"), Some(3.0));\n+ assert_eq!(parse_generation(Some(\"qwen3_5_moe\"), \"\"), Some(3.5));\n+ assert_eq!(parse_generation(Some(\"qwen3_5\"), \"\"), Some(3.5));\n+ assert_eq!(parse_generation(Some(\"qwen3_next\"), \"\"), Some(3.8));\n+\n+ // DeepSeek family\n+ assert_eq!(parse_generation(Some(\"deepseek\"), \"\"), Some(1.0));\n+ assert_eq!(parse_generation(Some(\"deepseek_v2\"), \"\"), Some(2.0));\n+ assert_eq!(parse_generation(Some(\"deepseek_v3\"), \"\"), Some(3.0));\n+ assert_eq!(parse_generation(Some(\"deepseek_v4\"), \"\"), Some(4.0));\n+\n+ // Llama family\n+ assert_eq!(parse_generation(Some(\"llama4\"), \"\"), Some(4.0));\n+\n+ // Gemma family\n+ assert_eq!(parse_generation(Some(\"gemma\"), \"\"), Some(1.0));\n+ assert_eq!(parse_generation(Some(\"gemma2\"), \"\"), Some(2.0));\n+ assert_eq!(parse_generation(Some(\"gemma3\"), \"\"), Some(3.0));\n+ assert_eq!(parse_generation(Some(\"gemma4\"), \"\"), Some(4.0));\n+\n+ // Phi family\n+ assert_eq!(parse_generation(Some(\"phi\"), \"\"), Some(1.0));\n+ assert_eq!(parse_generation(Some(\"phi3\"), \"\"), Some(3.0));\n+\n+ // Unknown architecture\n+ assert_eq!(parse_generation(Some(\"unknown_arch\"), \"\"), None);\n+ }\n+\n+ #[test]\n+ fn test_parse_generation_from_name_fallback() {\n+ // Llama (architecture is just \"llama\" so falls through to name)\n+ assert_eq!(\n+ parse_generation(Some(\"llama\"), \"meta-llama/Llama-3.1-8B\"),\n+ Some(3.1)\n+ );\n+ assert_eq!(\n+ parse_generation(Some(\"llama\"), \"meta-llama/Llama-2-7B\"),\n+ Some(2.0)\n+ );\n+\n+ // Name-only (no architecture)\n+ assert_eq!(parse_generation(None, \"Qwen/Qwen3.6-35B-A3B\"), Some(3.6));\n+ assert_eq!(parse_generation(None, \"Qwen/Qwen2.5-72B\"), Some(2.5));\n+ assert_eq!(\n+ parse_generation(None, \"deepseek-ai/DeepSeek-V4-Flash\"),\n+ Some(4.0)\n+ );\n+ assert_eq!(parse_generation(None, \"google/gemma-3-12b-it\"), Some(3.0));\n+ }\n+\n+ #[test]\n+ fn test_generation_quality_bonus_values() {\n+ // Gen 1.0: bonus = 0\n+ assert_eq!(generation_quality_bonus(Some(\"deepseek\"), \"\"), 0.0);\n+ // Gen 2.0: bonus = 3\n+ assert_eq!(generation_quality_bonus(Some(\"qwen2\"), \"\"), 3.0);\n+ // Gen 3.0: bonus = 6\n+ assert_eq!(generation_quality_bonus(Some(\"qwen3\"), \"\"), 6.0);\n+ // Gen 3.5: bonus = 7.5\n+ assert_eq!(generation_quality_bonus(Some(\"qwen3_5_moe\"), \"\"), 7.5);\n+ // Gen 4.0: bonus = 9 (capped)\n+ assert_eq!(generation_quality_bonus(Some(\"deepseek_v4\"), \"\"), 9.0);\n+ // No architecture: bonus = 0\n+ assert_eq!(generation_quality_bonus(None, \"some-unknown-model\"), 0.0);\n+ }\n+\n+ #[test]\n+ fn test_generation_coverage_on_embedded_database() {\n+ let db = ModelDatabase::new();\n+ let models = db.get_all_models();\n+\n+ let mut has_gen = 0;\n+ let mut total_known_family = 0;\n+\n+ for model in models {\n+ let name_lower = model.name.to_lowercase();\n+ let is_known_family = [\"qwen\", \"llama\", \"deepseek\", \"gemma\", \"phi\", \"mistral\"]\n+ .iter()\n+ .any(|f| name_lower.contains(f));\n+\n+ if is_known_family {\n+ total_known_family += 1;\n+ let generation = parse_generation(model.architecture.as_deref(), &model.name);\n+ if generation.is_some() {\n+ has_gen += 1;\n+ }\n+ }\n+ }\n+\n+ // At least 80% of known-family models should have parseable generation\n+ let coverage = has_gen as f64 / total_known_family as f64;\n+ assert!(\n+ coverage > 0.80,\n+ \"Generation coverage for known families is only {:.1}% ({}/{})\",\n+ coverage * 100.0,\n+ has_gen,\n+ total_known_family\n+ );\n+ }\n }\ndiff --git a/llmfit-core/src/plan.rs b/llmfit-core/src/plan.rs\nindex e19616cd..daa2d2d9 100644\n--- a/llmfit-core/src/plan.rs\n+++ b/llmfit-core/src/plan.rs\n@@ -866,6 +866,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n }\n }\n \ndiff --git a/llmfit-core/src/update.rs b/llmfit-core/src/update.rs\nindex b3cfe2bf..49f0e64b 100644\n--- a/llmfit-core/src/update.rs\n+++ b/llmfit-core/src/update.rs\n@@ -307,6 +307,8 @@ fn estimate_ram(\n /// optional because configs vary wildly across architectures.\n #[derive(Deserialize, Debug, Default)]\n struct HfConfig {\n+ #[serde(default)]\n+ model_type: Option<String>,\n #[serde(default)]\n num_hidden_layers: Option<u32>,\n #[serde(default)]\n@@ -538,6 +540,8 @@ fn map_to_llm_model(hf: HfApiModel, token: Option<&str>) -> Option<LlmModel> {\n (None, None, None, None, None, None, None, None)\n };\n \n+ let architecture = cfg.as_ref().and_then(|c| c.model_type.clone());\n+\n Some(LlmModel {\n name: hf.id.clone(),\n provider,\n@@ -570,6 +574,7 @@ fn map_to_llm_model(hf: HfApiModel, token: Option<&str>) -> Option<LlmModel> {\n moe_intermediate_size,\n vocab_size,\n shared_expert_intermediate_size,\n+ architecture,\n })\n }\n \ndiff --git a/llmfit-tui/src/display.rs b/llmfit-tui/src/display.rs\nindex 7d99a452..c6424350 100644\n--- a/llmfit-tui/src/display.rs\n+++ b/llmfit-tui/src/display.rs\n@@ -964,6 +964,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n },\n fit_level: FitLevel::Good,\n run_mode,\ndiff --git a/llmfit-tui/src/main.rs b/llmfit-tui/src/main.rs\nindex b4184f8a..fed5b712 100644\n--- a/llmfit-tui/src/main.rs\n+++ b/llmfit-tui/src/main.rs\n@@ -2860,6 +2860,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n },\n fit_level,\n run_mode: RunMode::Gpu,\n@@ -2945,6 +2946,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n },\n LlmModel {\n name: \"Qwen/Qwen3-Coder-Next\".to_string(),\n@@ -2975,6 +2977,7 @@ mod tests {\n moe_intermediate_size: None,\n vocab_size: None,\n shared_expert_intermediate_size: None,\n+ architecture: None,\n },\n ];\n \ndiff --git a/scripts/validate_generation_scoring.py b/scripts/validate_generation_scoring.py\nnew file mode 100644\nindex 00000000..ca5cd82f\n--- /dev/null\n+++ b/scripts/validate_generation_scoring.py\n@@ -0,0 +1,360 @@\n+#!/usr/bin/env python3\n+\"\"\"\n+Validate generation-aware quality scoring on the scraped model database.\n+\n+Loads hf_models.json and applies the same generation parsing logic as the Rust\n+code to demonstrate that newer model generations are scored appropriately.\n+\"\"\"\n+\n+import json\n+import re\n+import sys\n+from collections import defaultdict\n+\n+\n+def parse_generation(architecture: str | None, name: str) -> float | None:\n+ \"\"\"Mirror of models::parse_generation in Rust.\"\"\"\n+ if architecture:\n+ arch = architecture.lower()\n+\n+ # DeepSeek\n+ if arch.startswith(\"deepseek\"):\n+ if \"v4\" in arch:\n+ return 4.0\n+ elif \"v3\" in arch:\n+ return 3.0\n+ elif \"v2\" in arch:\n+ return 2.0\n+ return 1.0\n+\n+ # Qwen\n+ if arch.startswith(\"qwen\"):\n+ suffix = arch[len(\"qwen\"):]\n+ if suffix.startswith(\"3_5\") or suffix.startswith(\"3.5\"):\n+ return 3.5\n+ if suffix.startswith(\"3_next\") or suffix.startswith(\"3next\"):\n+ return 3.8\n+ if suffix.startswith(\"3\"):\n+ return 3.0\n+ if suffix.startswith(\"2\"):\n+ return 2.0\n+ if suffix.startswith(\"1\"):\n+ return 1.0\n+ return 1.0\n+\n+ # Llama\n+ if arch.startswith(\"llama\"):\n+ suffix = arch[len(\"llama\"):]\n+ if suffix.startswith(\"4\"):\n+ return 4.0\n+ # fall through to name\n+\n+ # Gemma\n+ if arch.startswith(\"gemma\"):\n+ suffix = arch[len(\"gemma\"):]\n+ if suffix.startswith(\"4\"):\n+ return 4.0\n+ if suffix.startswith(\"3\"):\n+ return 3.0\n+ if suffix.startswith(\"2\"):\n+ return 2.0\n+ return 1.0\n+\n+ # Phi\n+ if arch.startswith(\"phi\"):\n+ suffix = arch[len(\"phi\"):]\n+ if suffix.startswith(\"4\"):\n+ return 4.0\n+ if suffix.startswith(\"3\") or suffix.startswith(\"moe\"):\n+ return 3.0\n+ if suffix.startswith(\"2\"):\n+ return 2.0\n+ return 1.0\n+\n+ # Mistral/Mixtral\n+ if arch.startswith(\"mistral\") or arch.startswith(\"mixtral\"):\n+ return 1.0\n+\n+ # Cohere\n+ if arch.startswith(\"cohere\"):\n+ suffix = arch[len(\"cohere\"):]\n+ if suffix.startswith(\"2\"):\n+ return 2.0\n+ return 1.0\n+\n+ # Falcon\n+ if arch.startswith(\"falcon\"):\n+ suffix = arch[len(\"falcon\"):]\n+ if suffix.startswith(\"3\"):\n+ return 3.0\n+ return 1.0\n+\n+ # Granite\n+ if arch.startswith(\"granite\"):\n+ suffix = arch[len(\"granite\"):]\n+ if suffix.startswith(\"4\"):\n+ return 4.0\n+ return 1.0\n+\n+ # Fallback: name-based\n+ name_lower = name.lower()\n+\n+ if \"qwen3.6\" in name_lower or \"qwen3_6\" in name_lower:\n+ return 3.6\n+ if \"qwen3.5\" in name_lower or \"qwen3_5\" in name_lower:\n+ return 3.5\n+ if \"qwen3\" in name_lower:\n+ return 3.0\n+ if \"qwen2.5\" in name_lower or \"qwen2_5\" in name_lower:\n+ return 2.5\n+ if \"qwen2\" in name_lower:\n+ return 2.0\n+\n+ if \"llama-4\" in name_lower or \"llama4\" in name_lower:\n+ return 4.0\n+ if \"llama-3.3\" in name_lower or \"llama3.3\" in name_lower:\n+ return 3.3\n+ if \"llama-3.2\" in name_lower or \"llama3.2\" in name_lower:\n+ return 3.2\n+ if \"llama-3.1\" in name_lower or \"llama3.1\" in name_lower:\n+ return 3.1\n+ if \"llama-3\" in name_lower or \"llama3\" in name_lower:\n+ return 3.0\n+ if \"llama-2\" in name_lower or \"llama2\" in name_lower:\n+ return 2.0\n+\n+ if \"gemma-4\" in name_lower or \"gemma4\" in name_lower:\n+ return 4.0\n+ if \"gemma-3\" in name_lower or \"gemma3\" in name_lower:\n+ return 3.0\n+ if \"gemma-2\" in name_lower or \"gemma2\" in name_lower:\n+ return 2.0\n+\n+ if \"deepseek-v4\" in name_lower:\n+ return 4.0\n+ if \"deepseek-v3\" in name_lower:\n+ return 3.0\n+ if \"deepseek-v2\" in name_lower:\n+ return 2.0\n+\n+ if \"phi-4\" in name_lower or \"phi4\" in name_lower:\n+ return 4.0\n+ if \"phi-3\" in name_lower or \"phi3\" in name_lower:\n+ return 3.0\n+\n+ return None\n+\n+\n+def generation_bonus(architecture: str | None, name: str) -> float:\n+ gen = parse_generation(architecture, name)\n+ if gen is None:\n+ return 0.0\n+ return min((gen - 1.0) * 3.0, 9.0)\n+\n+\n+def params_b(model: dict) -> float:\n+ \"\"\"Extract parameter count in billions.\"\"\"\n+ if model.get(\"parameters_raw\"):\n+ return model[\"parameters_raw\"] / 1e9\n+ pc = model.get(\"parameter_count\", \"\")\n+ m = re.search(r\"([\\d.]+)\\s*[Bb]\", pc)\n+ if m:\n+ return float(m.group(1))\n+ m = re.search(r\"([\\d.]+)\\s*[Mm]\", pc)\n+ if m:\n+ return float(m.group(1)) / 1000\n+ return 0.0\n+\n+\n+def quality_score_old(model: dict) -> float:\n+ \"\"\"Old scoring without generation bonus.\"\"\"\n+ params = params_b(model)\n+ if params < 1.0:\n+ base = 30.0\n+ elif params < 3.0:\n+ base = 45.0\n+ elif params < 7.0:\n+ base = 60.0\n+ elif params < 10.0:\n+ base = 75.0\n+ elif params < 20.0:\n+ base = 82.0\n+ elif params < 40.0:\n+ base = 89.0\n+ else:\n+ base = 95.0\n+\n+ name_lower = model[\"name\"].lower()\n+ if \"qwen\" in name_lower:\n+ family_bump = 2.0\n+ elif \"deepseek\" in name_lower:\n+ family_bump = 3.0\n+ elif \"llama\" in name_lower:\n+ family_bump = 2.0\n+ elif \"mistral\" in name_lower or \"mixtral\" in name_lower:\n+ family_bump = 1.0\n+ elif \"gemma\" in name_lower:\n+ family_bump = 1.0\n+ else:\n+ family_bump = 0.0\n+\n+ return min(base + family_bump - 5.0, 100.0) # -5 for Q4_K_M penalty\n+\n+\n+def quality_score_new(model: dict) -> float:\n+ \"\"\"New scoring with generation bonus.\"\"\"\n+ params = params_b(model)\n+ if params < 1.0:\n+ base = 30.0\n+ elif params < 3.0:\n+ base = 45.0\n+ elif params < 7.0:\n+ base = 60.0\n+ elif params < 10.0:\n+ base = 75.0\n+ elif params < 20.0:\n+ base = 82.0\n+ elif params < 40.0:\n+ base = 89.0\n+ else:\n+ base = 95.0\n+\n+ name_lower = model[\"name\"].lower()\n+ if \"qwen\" in name_lower:\n+ family_bump = 2.0\n+ elif \"deepseek\" in name_lower:\n+ family_bump = 3.0\n+ elif \"llama\" in name_lower:\n+ family_bump = 2.0\n+ elif \"mistral\" in name_lower or \"mixtral\" in name_lower:\n+ family_bump = 1.0\n+ elif \"gemma\" in name_lower:\n+ family_bump = 1.0\n+ else:\n+ family_bump = 0.0\n+\n+ gen_bonus = generation_bonus(model.get(\"architecture\"), model[\"name\"])\n+\n+ return min(base + family_bump + gen_bonus - 5.0, 100.0) # -5 for Q4_K_M penalty\n+\n+\n+def main():\n+ data_path = \"data/hf_models.json\"\n+ with open(data_path) as f:\n+ models = json.load(f)\n+\n+ total = len(models)\n+ print(f\"Loaded {total} models from {data_path}\\n\")\n+\n+ # Count models with parseable generation\n+ gen_counts = defaultdict(int)\n+ family_gens = defaultdict(set)\n+ no_gen = 0\n+ has_gen = 0\n+\n+ for m in models:\n+ gen = parse_generation(m.get(\"architecture\"), m[\"name\"])\n+ if gen is not None:\n+ has_gen += 1\n+ gen_counts[gen] += 1\n+ # Extract family\n+ name_lower = m[\"name\"].lower()\n+ for fam in [\"qwen\", \"llama\", \"deepseek\", \"gemma\", \"phi\", \"mistral\", \"falcon\", \"granite\"]:\n+ if fam in name_lower:\n+ family_gens[fam].add(gen)\n+ break\n+ else:\n+ no_gen += 1\n+\n+ print(f\"Generation coverage: {has_gen}/{total} ({100*has_gen/total:.1f}%)\")\n+ print(f\"No generation info: {no_gen}/{total} ({100*no_gen/total:.1f}%)\\n\")\n+\n+ print(\"Generation distribution:\")\n+ for gen in sorted(gen_counts.keys()):\n+ print(f\" Gen {gen:>4.1f}: {gen_counts[gen]:>4} models\")\n+\n+ print(f\"\\nFamilies with multiple generations ({len([f for f in family_gens if len(family_gens[f]) > 1])}):\")\n+ for fam in sorted(family_gens.keys()):\n+ gens = sorted(family_gens[fam])\n+ if len(gens) > 1:\n+ print(f\" {fam:>10}: gens {', '.join(f'{g:.1f}' for g in gens)}\")\n+\n+ # Show specific ranking improvements\n+ print(\"\\n\" + \"=\" * 70)\n+ print(\"KEY COMPARISONS: Generation-aware scoring fixes\")\n+ print(\"=\" * 70)\n+\n+ comparisons = [\n+ (\"Qwen/Qwen3.6-35B-A3B\", \"Qwen/Qwen2.5-72B-Instruct\"),\n+ (\"Qwen/Qwen3-8B\", \"Qwen/Qwen2.5-7B-Instruct\"),\n+ (\"google/gemma-4-E2B-it\", \"google/gemma-2-2b-it\"),\n+ (\"google/gemma-3-27b-it\", \"google/gemma-2-27b-it\"),\n+ ]\n+\n+ model_lookup = {m[\"name\"]: m for m in models}\n+\n+ for newer_name, older_name in comparisons:\n+ newer = model_lookup.get(newer_name)\n+ older = model_lookup.get(older_name)\n+ if not newer or not older:\n+ print(f\"\\n SKIP: {newer_name} or {older_name} not found in database\")\n+ continue\n+\n+ old_newer = quality_score_old(newer)\n+ old_older = quality_score_old(older)\n+ new_newer = quality_score_new(newer)\n+ new_older = quality_score_new(older)\n+\n+ newer_gen = parse_generation(newer.get(\"architecture\"), newer[\"name\"])\n+ older_gen = parse_generation(older.get(\"architecture\"), older[\"name\"])\n+\n+ print(f\"\\n {newer_name} (gen {newer_gen}, {params_b(newer):.1f}B)\")\n+ print(f\" vs {older_name} (gen {older_gen}, {params_b(older):.1f}B)\")\n+ print(f\" OLD: {old_newer:.1f} vs {old_older:.1f} (gap: {old_older - old_newer:+.1f})\")\n+ print(f\" NEW: {new_newer:.1f} vs {new_older:.1f} (gap: {new_older - new_newer:+.1f})\")\n+\n+ if old_older - old_newer > new_older - new_newer:\n+ print(f\" ✓ Gap narrowed by {(old_older - old_newer) - (new_older - new_newer):.1f} points\")\n+ if new_newer > new_older:\n+ print(f\" ✓ RANKING FIXED: newer model now scores higher\")\n+\n+ # Show top models by new score in each family\n+ print(\"\\n\" + \"=\" * 70)\n+ print(\"TOP 5 BY QUALITY SCORE (new vs old) — selected families\")\n+ print(\"=\" * 70)\n+\n+ for family in [\"qwen\", \"llama\", \"gemma\", \"deepseek\"]:\n+ family_models = [m for m in models if family in m[\"name\"].lower()]\n+ # Sort by new score, take top 5\n+ family_models.sort(key=lambda m: quality_score_new(m), reverse=True)\n+ print(f\"\\n {family.upper()} (top 5):\")\n+ for m in family_models[:5]:\n+ gen = parse_generation(m.get(\"architecture\"), m[\"name\"])\n+ old = quality_score_old(m)\n+ new = quality_score_new(m)\n+ print(f\" {m['name'][:50]:<50} gen={gen} old={old:5.1f} new={new:5.1f} Δ={new-old:+.1f}\")\n+\n+ # Summary stats\n+ print(\"\\n\" + \"=\" * 70)\n+ print(\"SUMMARY\")\n+ print(\"=\" * 70)\n+\n+ score_changes = []\n+ for m in models:\n+ old = quality_score_old(m)\n+ new = quality_score_new(m)\n+ if new != old:\n+ score_changes.append((m[\"name\"], old, new, new - old))\n+\n+ print(f\"\\n Models with score changes: {len(score_changes)}/{total} ({100*len(score_changes)/total:.1f}%)\")\n+ print(f\" Models unchanged: {total - len(score_changes)}/{total}\")\n+\n+ if score_changes:\n+ deltas = [x[3] for x in score_changes]\n+ print(f\" Average score increase: {sum(deltas)/len(deltas):+.2f}\")\n+ print(f\" Max score increase: {max(deltas):+.1f}\")\n+ print(f\" Min score increase: {min(deltas):+.1f}\")\n+\n+\n+if __name__ == \"__main__\":\n+ main()\n"},"changed_files":{"kind":"list like","value":["data/hf_models.json","llmfit-core/src/fit.rs","llmfit-core/src/models.rs","llmfit-core/src/plan.rs","llmfit-core/src/update.rs","llmfit-tui/src/display.rs","llmfit-tui/src/main.rs","scripts/validate_generation_scoring.py"],"string":"[\n \"data/hf_models.json\",\n \"llmfit-core/src/fit.rs\",\n \"llmfit-core/src/models.rs\",\n \"llmfit-core/src/plan.rs\",\n \"llmfit-core/src/update.rs\",\n \"llmfit-tui/src/display.rs\",\n \"llmfit-tui/src/main.rs\",\n \"scripts/validate_generation_scoring.py\"\n]","isConversational":false},"review_threads":{"kind":"list like","value":[],"string":"[]","isConversational":false},"test_patch":{"kind":"string","value":""},"merged":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":490,"cells":{"repo":{"kind":"string","value":"AlexsJones/llmfit"},"pr_number":{"kind":"number","value":548,"string":"548"},"task_type":{"kind":"string","value":"issue_to_patch"},"issue_text":{"kind":"string","value":""},"pr_title":{"kind":"string","value":"fix(docker): skip HTTP probe on Linux when Docker Desktop is not running"},"pr_body":{"kind":"string","value":"## Problem\n\nDocker Model Runner is a Docker Desktop feature. On Linux, Docker Desktop is uncommon, so the startup HTTP probe to `localhost:12434` times out after ~800ms on most Linux systems, slowing down every llmfit startup.\n\n## Solution\n\nAdd `is_docker_desktop_running()` which checks for Docker Desktop's presence by:\n1. Looking for its socket at `/run/docker-desktop/docker.sock` or `~/.docker/desktop/docker.sock`\n2. Falling back to checking if `DOCKER_MODEL_RUNNER_HOST` is explicitly set\n\nIf Docker Desktop isn't running on Linux, skip the HTTP probe entirely and return `available=false` immediately.\n\n## Changes\n\n- `llmfit-core/src/providers.rs`: Added `is_docker_desktop_running()` helper and early return in `DockerModelRunnerProvider::detect_with_installed()`\n\n## Testing\n\n- On Linux without Docker Desktop: startup is now instant (no 800ms delay)\n- On Linux with Docker Desktop: probe still works normally\n- On macOS/Windows: no change in behavior (check only applies to Linux via `cfg!(target_os = \"linux\")`)\n- `cargo check` passes cleanly"},"base_sha":{"kind":"string","value":"7677837fad8ce970e8f32b90315617722b535fef"},"head_sha":{"kind":"string","value":"c77f3089eccb4ca20817ac954014b02b76b53102"},"gold_diff":{"kind":"string","value":"diff --git a/llmfit-core/src/hardware.rs b/llmfit-core/src/hardware.rs\nindex d9937ff7..24fbbe25 100644\n--- a/llmfit-core/src/hardware.rs\n+++ b/llmfit-core/src/hardware.rs\n@@ -579,7 +579,7 @@ impl SystemSpecs {\n // Filter out integrated GPUs (iGPUs) that have very little VRAM.\n // rocm-smi reports all GPU agents including iGPUs on APUs like\n // Ryzen 9800X3D, which would otherwise inflate the GPU count.\n- // Discrete GPUs have > 2 GB VRAM; iGPUs typically show <= 2 GB.\n+ // Discrete GPUs have > 2 GB VRAM; iGPUs typically show <= 2 GB.\n const IGPU_VRAM_THRESHOLD: u64 = 2 * 1024 * 1024 * 1024; // 2 GB\n let discrete_vram: Vec<u64> = per_gpu_vram_bytes\n .iter()\ndiff --git a/llmfit-core/src/providers.rs b/llmfit-core/src/providers.rs\nindex 3e70013a..8a8eccfb 100644\n--- a/llmfit-core/src/providers.rs\n+++ b/llmfit-core/src/providers.rs\n@@ -1415,6 +1415,28 @@ pub struct DockerModelRunnerProvider {\n base_url: String,\n }\n \n+/// Check if Docker Desktop is running on Linux by looking for its socket or process.\n+/// Returns `true` if Docker Desktop appears to be active, `false` otherwise.\n+/// This avoids a slow HTTP timeout on Linux systems without Docker Desktop.\n+fn is_docker_desktop_running() -> bool {\n+ // Docker Desktop on Linux creates a specific socket path\n+ if std::path::Path::new(\"/run/docker-desktop/docker.sock\").exists()\n+ || std::path::Path::new(\n+ &std::env::var(\"HOME\")\n+ .map(|h| format!(\"{h}/.docker/desktop/docker.sock\"))\n+ .unwrap_or_default(),\n+ )\n+ .exists()\n+ {\n+ return true;\n+ }\n+ // Fall back to checking if the DOCKER_MODEL_RUNNER_HOST env var is explicitly set\n+ // to a non-empty value (an empty string means the user hasn't configured it).\n+ std::env::var(\"DOCKER_MODEL_RUNNER_HOST\")\n+ .map(|v| !v.trim().is_empty())\n+ .unwrap_or(false)\n+}\n+\n fn normalize_docker_mr_host(raw: &str) -> Option<String> {\n let host = raw.trim();\n if host.is_empty() {\n@@ -1464,6 +1486,13 @@ impl DockerModelRunnerProvider {\n /// Single-pass startup probe.\n /// Returns `(available, installed_models, count)`.\n pub fn detect_with_installed(&self) -> (bool, HashSet<String>, usize) {\n+ // Docker Model Runner is a Docker Desktop feature. On Linux, Docker Desktop\n+ // is uncommon. Skip the HTTP probe if Docker Desktop is not running to avoid\n+ // a ~800ms timeout on every startup.\n+ if cfg!(target_os = \"linux\") && !is_docker_desktop_running() {\n+ return (false, HashSet::new(), 0);\n+ }\n+\n let mut set = HashSet::new();\n let Ok(resp) = ureq::get(&self.models_url())\n .config()\n@@ -4300,4 +4329,30 @@ mod tests {\n assert_eq!(normalize_vllm_host(\"\"), None);\n assert_eq!(normalize_vllm_host(\" \"), None);\n }\n+\n+ #[test]\n+ fn test_docker_desktop_running_via_env_var() {\n+ // Test 1: Non-empty value should detect Docker Desktop\n+ unsafe {\n+ std::env::set_var(\"DOCKER_MODEL_RUNNER_HOST\", \"localhost:12434\");\n+ }\n+ assert!(is_docker_desktop_running());\n+\n+ // Test 2: Empty string should NOT count as running\n+ unsafe {\n+ std::env::set_var(\"DOCKER_MODEL_RUNNER_HOST\", \"\");\n+ }\n+ assert!(!is_docker_desktop_running());\n+\n+ // Test 3: Whitespace-only should NOT count as running\n+ unsafe {\n+ std::env::set_var(\"DOCKER_MODEL_RUNNER_HOST\", \" \");\n+ }\n+ assert!(!is_docker_desktop_running());\n+\n+ // Cleanup\n+ unsafe {\n+ std::env::remove_var(\"DOCKER_MODEL_RUNNER_HOST\");\n+ }\n+ }\n }\n"},"changed_files":{"kind":"list like","value":["llmfit-core/src/hardware.rs","llmfit-core/src/providers.rs"],"string":"[\n \"llmfit-core/src/hardware.rs\",\n \"llmfit-core/src/providers.rs\"\n]","isConversational":false},"review_threads":{"kind":"list like","value":[],"string":"[]","isConversational":false},"test_patch":{"kind":"string","value":""},"merged":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":491,"cells":{"repo":{"kind":"string","value":"AlexsJones/llmfit"},"pr_number":{"kind":"number","value":546,"string":"546"},"task_type":{"kind":"string","value":"issue_to_patch"},"issue_text":{"kind":"string","value":""},"pr_title":{"kind":"string","value":"chore(main): release 0.9.24"},"pr_body":{"kind":"string","value":":robot: I have created a release *beep* *boop*\n---\n\n\n## [0.9.24](https://github.com/AlexsJones/llmfit/compare/v0.9.23...v0.9.24) (2026-05-12)\n\n\n### Features\n\n* add MCP server mode and NATS event publishing to llmfit serve ([#545](https://github.com/AlexsJones/llmfit/issues/545)) ([c4958f7](https://github.com/AlexsJones/llmfit/commit/c4958f7edb59225ffd569779fd8442915720bf03))\n\n\n### Bug Fixes\n\n* **hardware:** fix iGPU filtering on Ryzen 9 9950X where 2 GB iGPU passes the >= 2 GB discrete GPU threshold ([#543](https://github.com/AlexsJones/llmfit/issues/543)) ([c655bc1](https://github.com/AlexsJones/llmfit/commit/c655bc1b2e10b5b0a13a123344755e450832a06d))\n* MoE offload speed overestimate for Qwen3-Coder-Next ([#544](https://github.com/AlexsJones/llmfit/issues/544)) ([5bdc9df](https://github.com/AlexsJones/llmfit/commit/5bdc9df9d8e28168fdc4650741be71b715a11fc6))\n\n---\nThis PR was generated with [Release Please](https://github.com/googleapis/release-please). See [documentation](https://github.com/googleapis/release-please#release-please)."},"base_sha":{"kind":"string","value":"c655bc1b2e10b5b0a13a123344755e450832a06d"},"head_sha":{"kind":"string","value":"f20bfd61c16f6e76ea2b1f1295c5424ca7a4e4a5"},"gold_diff":{"kind":"string","value":"diff --git a/.release-please-manifest.json b/.release-please-manifest.json\nindex e0891d89..9f7b2b3e 100644\n--- a/.release-please-manifest.json\n+++ b/.release-please-manifest.json\n@@ -1,3 +1,3 @@\n {\n- \".\": \"0.9.23\"\n+ \".\": \"0.9.24\"\n }\ndiff --git a/CHANGELOG.md b/CHANGELOG.md\nindex 71cf341a..01f06c74 100644\n--- a/CHANGELOG.md\n+++ b/CHANGELOG.md\n@@ -1,5 +1,18 @@\n # Changelog\n \n+## [0.9.24](https://github.com/AlexsJones/llmfit/compare/v0.9.23...v0.9.24) (2026-05-12)\n+\n+\n+### Features\n+\n+* add MCP server mode and NATS event publishing to llmfit serve ([#545](https://github.com/AlexsJones/llmfit/issues/545)) ([c4958f7](https://github.com/AlexsJones/llmfit/commit/c4958f7edb59225ffd569779fd8442915720bf03))\n+\n+\n+### Bug Fixes\n+\n+* **hardware:** fix iGPU filtering on Ryzen 9 9950X where 2 GB iGPU passes the >= 2 GB discrete GPU threshold ([#543](https://github.com/AlexsJones/llmfit/issues/543)) ([c655bc1](https://github.com/AlexsJones/llmfit/commit/c655bc1b2e10b5b0a13a123344755e450832a06d))\n+* MoE offload speed overestimate for Qwen3-Coder-Next ([#544](https://github.com/AlexsJones/llmfit/issues/544)) ([5bdc9df](https://github.com/AlexsJones/llmfit/commit/5bdc9df9d8e28168fdc4650741be71b715a11fc6))\n+\n ## [0.9.23](https://github.com/AlexsJones/llmfit/compare/v0.9.22...v0.9.23) (2026-05-10)\n \n \ndiff --git a/Cargo.lock b/Cargo.lock\nindex abe529df..3f4e9d18 100644\n--- a/Cargo.lock\n+++ b/Cargo.lock\n@@ -2531,7 +2531,7 @@ checksum = \"11d3d7f243d5c5a8b9bb5d6dd2b1602c0cb0b9db1621bafc7ed66e35ff9fe092\"\n \n [[package]]\n name = \"llmfit\"\n-version = \"0.9.23\"\n+version = \"0.9.24\"\n dependencies = [\n \"arboard\",\n \"assert_cmd\",\n@@ -2556,7 +2556,7 @@ dependencies = [\n \n [[package]]\n name = \"llmfit-core\"\n-version = \"0.9.23\"\n+version = \"0.9.24\"\n dependencies = [\n \"dirs\",\n \"http\",\n@@ -2571,7 +2571,7 @@ dependencies = [\n \n [[package]]\n name = \"llmfit-desktop\"\n-version = \"0.9.23\"\n+version = \"0.9.24\"\n dependencies = [\n \"llmfit-core\",\n \"serde\",\ndiff --git a/Cargo.toml b/Cargo.toml\nindex 3b4954d7..df5145ea 100644\n--- a/Cargo.toml\n+++ b/Cargo.toml\n@@ -4,5 +4,5 @@ default-members = [\"llmfit-core\", \"llmfit-tui\"]\n resolver = \"3\"\n \n [workspace.package]\n-version = \"0.9.23\"\n+version = \"0.9.24\"\n license = \"MIT\"\ndiff --git a/version.txt b/version.txt\nindex 68a59457..37d8b0b7 100644\n--- a/version.txt\n+++ b/version.txt\n@@ -1,1 +1,1 @@\n-0.9.23\n+0.9.24\n"},"changed_files":{"kind":"list like","value":[".release-please-manifest.json","CHANGELOG.md","Cargo.lock","Cargo.toml","version.txt"],"string":"[\n \".release-please-manifest.json\",\n \"CHANGELOG.md\",\n \"Cargo.lock\",\n \"Cargo.toml\",\n \"version.txt\"\n]","isConversational":false},"review_threads":{"kind":"list like","value":[],"string":"[]","isConversational":false},"test_patch":{"kind":"string","value":""},"merged":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":492,"cells":{"repo":{"kind":"string","value":"AlexsJones/llmfit"},"pr_number":{"kind":"number","value":543,"string":"543"},"task_type":{"kind":"string","value":"issue_to_patch"},"issue_text":{"kind":"string","value":""},"pr_title":{"kind":"string","value":"fix(hardware): fix iGPU filtering on Ryzen 9 9950X with 2 GB iGPU"},"pr_body":{"kind":"string","value":"On systems with an AMD Ryzen 9 9950X (or similar APUs with a Granite Ridge iGPU), rocm-smi reports two GPU agents: the discrete GPU and the integrated GPU. The iGPU filter in detect_amd_gpu_rocm_info() uses a 2 GB VRAM threshold with >= to distinguish discrete GPUs from iGPUs.\r\n\r\nThe Ryzen 9 9950X's Granite Ridge iGPU has exactly 2 GB of VRAM (2,147,483,648 bytes), so it passes the >= check and is counted as a discrete GPU. This results in llmfit system reporting gpu_count: 2 with both labeled as the discrete GPU's name, even though only one discrete GPU is present.\r\n\r\nChange >= to > so that GPUs with exactly 2 GB VRAM (the iGPU boundary) are excluded, while all discrete GPUs which have well over 2 GB are still included. The fallback path for iGPU-only systems (the discrete_vram.is_empty() branch) still handles the case where no discrete GPU is found.\r\n\r\nSystem details:\r\n- CPU: AMD Ryzen 9 9950X 16-Core Processor\r\n- Discrete GPU: AMD Radeon AI PRO R9700 (32 GB VRAM)\r\n- iGPU: AMD Radeon Graphics / Granite Ridge (2 GB VRAM, PCI ID 1002:13C0)\r\n- ROCm SMI version: 4.0.0, ROCm SMI LIB: 7.8.0"},"base_sha":{"kind":"string","value":"05e884fcd68a5650afa010c2e93f790233017929"},"head_sha":{"kind":"string","value":"aac98543ec025386d32304f199a30484a9191ae1"},"gold_diff":{"kind":"string","value":"diff --git a/llmfit-core/src/hardware.rs b/llmfit-core/src/hardware.rs\nindex f34125db..d9937ff7 100644\n--- a/llmfit-core/src/hardware.rs\n+++ b/llmfit-core/src/hardware.rs\n@@ -579,12 +579,12 @@ impl SystemSpecs {\n // Filter out integrated GPUs (iGPUs) that have very little VRAM.\n // rocm-smi reports all GPU agents including iGPUs on APUs like\n // Ryzen 9800X3D, which would otherwise inflate the GPU count.\n- // Discrete GPUs have >= 2 GB VRAM; iGPUs typically show < 1 GB.\n+ // Discrete GPUs have > 2 GB VRAM; iGPUs typically show <= 2 GB.\n const IGPU_VRAM_THRESHOLD: u64 = 2 * 1024 * 1024 * 1024; // 2 GB\n let discrete_vram: Vec<u64> = per_gpu_vram_bytes\n .iter()\n .copied()\n- .filter(|&v| v >= IGPU_VRAM_THRESHOLD)\n+ .filter(|&v| v > IGPU_VRAM_THRESHOLD)\n .collect();\n let (effective_vram, gpu_count) = if discrete_vram.is_empty() {\n // No discrete GPUs found; use all entries (may be an iGPU-only system)\n"},"changed_files":{"kind":"list like","value":["llmfit-core/src/hardware.rs"],"string":"[\n \"llmfit-core/src/hardware.rs\"\n]","isConversational":false},"review_threads":{"kind":"list like","value":[],"string":"[]","isConversational":false},"test_patch":{"kind":"string","value":""},"merged":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":493,"cells":{"repo":{"kind":"string","value":"AlexsJones/llmfit"},"pr_number":{"kind":"number","value":544,"string":"544"},"task_type":{"kind":"string","value":"issue_to_patch"},"issue_text":{"kind":"string","value":""},"pr_title":{"kind":"string","value":"fix: MoE offload speed overestimate for Qwen3-Coder-Next"},"pr_body":{"kind":"string","value":"## Summary\n- Fix wrong `active_parameters` (3B → 5.46B) for `Qwen/Qwen3-Coder-Next` in model catalog\n- Fix MoeOffload speed path skipping `run_mode_factor` (0.8) — early return predates RunModeFactors system\n\n## Problem\nLLMFit estimated **28.3 tok/s** for Qwen3-Coder-Next in MoEOffload mode. Actual measured speed on RX 6900 XT is **10.3 tok/s** — a 2.74x overestimate.\n\n## Root Causes\n\n### 1. Wrong active_parameters in catalog\n`Qwen/Qwen3-Coder-Next` had `active_parameters: 3000000000` (3B). Every other variant of the same model (`unsloth/Qwen3-Coder-Next`, `Qwen/Qwen3-Next-80B-A3B-Instruct`, etc.) has `active_parameters: ~5462052994` (5.46B). The GGUF scraping got a wrong value for this one entry.\n\nImpact alone: 28.3 → 15.5 tok/s (still 1.50x overestimate)\n\n### 2. MoeOffload path skips run_mode_factor\nThe MoeOffload early-return at `fit.rs:1085` was written in commit `e1f3245` (April 19) BEFORE `RunModeFactors` was added in commit `7794a29`. Every other estimation path applies `mode_factor`, but MoeOffload doesn't.\n\nImpact with both fixes: 28.3 → **12.4 tok/s** (1.20x overestimate, within ±30% tolerance)\n\n## Validation\nBenchmarks run with `llama-bench` on RX 6900 XT (16GB VRAM, 512 GB/s, DDR4):\n\n| Model | Measured | LLMFit Fixture | Gap |\n|-------|----------|---------------|-----|\n| OLMoE-1B-7B Q4_K_M | 257.1 tok/s | 258.2 | 0.4% |\n| Qwen1.5-MoE Q4_K_M | 130.9 tok/s | 128.7 | 1.7% |\n| DeepSeek-V2-Lite Q4_K_M | 123.0 tok/s | 123.8 | 0.6% |\n| Qwen3.5-35B Q2_K_XL | 78.4 tok/s | 79.6 | 1.5% |\n\nAll GPU-mode MoE models unchanged by this fix. All 358 existing tests pass.\n\n## Test plan\n- [x] `cargo test --release` — 358 passed, 0 failed\n- [x] GPU-mode MoE estimates unchanged (spot-checked 5 models)\n- [x] Qwen3-Coder-Next MoeOffload estimate: 28.3 → 12.4 tok/s (measured: 10.3)\n- [x] Debug output: `LLMFIT_DEBUG=1 llmfit recommend` shows correct DDR bandwidth calculation\n\n👾 Generated with [Letta Code](https://letta.com)"},"base_sha":{"kind":"string","value":"05e884fcd68a5650afa010c2e93f790233017929"},"head_sha":{"kind":"string","value":"72e64c1472e3776de9e4ecd9009007ce18143adc"},"gold_diff":{"kind":"string","value":"diff --git a/llmfit-core/data/hf_models.json b/llmfit-core/data/hf_models.json\nindex 6070caa4..e357ab2e 100644\n--- a/llmfit-core/data/hf_models.json\n+++ b/llmfit-core/data/hf_models.json\n@@ -52497,7 +52497,7 @@\n \"is_moe\": true,\n \"num_experts\": 512,\n \"active_experts\": 10,\n- \"active_parameters\": 3000000000,\n+ \"active_parameters\": 5462052994,\n \"gguf_sources\": [\n {\n \"repo\": \"unsloth/Qwen3-Coder-Next-GGUF\",\n@@ -56853,4 +56853,4 @@\n \"active_experts\": 6,\n \"active_parameters\": 49000000000\n }\n-]\n\\ No newline at end of file\n+]\ndiff --git a/llmfit-core/src/fit.rs b/llmfit-core/src/fit.rs\nindex e9c960d3..7ab06e4a 100644\n--- a/llmfit-core/src/fit.rs\n+++ b/llmfit-core/src/fit.rs\n@@ -1082,7 +1082,8 @@ fn estimate_tps(\n gpu_compute_time,\n 1.0 / total_time\n );\n- return (1.0 / total_time).max(0.1);\n+ let mode_factor = config.run_mode_factors.for_run_mode(run_mode);\n+ return ((1.0 / total_time) * mode_factor).max(0.1);\n }\n \n // GPU mode: MoE model fits in VRAM with ALL expert weights loaded.\n"},"changed_files":{"kind":"list like","value":["llmfit-core/data/hf_models.json","llmfit-core/src/fit.rs"],"string":"[\n \"llmfit-core/data/hf_models.json\",\n \"llmfit-core/src/fit.rs\"\n]","isConversational":false},"review_threads":{"kind":"list like","value":[],"string":"[]","isConversational":false},"test_patch":{"kind":"string","value":""},"merged":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":494,"cells":{"repo":{"kind":"string","value":"AlexsJones/llmfit"},"pr_number":{"kind":"number","value":545,"string":"545"},"task_type":{"kind":"string","value":"issue_to_patch"},"issue_text":{"kind":"string","value":""},"pr_title":{"kind":"string","value":"feat: add MCP server mode and NATS event publishing"},"pr_body":{"kind":"string","value":"## Summary\n\n- Adds `llmfit serve --mcp` which runs an MCP server over stdio, exposing 6 tools for agent discovery (system specs, model recommendations, search, hardware planning, runtime checks, installed models)\n- Adds `llmfit serve --send-events` (behind `nats` cargo feature) to publish hardware/model events to NATS on `llmfit.{event_type}.{hostname}` subjects for Sympozium membrane integration\n- Extracts shared formatting logic into `serve_shared.rs` to avoid duplication between HTTP and MCP code paths\n\n## Test plan\n\n- [x] All 387 existing tests pass (`cargo test`)\n- [x] Builds without `nats` feature (default)\n- [x] Builds with `nats` feature (`cargo build --features nats`)\n- [x] MCP `initialize` → returns server info\n- [x] MCP `tools/list` → returns all 6 tools with correct JSON schemas\n- [x] MCP `tools/call get_system_specs` → returns hardware JSON\n- [x] MCP `tools/call recommend_models` → returns filtered model list\n- [x] HTTP `llmfit serve` unchanged behavior verified\n- [ ] NATS integration test with local NATS server"},"base_sha":{"kind":"string","value":"05e884fcd68a5650afa010c2e93f790233017929"},"head_sha":{"kind":"string","value":"e74e250c84b6ae8d823757d75404e7dfc91dad32"},"gold_diff":{"kind":"string","value":"diff --git a/API.md b/API.md\nindex 34e6febd..4889c910 100644\n--- a/API.md\n+++ b/API.md\n@@ -224,6 +224,107 @@ curl \"http://127.0.0.1:8787/api/v1/models/top?limit=5&min_fit=good&use_case=codi\n curl \"http://127.0.0.1:8787/api/v1/models/Mistral?runtime=any\"\n ```\n \n+---\n+\n+## MCP Server Mode\n+\n+llmfit can run as an MCP (Model Context Protocol) server over stdio, making it discoverable by AI agents (Claude, Cursor, etc.).\n+\n+### Start the MCP server\n+\n+```sh\n+llmfit serve --mcp\n+```\n+\n+Global hardware overrides still apply:\n+\n+```sh\n+llmfit --memory 24G --ram 64G serve --mcp\n+```\n+\n+### MCP client configuration\n+\n+Add to your MCP client config (e.g. `claude_desktop_config.json`):\n+\n+```json\n+{\n+ \"mcpServers\": {\n+ \"llmfit\": {\n+ \"command\": \"llmfit\",\n+ \"args\": [\"serve\", \"--mcp\"]\n+ }\n+ }\n+}\n+```\n+\n+### Available tools\n+\n+| Tool | Description | Parameters |\n+|------|-------------|------------|\n+| `get_system_specs` | Node hardware info (RAM, GPU, CPU) | None |\n+| `recommend_models` | Top models for this hardware | `limit?`, `use_case?`, `min_fit?`, `runtime?`, `license?`, `sort?` |\n+| `search_models` | Free-text model search | `query`, `limit?` |\n+| `plan_hardware` | Hardware requirements for a model | `model`, `context?`, `quant?`, `target_tps?` |\n+| `get_runtimes` | Installed inference runtimes | None |\n+| `get_installed_models` | Models in local runtimes | None |\n+\n+---\n+\n+## NATS Event Publishing\n+\n+When built with the `nats` feature, llmfit can publish hardware and model events to NATS for integration with coordination systems (e.g. Sympozium membrane).\n+\n+### Build with NATS support\n+\n+```sh\n+cargo build --features nats\n+```\n+\n+### Enable event publishing\n+\n+```sh\n+llmfit serve --send-events --nats-url nats://localhost:4222\n+llmfit serve --mcp --send-events # also works with MCP mode\n+```\n+\n+The `NATS_URL` environment variable is also supported.\n+\n+### Event subjects\n+\n+Events are published to `llmfit.{event_type}.{hostname}`:\n+\n+| Subject | Trigger | Payload |\n+|---------|---------|---------|\n+| `llmfit.system.{hostname}` | Startup + every 60s | System hardware specs |\n+| `llmfit.fit.{hostname}` | After fit analysis | Model fit summary |\n+| `llmfit.plan.{hostname}` | After plan estimate | Plan estimate |\n+| `llmfit.runtimes.{hostname}` | Startup + on query | Runtime availability |\n+| `llmfit.installed.{hostname}` | Startup + on query | Installed models |\n+\n+### Event envelope\n+\n+All events are wrapped in a common envelope:\n+\n+```json\n+{\n+ \"timestamp\": \"1747058400\",\n+ \"hostname\": \"worker-1\",\n+ \"event_type\": \"system\",\n+ \"version\": \"1\",\n+ \"data\": { ... }\n+}\n+```\n+\n+### Subscribe to events\n+\n+```sh\n+nats sub 'llmfit.>' # all events from all nodes\n+nats sub 'llmfit.system.>' # system specs from all nodes\n+nats sub 'llmfit.system.worker-1' # system specs from specific node\n+```\n+\n+---\n+\n ## Versioning notes\n \n Current API prefix is `v1`.\ndiff --git a/Cargo.lock b/Cargo.lock\nindex 9329e8be..abe529df 100644\n--- a/Cargo.lock\n+++ b/Cargo.lock\n@@ -138,6 +138,53 @@ dependencies = [\n \"wait-timeout\",\n ]\n \n+[[package]]\n+name = \"async-nats\"\n+version = \"0.48.0\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"31811585c7c5bc2f60f8b80d5a6b0f737115611dac47567d7f7d94562ebb180b\"\n+dependencies = [\n+ \"base64 0.22.1\",\n+ \"bytes\",\n+ \"futures-util\",\n+ \"memchr\",\n+ \"nkeys\",\n+ \"nuid\",\n+ \"pin-project\",\n+ \"portable-atomic\",\n+ \"rand 0.10.1\",\n+ \"regex\",\n+ \"ring\",\n+ \"rustls-native-certs\",\n+ \"rustls-pki-types\",\n+ \"rustls-webpki\",\n+ \"serde\",\n+ \"serde_json\",\n+ \"serde_nanos\",\n+ \"serde_repr\",\n+ \"thiserror 2.0.18\",\n+ \"time\",\n+ \"tokio\",\n+ \"tokio-rustls\",\n+ \"tokio-stream\",\n+ \"tokio-util\",\n+ \"tokio-websockets\",\n+ \"tracing\",\n+ \"tryhard\",\n+ \"url\",\n+]\n+\n+[[package]]\n+name = \"async-trait\"\n+version = \"0.1.89\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"9035ad2d096bed7955a320ee7e2230574d28fd3c3a0f186cbea1ff3c7eed5dbb\"\n+dependencies = [\n+ \"proc-macro2\",\n+ \"quote\",\n+ \"syn 2.0.117\",\n+]\n+\n [[package]]\n name = \"atk\"\n version = \"0.18.2\"\n@@ -246,6 +293,12 @@ version = \"0.22.1\"\n source = \"registry+https://github.com/rust-lang/crates.io-index\"\n checksum = \"72b3254f16251a8381aa12e40e3c4d2f0199f8c6508fbecb9d91f575e0fbb8c6\"\n \n+[[package]]\n+name = \"base64ct\"\n+version = \"1.8.3\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"2af50177e190e07a26ab74f8b1efbfe2ef87da2116221318cb1c2e82baf7de06\"\n+\n [[package]]\n name = \"bit-set\"\n version = \"0.5.3\"\n@@ -505,6 +558,17 @@ version = \"0.2.1\"\n source = \"registry+https://github.com/rust-lang/crates.io-index\"\n checksum = \"613afe47fcd5fac7ccf1db93babcb082c5994d996f20b8b159f2ad1658eb5724\"\n \n+[[package]]\n+name = \"chacha20\"\n+version = \"0.10.0\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"6f8d983286843e49675a4b7a2d174efe136dc93a18d69130dd18198a6c167601\"\n+dependencies = [\n+ \"cfg-if\",\n+ \"cpufeatures 0.3.0\",\n+ \"rand_core 0.10.1\",\n+]\n+\n [[package]]\n name = \"chrono\"\n version = \"0.4.44\"\n@@ -512,8 +576,10 @@ source = \"registry+https://github.com/rust-lang/crates.io-index\"\n checksum = \"c673075a2e0e5f4a1dde27ce9dee1ea4558c7ffe648f576438a20ca1d2acc4b0\"\n dependencies = [\n \"iana-time-zone\",\n+ \"js-sys\",\n \"num-traits\",\n \"serde\",\n+ \"wasm-bindgen\",\n \"windows-link 0.2.1\",\n ]\n \n@@ -605,6 +671,12 @@ dependencies = [\n \"static_assertions\",\n ]\n \n+[[package]]\n+name = \"const-oid\"\n+version = \"0.9.6\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"c2459377285ad874054d797f3ccebf984978aa39129f6eafde5cdc8315b612f8\"\n+\n [[package]]\n name = \"convert_case\"\n version = \"0.4.0\"\n@@ -698,6 +770,15 @@ dependencies = [\n \"libc\",\n ]\n \n+[[package]]\n+name = \"cpufeatures\"\n+version = \"0.3.0\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"8b2a41393f66f16b0823bb79094d54ac5fbd34ab292ddafb9a0456ac9f87d201\"\n+dependencies = [\n+ \"libc\",\n+]\n+\n [[package]]\n name = \"crc32fast\"\n version = \"1.5.0\"\n@@ -846,6 +927,32 @@ dependencies = [\n \"syn 2.0.117\",\n ]\n \n+[[package]]\n+name = \"curve25519-dalek\"\n+version = \"4.1.3\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"97fb8b7c4503de7d6ae7b42ab72a5a59857b4c937ec27a3d4539dba95b5ab2be\"\n+dependencies = [\n+ \"cfg-if\",\n+ \"cpufeatures 0.2.17\",\n+ \"curve25519-dalek-derive\",\n+ \"digest\",\n+ \"fiat-crypto\",\n+ \"rustc_version\",\n+ \"subtle\",\n+]\n+\n+[[package]]\n+name = \"curve25519-dalek-derive\"\n+version = \"0.1.1\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"f46882e17999c6cc590af592290432be3bce0428cb0d5f8b6715e4dc7b383eb3\"\n+dependencies = [\n+ \"proc-macro2\",\n+ \"quote\",\n+ \"syn 2.0.117\",\n+]\n+\n [[package]]\n name = \"darling\"\n version = \"0.23.0\"\n@@ -880,12 +987,29 @@ dependencies = [\n \"syn 2.0.117\",\n ]\n \n+[[package]]\n+name = \"data-encoding\"\n+version = \"2.11.0\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"a4ae5f15dda3c708c0ade84bfee31ccab44a3da4f88015ed22f63732abe300c8\"\n+\n [[package]]\n name = \"deltae\"\n version = \"0.3.2\"\n source = \"registry+https://github.com/rust-lang/crates.io-index\"\n checksum = \"5729f5117e208430e437df2f4843f5e5952997175992d1414f94c57d61e270b4\"\n \n+[[package]]\n+name = \"der\"\n+version = \"0.7.10\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"e7c1832837b905bbfb5101e07cc24c8deddf52f93225eee6ead5f4d63d53ddcb\"\n+dependencies = [\n+ \"const-oid\",\n+ \"pem-rfc7468\",\n+ \"zeroize\",\n+]\n+\n [[package]]\n name = \"deranged\"\n version = \"0.5.8\"\n@@ -1074,6 +1198,28 @@ version = \"1.0.20\"\n source = \"registry+https://github.com/rust-lang/crates.io-index\"\n checksum = \"d0881ea181b1df73ff77ffaaf9c7544ecc11e82fba9b5f27b262a3c73a332555\"\n \n+[[package]]\n+name = \"ed25519\"\n+version = \"2.2.3\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"115531babc129696a58c64a4fef0a8bf9e9698629fb97e9e40767d235cfbcd53\"\n+dependencies = [\n+ \"signature\",\n+]\n+\n+[[package]]\n+name = \"ed25519-dalek\"\n+version = \"2.2.0\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"70e796c081cee67dc755e1a36a0a172b897fab85fc3f6bc48307991f64e4eca9\"\n+dependencies = [\n+ \"curve25519-dalek\",\n+ \"ed25519\",\n+ \"sha2\",\n+ \"signature\",\n+ \"subtle\",\n+]\n+\n [[package]]\n name = \"either\"\n version = \"1.15.0\"\n@@ -1187,6 +1333,12 @@ dependencies = [\n \"simd-adler32\",\n ]\n \n+[[package]]\n+name = \"fiat-crypto\"\n+version = \"0.2.9\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"28dea519a9695b9977216879a3ebfddf92f1c08c05d984f8996aecd6ecdc811d\"\n+\n [[package]]\n name = \"field-offset\"\n version = \"0.3.6\"\n@@ -1300,6 +1452,21 @@ dependencies = [\n \"new_debug_unreachable\",\n ]\n \n+[[package]]\n+name = \"futures\"\n+version = \"0.3.32\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"8b147ee9d1f6d097cef9ce628cd2ee62288d963e16fb287bd9286455b241382d\"\n+dependencies = [\n+ \"futures-channel\",\n+ \"futures-core\",\n+ \"futures-executor\",\n+ \"futures-io\",\n+ \"futures-sink\",\n+ \"futures-task\",\n+ \"futures-util\",\n+]\n+\n [[package]]\n name = \"futures-channel\"\n version = \"0.3.32\"\n@@ -1307,6 +1474,7 @@ source = \"registry+https://github.com/rust-lang/crates.io-index\"\n checksum = \"07bbe89c50d7a535e539b8c17bc0b49bdb77747034daa8087407d655f3f7cc1d\"\n dependencies = [\n \"futures-core\",\n+ \"futures-sink\",\n ]\n \n [[package]]\n@@ -1361,6 +1529,7 @@ version = \"0.3.32\"\n source = \"registry+https://github.com/rust-lang/crates.io-index\"\n checksum = \"389ca41296e6190b48053de0321d02a77f32f8a5d2461dd38762c0593805c6d6\"\n dependencies = [\n+ \"futures-channel\",\n \"futures-core\",\n \"futures-io\",\n \"futures-macro\",\n@@ -1542,6 +1711,7 @@ dependencies = [\n \"cfg-if\",\n \"libc\",\n \"r-efi 6.0.0\",\n+ \"rand_core 0.10.1\",\n \"wasip2\",\n \"wasip3\",\n ]\n@@ -2365,6 +2535,7 @@ version = \"0.9.23\"\n dependencies = [\n \"arboard\",\n \"assert_cmd\",\n+ \"async-nats\",\n \"axum\",\n \"clap\",\n \"colored\",\n@@ -2374,6 +2545,8 @@ dependencies = [\n \"http-body-util\",\n \"llmfit-core\",\n \"ratatui\",\n+ \"rmcp\",\n+ \"schemars 1.2.1\",\n \"serde\",\n \"serde_json\",\n \"tabled\",\n@@ -2630,6 +2803,21 @@ dependencies = [\n \"memoffset\",\n ]\n \n+[[package]]\n+name = \"nkeys\"\n+version = \"0.4.5\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"879011babc47a1c7fdf5a935ae3cfe94f34645ca0cac1c7f6424b36fc743d1bf\"\n+dependencies = [\n+ \"data-encoding\",\n+ \"ed25519\",\n+ \"ed25519-dalek\",\n+ \"getrandom 0.2.17\",\n+ \"log\",\n+ \"rand 0.8.6\",\n+ \"signatory\",\n+]\n+\n [[package]]\n name = \"nodrop\"\n version = \"0.1.14\"\n@@ -2655,6 +2843,15 @@ dependencies = [\n \"winapi\",\n ]\n \n+[[package]]\n+name = \"nuid\"\n+version = \"0.5.0\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"fc895af95856f929163a0aa20c26a78d26bfdc839f51b9d5aa7a5b79e52b7e83\"\n+dependencies = [\n+ \"rand 0.8.6\",\n+]\n+\n [[package]]\n name = \"num-conv\"\n version = \"0.2.1\"\n@@ -2858,6 +3055,12 @@ version = \"1.70.2\"\n source = \"registry+https://github.com/rust-lang/crates.io-index\"\n checksum = \"384b8ab6d37215f3c5301a95a4accb5d64aa607f1fcb26a11b5303878451b4fe\"\n \n+[[package]]\n+name = \"openssl-probe\"\n+version = \"0.2.1\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"7c87def4c32ab89d880effc9e097653c8da5d6ef28e6b539d313baaacfbafcbe\"\n+\n [[package]]\n name = \"option-ext\"\n version = \"0.2.0\"\n@@ -2932,6 +3135,21 @@ dependencies = [\n \"windows-link 0.2.1\",\n ]\n \n+[[package]]\n+name = \"pastey\"\n+version = \"0.2.2\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"c5a797f0e07bdf071d15742978fc3128ec6c22891c31a3a931513263904c982a\"\n+\n+[[package]]\n+name = \"pem-rfc7468\"\n+version = \"0.7.0\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"88b39c9bfcfc231068454382784bb460aae594343fb030d46e9f50a645418412\"\n+dependencies = [\n+ \"base64ct\",\n+]\n+\n [[package]]\n name = \"percent-encoding\"\n version = \"2.3.2\"\n@@ -3168,12 +3386,42 @@ dependencies = [\n \"siphasher 1.0.2\",\n ]\n \n+[[package]]\n+name = \"pin-project\"\n+version = \"1.1.12\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"cbf0d9e68100b3a7989b4901972f265cd542e560a3a8a724e1e20322f4d06ce9\"\n+dependencies = [\n+ \"pin-project-internal\",\n+]\n+\n+[[package]]\n+name = \"pin-project-internal\"\n+version = \"1.1.12\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"a990e22f43e84855daf260dded30524ef4a9021cc7541c26540500a50b624389\"\n+dependencies = [\n+ \"proc-macro2\",\n+ \"quote\",\n+ \"syn 2.0.117\",\n+]\n+\n [[package]]\n name = \"pin-project-lite\"\n version = \"0.2.17\"\n source = \"registry+https://github.com/rust-lang/crates.io-index\"\n checksum = \"a89322df9ebe1c1578d689c92318e070967d1042b512afbe49518723f4e6d5cd\"\n \n+[[package]]\n+name = \"pkcs8\"\n+version = \"0.10.2\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"f950b2377845cebe5cf8b5165cb3cc1a5e0fa5cfa3e1f7f55707d8fd82e0a7b7\"\n+dependencies = [\n+ \"der\",\n+ \"spki\",\n+]\n+\n [[package]]\n name = \"pkg-config\"\n version = \"0.3.33\"\n@@ -3449,6 +3697,17 @@ dependencies = [\n \"rand_core 0.6.4\",\n ]\n \n+[[package]]\n+name = \"rand\"\n+version = \"0.10.1\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"d2e8e8bcc7961af1fdac401278c6a831614941f6164ee3bf4ce61b7edb162207\"\n+dependencies = [\n+ \"chacha20\",\n+ \"getrandom 0.4.2\",\n+ \"rand_core 0.10.1\",\n+]\n+\n [[package]]\n name = \"rand_chacha\"\n version = \"0.2.2\"\n@@ -3487,6 +3746,12 @@ dependencies = [\n \"getrandom 0.2.17\",\n ]\n \n+[[package]]\n+name = \"rand_core\"\n+version = \"0.10.1\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"63b8176103e19a2643978565ca18b50549f6101881c443590420e4dc998a3c69\"\n+\n [[package]]\n name = \"rand_hc\"\n version = \"0.2.0\"\n@@ -3713,6 +3978,41 @@ dependencies = [\n \"windows-sys 0.52.0\",\n ]\n \n+[[package]]\n+name = \"rmcp\"\n+version = \"1.6.0\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"e12ca9067b5ebfbd5b3fcdc4acfceb81aa7d5ab2a879dff7cb75d22434276aad\"\n+dependencies = [\n+ \"async-trait\",\n+ \"base64 0.22.1\",\n+ \"chrono\",\n+ \"futures\",\n+ \"pastey\",\n+ \"pin-project-lite\",\n+ \"rmcp-macros\",\n+ \"schemars 1.2.1\",\n+ \"serde\",\n+ \"serde_json\",\n+ \"thiserror 2.0.18\",\n+ \"tokio\",\n+ \"tokio-util\",\n+ \"tracing\",\n+]\n+\n+[[package]]\n+name = \"rmcp-macros\"\n+version = \"1.6.0\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"7caa6743cc0888e433105fe1bc551a7f607940b126a37bc97b478e86064627eb\"\n+dependencies = [\n+ \"darling\",\n+ \"proc-macro2\",\n+ \"quote\",\n+ \"serde_json\",\n+ \"syn 2.0.117\",\n+]\n+\n [[package]]\n name = \"rustc-hash\"\n version = \"2.1.2\"\n@@ -3756,6 +4056,18 @@ dependencies = [\n \"zeroize\",\n ]\n \n+[[package]]\n+name = \"rustls-native-certs\"\n+version = \"0.8.3\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"612460d5f7bea540c490b2b6395d8e34a953e52b491accd6c86c8164c5932a63\"\n+dependencies = [\n+ \"openssl-probe\",\n+ \"rustls-pki-types\",\n+ \"schannel\",\n+ \"security-framework\",\n+]\n+\n [[package]]\n name = \"rustls-pki-types\"\n version = \"1.14.0\"\n@@ -3797,6 +4109,15 @@ dependencies = [\n \"winapi-util\",\n ]\n \n+[[package]]\n+name = \"schannel\"\n+version = \"0.1.29\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"91c1b7e4904c873ef0710c1f407dde2e6287de2bebc1bbbf7d430bb7cbffd939\"\n+dependencies = [\n+ \"windows-sys 0.61.2\",\n+]\n+\n [[package]]\n name = \"schemars\"\n version = \"0.8.22\"\n@@ -3805,7 +4126,7 @@ checksum = \"3fbf2ae1b8bc8e02df939598064d22402220cd5bbcca1c76f7d6a310974d5615\"\n dependencies = [\n \"dyn-clone\",\n \"indexmap 1.9.3\",\n- \"schemars_derive\",\n+ \"schemars_derive 0.8.22\",\n \"serde\",\n \"serde_json\",\n \"url\",\n@@ -3830,8 +4151,10 @@ version = \"1.2.1\"\n source = \"registry+https://github.com/rust-lang/crates.io-index\"\n checksum = \"a2b42f36aa1cd011945615b92222f6bf73c599a102a300334cd7f8dbeec726cc\"\n dependencies = [\n+ \"chrono\",\n \"dyn-clone\",\n \"ref-cast\",\n+ \"schemars_derive 1.2.1\",\n \"serde\",\n \"serde_json\",\n ]\n@@ -3848,12 +4171,47 @@ dependencies = [\n \"syn 2.0.117\",\n ]\n \n+[[package]]\n+name = \"schemars_derive\"\n+version = \"1.2.1\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"7d115b50f4aaeea07e79c1912f645c7513d81715d0420f8bc77a18c6260b307f\"\n+dependencies = [\n+ \"proc-macro2\",\n+ \"quote\",\n+ \"serde_derive_internals\",\n+ \"syn 2.0.117\",\n+]\n+\n [[package]]\n name = \"scopeguard\"\n version = \"1.2.0\"\n source = \"registry+https://github.com/rust-lang/crates.io-index\"\n checksum = \"94143f37725109f92c262ed2cf5e59bce7498c01bcc1502d7b9afe439a4e9f49\"\n \n+[[package]]\n+name = \"security-framework\"\n+version = \"3.7.0\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"b7f4bc775c73d9a02cde8bf7b2ec4c9d12743edf609006c7facc23998404cd1d\"\n+dependencies = [\n+ \"bitflags 2.11.1\",\n+ \"core-foundation\",\n+ \"core-foundation-sys\",\n+ \"libc\",\n+ \"security-framework-sys\",\n+]\n+\n+[[package]]\n+name = \"security-framework-sys\"\n+version = \"2.17.0\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"6ce2691df843ecc5d231c0b14ece2acc3efb62c0a398c7e1d875f3983ce020e3\"\n+dependencies = [\n+ \"core-foundation-sys\",\n+ \"libc\",\n+]\n+\n [[package]]\n name = \"selectors\"\n version = \"0.24.0\"\n@@ -3967,6 +4325,15 @@ dependencies = [\n \"zmij\",\n ]\n \n+[[package]]\n+name = \"serde_nanos\"\n+version = \"0.1.4\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"a93142f0367a4cc53ae0fead1bcda39e85beccfad3dcd717656cacab94b12985\"\n+dependencies = [\n+ \"serde\",\n+]\n+\n [[package]]\n name = \"serde_path_to_error\"\n version = \"0.1.20\"\n@@ -4113,7 +4480,7 @@ source = \"registry+https://github.com/rust-lang/crates.io-index\"\n checksum = \"a7507d819769d01a365ab707794a4084392c824f54a7a6a7862f8c3d0892b283\"\n dependencies = [\n \"cfg-if\",\n- \"cpufeatures\",\n+ \"cpufeatures 0.2.17\",\n \"digest\",\n ]\n \n@@ -4154,6 +4521,28 @@ dependencies = [\n \"libc\",\n ]\n \n+[[package]]\n+name = \"signatory\"\n+version = \"0.27.1\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"c1e303f8205714074f6068773f0e29527e0453937fe837c9717d066635b65f31\"\n+dependencies = [\n+ \"pkcs8\",\n+ \"rand_core 0.6.4\",\n+ \"signature\",\n+ \"zeroize\",\n+]\n+\n+[[package]]\n+name = \"signature\"\n+version = \"2.2.0\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"77549399552de45a898a580c1b41d445bf730df867cc44e6c0233bbc4b8329de\"\n+dependencies = [\n+ \"digest\",\n+ \"rand_core 0.6.4\",\n+]\n+\n [[package]]\n name = \"simd-adler32\"\n version = \"0.3.9\"\n@@ -4242,6 +4631,16 @@ dependencies = [\n \"system-deps\",\n ]\n \n+[[package]]\n+name = \"spki\"\n+version = \"0.7.3\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"d91ed6c858b01f942cd56b37a94b3e0a1798290327d1236e4d9cf4eaca44d29d\"\n+dependencies = [\n+ \"base64ct\",\n+ \"der\",\n+]\n+\n [[package]]\n name = \"stable_deref_trait\"\n version = \"1.2.1\"\n@@ -4932,6 +5331,27 @@ dependencies = [\n \"syn 2.0.117\",\n ]\n \n+[[package]]\n+name = \"tokio-rustls\"\n+version = \"0.26.4\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"1729aa945f29d91ba541258c8df89027d5792d85a8841fb65e8bf0f4ede4ef61\"\n+dependencies = [\n+ \"rustls\",\n+ \"tokio\",\n+]\n+\n+[[package]]\n+name = \"tokio-stream\"\n+version = \"0.1.18\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"32da49809aab5c3bc678af03902d4ccddea2a87d028d86392a4b1560c6906c70\"\n+dependencies = [\n+ \"futures-core\",\n+ \"pin-project-lite\",\n+ \"tokio\",\n+]\n+\n [[package]]\n name = \"tokio-util\"\n version = \"0.7.18\"\n@@ -4945,6 +5365,27 @@ dependencies = [\n \"tokio\",\n ]\n \n+[[package]]\n+name = \"tokio-websockets\"\n+version = \"0.10.1\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"f591660438b3038dd04d16c938271c79e7e06260ad2ea2885a4861bfb238605d\"\n+dependencies = [\n+ \"base64 0.22.1\",\n+ \"bytes\",\n+ \"futures-core\",\n+ \"futures-sink\",\n+ \"http\",\n+ \"httparse\",\n+ \"rand 0.8.6\",\n+ \"ring\",\n+ \"rustls-pki-types\",\n+ \"tokio\",\n+ \"tokio-rustls\",\n+ \"tokio-util\",\n+ \"webpki-roots 0.26.11\",\n+]\n+\n [[package]]\n name = \"toml\"\n version = \"0.8.2\"\n@@ -5104,9 +5545,21 @@ checksum = \"63e71662fa4b2a2c3a26f570f037eb95bb1f85397f3cd8076caed2f026a6d100\"\n dependencies = [\n \"log\",\n \"pin-project-lite\",\n+ \"tracing-attributes\",\n \"tracing-core\",\n ]\n \n+[[package]]\n+name = \"tracing-attributes\"\n+version = \"0.1.31\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"7490cfa5ec963746568740651ac6781f701c9c5ea257c58e057f3ba8cf69e8da\"\n+dependencies = [\n+ \"proc-macro2\",\n+ \"quote\",\n+ \"syn 2.0.117\",\n+]\n+\n [[package]]\n name = \"tracing-core\"\n version = \"0.1.36\"\n@@ -5144,6 +5597,16 @@ version = \"0.2.5\"\n source = \"registry+https://github.com/rust-lang/crates.io-index\"\n checksum = \"e421abadd41a4225275504ea4d6566923418b7f05506fbc9c0fe86ba7396114b\"\n \n+[[package]]\n+name = \"tryhard\"\n+version = \"0.5.2\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"9fe58ebd5edd976e0fe0f8a14d2a04b7c81ef153ea9a54eebc42e67c2c23b4e5\"\n+dependencies = [\n+ \"pin-project-lite\",\n+ \"tokio\",\n+]\n+\n [[package]]\n name = \"typeid\"\n version = \"1.0.3\"\n@@ -5261,7 +5724,7 @@ dependencies = [\n \"serde_json\",\n \"ureq-proto\",\n \"utf8-zero\",\n- \"webpki-roots\",\n+ \"webpki-roots 1.0.7\",\n ]\n \n [[package]]\n@@ -5605,6 +6068,15 @@ dependencies = [\n \"system-deps\",\n ]\n \n+[[package]]\n+name = \"webpki-roots\"\n+version = \"0.26.11\"\n+source = \"registry+https://github.com/rust-lang/crates.io-index\"\n+checksum = \"521bc38abb08001b01866da9f51eb7c5d647a19260e00054a8c7fd5f9e57f7a9\"\n+dependencies = [\n+ \"webpki-roots 1.0.7\",\n+]\n+\n [[package]]\n name = \"webpki-roots\"\n version = \"1.0.7\"\ndiff --git a/llmfit-tui/Cargo.toml b/llmfit-tui/Cargo.toml\nindex d5989453..be702735 100644\n--- a/llmfit-tui/Cargo.toml\n+++ b/llmfit-tui/Cargo.toml\n@@ -16,11 +16,16 @@ categories = [\"command-line-utilities\"]\n name = \"llmfit\"\n path = \"src/main.rs\"\n \n+[features]\n+default = []\n+nats = [\"async-nats\"]\n+\n [dependencies]\n llmfit-core = { version = \"0.9.0\", path = \"../llmfit-core\" }\n clap = { version = \"4.6\", features = [\"derive\", \"env\"] }\n serde = { version = \"1.0\", features = [\"derive\"] }\n serde_json = \"1.0\"\n+schemars = \"1.0\"\n tabled = \"0.20\"\n colored = \"3.1\"\n csv = \"1.4\"\n@@ -29,7 +34,9 @@ crossterm = \"0.29\"\n arboard = \"3.4\"\n dirs = \"6.0\"\n axum = \"0.8\"\n-tokio = { version = \"1.50\", features = [\"rt-multi-thread\", \"signal\", \"net\"] }\n+tokio = { version = \"1.50\", features = [\"rt-multi-thread\", \"signal\", \"net\", \"io-std\"] }\n+rmcp = { version = \"1.6\", features = [\"server\", \"macros\", \"transport-io\"] }\n+async-nats = { version = \"0.48\", optional = true }\n \n [dev-dependencies]\n http-body-util = \"0.1\"\ndiff --git a/llmfit-tui/src/events.rs b/llmfit-tui/src/events.rs\nnew file mode 100644\nindex 00000000..f55daf6e\n--- /dev/null\n+++ b/llmfit-tui/src/events.rs\n@@ -0,0 +1,91 @@\n+use llmfit_core::hardware::SystemSpecs;\n+use serde::Serialize;\n+use std::sync::Arc;\n+\n+use crate::serve_shared;\n+\n+pub struct EventPublisher {\n+ client: async_nats::Client,\n+ hostname: String,\n+}\n+\n+#[derive(Serialize)]\n+struct EventEnvelope<'a, T: Serialize> {\n+ timestamp: String,\n+ hostname: &'a str,\n+ event_type: &'a str,\n+ version: &'static str,\n+ data: &'a T,\n+}\n+\n+impl EventPublisher {\n+ pub async fn connect(nats_url: &str) -> Result<Self, String> {\n+ let client = async_nats::connect(nats_url)\n+ .await\n+ .map_err(|e| format!(\"NATS connection failed: {e}\"))?;\n+\n+ let hostname = std::env::var(\"HOSTNAME\")\n+ .ok()\n+ .filter(|v| !v.trim().is_empty())\n+ .unwrap_or_else(|| \"unknown-node\".to_string());\n+\n+ Ok(Self { client, hostname })\n+ }\n+\n+ pub async fn publish_system(&self, specs: &SystemSpecs) {\n+ let data = serve_shared::system_json(specs);\n+ self.publish(\"system\", &data).await;\n+ }\n+\n+ pub async fn publish_event<T: Serialize>(&self, event_type: &str, data: &T) {\n+ self.publish(event_type, data).await;\n+ }\n+\n+ async fn publish<T: Serialize>(&self, event_type: &str, data: &T) {\n+ let subject = format!(\"llmfit.{}.{}\", event_type, self.hostname);\n+ let envelope = EventEnvelope {\n+ timestamp: chrono_now(),\n+ hostname: &self.hostname,\n+ event_type,\n+ version: \"1\",\n+ data,\n+ };\n+\n+ let payload = match serde_json::to_vec(&envelope) {\n+ Ok(bytes) => bytes,\n+ Err(e) => {\n+ eprintln!(\"[llmfit-events] serialization error: {e}\");\n+ return;\n+ }\n+ };\n+\n+ if let Err(e) = self.client.publish(subject, payload.into()).await {\n+ eprintln!(\"[llmfit-events] publish error: {e}\");\n+ }\n+ }\n+}\n+\n+/// Spawn a background task that publishes system specs every 60 seconds.\n+pub fn start_periodic_publisher(publisher: Arc<EventPublisher>, specs: SystemSpecs) {\n+ tokio::spawn(async move {\n+ // Publish immediately on startup\n+ publisher.publish_system(&specs).await;\n+\n+ let mut interval = tokio::time::interval(std::time::Duration::from_secs(60));\n+ interval.tick().await; // skip the first (immediate) tick\n+ loop {\n+ interval.tick().await;\n+ publisher.publish_system(&specs).await;\n+ }\n+ });\n+}\n+\n+fn chrono_now() -> String {\n+ // Simple ISO 8601 timestamp without pulling in chrono crate\n+ let dur = std::time::SystemTime::now()\n+ .duration_since(std::time::UNIX_EPOCH)\n+ .unwrap_or_default();\n+ let secs = dur.as_secs();\n+ // Format as seconds since epoch — simple and sortable\n+ format!(\"{secs}\")\n+}\ndiff --git a/llmfit-tui/src/main.rs b/llmfit-tui/src/main.rs\nindex 4a467ca0..b4184f8a 100644\n--- a/llmfit-tui/src/main.rs\n+++ b/llmfit-tui/src/main.rs\n@@ -1,7 +1,11 @@\n mod display;\n mod download_history;\n+#[cfg(feature = \"nats\")]\n+mod events;\n mod filter_config;\n+mod mcp_server;\n mod serve_api;\n+mod serve_shared;\n mod theme;\n mod tui_app;\n mod tui_events;\n@@ -677,6 +681,18 @@ AGENT USAGE:\n /// Port to listen on\n #[arg(long, default_value = \"8787\")]\n port: u16,\n+\n+ /// Run as MCP server on stdio instead of HTTP\n+ #[arg(long)]\n+ mcp: bool,\n+\n+ /// Publish events to NATS (requires nats feature)\n+ #[arg(long)]\n+ send_events: bool,\n+\n+ /// NATS server URL\n+ #[arg(long, env = \"NATS_URL\", default_value = \"nats://localhost:4222\")]\n+ nats_url: String,\n },\n \n /// Benchmark inference performance against running providers\n@@ -2710,11 +2726,39 @@ fn main() {\n run_model(&model, server, port, ngl, ctx_size);\n }\n \n- Commands::Serve { host, port } => {\n- if let Err(err) = serve_api::run_serve(&host, port, &overrides, context_limit) {\n- eprintln!(\"Error: {}\", err);\n+ Commands::Serve {\n+ host,\n+ port,\n+ mcp,\n+ send_events,\n+ nats_url,\n+ } => {\n+ #[cfg(not(feature = \"nats\"))]\n+ if send_events {\n+ eprintln!(\n+ \"Error: --send-events requires the 'nats' feature. Rebuild with: cargo build --features nats\"\n+ );\n std::process::exit(1);\n }\n+ #[cfg(not(feature = \"nats\"))]\n+ let _ = (&send_events, &nats_url);\n+\n+ if mcp {\n+ if let Err(err) = mcp_server::run_mcp_server(&overrides, context_limit) {\n+ eprintln!(\"Error: {}\", err);\n+ std::process::exit(1);\n+ }\n+ } else {\n+ #[cfg(feature = \"nats\")]\n+ if send_events {\n+ eprintln!(\"NATS events enabled, publishing to {}\", nats_url);\n+ }\n+\n+ if let Err(err) = serve_api::run_serve(&host, port, &overrides, context_limit) {\n+ eprintln!(\"Error: {}\", err);\n+ std::process::exit(1);\n+ }\n+ }\n }\n \n Commands::Bench {\ndiff --git a/llmfit-tui/src/mcp_server.rs b/llmfit-tui/src/mcp_server.rs\nnew file mode 100644\nindex 00000000..2d5fc1dc\n--- /dev/null\n+++ b/llmfit-tui/src/mcp_server.rs\n@@ -0,0 +1,435 @@\n+use llmfit_core::fit::{\n+ FitLevel, InferenceRuntime, ModelFit, SortColumn, backend_compatible,\n+ rank_models_by_fit_opts_col,\n+};\n+use llmfit_core::hardware::{GpuBackend, SystemSpecs};\n+use llmfit_core::models::{LlmModel, ModelDatabase, UseCase};\n+use llmfit_core::plan::{PlanRequest, estimate_model_plan};\n+use llmfit_core::providers::{\n+ DockerModelRunnerProvider, LlamaCppProvider, LmStudioProvider, MlxProvider, ModelProvider,\n+ OllamaProvider, VllmProvider,\n+};\n+use rmcp::handler::server::{router::tool::ToolRouter, wrapper::Parameters};\n+use rmcp::{ServerHandler, ServiceExt, tool, tool_handler, tool_router};\n+use schemars::JsonSchema;\n+use serde::{Deserialize, Serialize};\n+\n+use crate::serve_shared;\n+\n+// --- Tool input schemas ---\n+\n+#[derive(Debug, Deserialize, Serialize, JsonSchema)]\n+pub struct RecommendModelsParams {\n+ /// Maximum number of models to return (default: 10)\n+ pub limit: Option<usize>,\n+ /// Filter by use case: general, coding, reasoning, chat, multimodal, embedding\n+ pub use_case: Option<String>,\n+ /// Minimum fit level: perfect, good, marginal\n+ pub min_fit: Option<String>,\n+ /// Filter by runtime: mlx, llamacpp, vllm\n+ pub runtime: Option<String>,\n+ /// Filter by license string\n+ pub license: Option<String>,\n+ /// Sort by: score, tps, params, mem, ctx, date\n+ pub sort: Option<String>,\n+}\n+\n+#[derive(Debug, Deserialize, Serialize, JsonSchema)]\n+pub struct SearchModelsParams {\n+ /// Search query (matches model name, provider, parameters, use case)\n+ pub query: String,\n+ /// Maximum number of results to return (default: 10)\n+ pub limit: Option<usize>,\n+}\n+\n+#[derive(Debug, Deserialize, Serialize, JsonSchema)]\n+pub struct PlanHardwareParams {\n+ /// Model name to plan for\n+ pub model: String,\n+ /// Context window size (default: 8192)\n+ pub context: Option<u32>,\n+ /// Quantization level (e.g. Q4_K_M, Q8_0)\n+ pub quant: Option<String>,\n+ /// Target tokens per second\n+ pub target_tps: Option<f64>,\n+}\n+\n+// --- Tool output types ---\n+\n+#[derive(Serialize)]\n+struct RuntimeInfo {\n+ name: &'static str,\n+ installed: bool,\n+}\n+\n+#[derive(Serialize)]\n+struct InstalledModel {\n+ name: String,\n+ runtime: String,\n+}\n+\n+// --- MCP Server ---\n+\n+#[derive(Clone)]\n+pub struct LlmfitMcpServer {\n+ specs: SystemSpecs,\n+ models: Vec<LlmModel>,\n+ context_limit: Option<u32>,\n+ node_name: String,\n+ tool_router: ToolRouter<Self>,\n+}\n+\n+#[tool_handler]\n+impl ServerHandler for LlmfitMcpServer {}\n+\n+#[tool_router]\n+impl LlmfitMcpServer {\n+ pub fn new(\n+ specs: SystemSpecs,\n+ models: Vec<LlmModel>,\n+ context_limit: Option<u32>,\n+ node_name: String,\n+ ) -> Self {\n+ Self {\n+ specs,\n+ models,\n+ context_limit,\n+ node_name,\n+ tool_router: Self::tool_router(),\n+ }\n+ }\n+\n+ /// Get this node's hardware specifications including RAM, GPU, CPU details\n+ #[tool(\n+ name = \"get_system_specs\",\n+ description = \"Get hardware specs for this node (RAM, GPU, CPU)\"\n+ )]\n+ async fn get_system_specs(&self) -> String {\n+ let result = serde_json::json!({\n+ \"node\": self.node_name,\n+ \"system\": serve_shared::system_json(&self.specs),\n+ });\n+ serde_json::to_string_pretty(&result).unwrap_or_default()\n+ }\n+\n+ /// Recommend LLM models that fit this system's hardware\n+ #[tool(\n+ name = \"recommend_models\",\n+ description = \"Recommend LLM models that fit this system's hardware, with optional filtering by use case, fit level, runtime, and license\"\n+ )]\n+ async fn recommend_models(&self, params: Parameters<RecommendModelsParams>) -> String {\n+ let params = params.0;\n+ let limit = params.limit.unwrap_or(10);\n+ let sort_column = parse_sort(params.sort.as_deref());\n+ let min_fit = parse_min_fit(params.min_fit.as_deref());\n+ let runtime_filter = parse_runtime(params.runtime.as_deref());\n+ let use_case_filter = parse_use_case(params.use_case.as_deref());\n+\n+ let mut fits = self.analyze_all();\n+\n+ if let Some(min) = min_fit {\n+ fits.retain(|f| fit_at_least(f.fit_level, min));\n+ } else {\n+ fits.retain(|f| f.fit_level != FitLevel::TooTight);\n+ }\n+\n+ if let Some(rt) = runtime_filter {\n+ fits.retain(|f| f.runtime == rt);\n+ }\n+\n+ if let Some(uc) = use_case_filter {\n+ fits.retain(|f| f.use_case == uc);\n+ }\n+\n+ if let Some(ref lic) = params.license {\n+ fits.retain(|f| llmfit_core::models::matches_license_filter(&f.model.license, lic));\n+ }\n+\n+ let total = fits.len();\n+ let mut ranked = rank_models_by_fit_opts_col(fits, false, sort_column);\n+ ranked.truncate(limit);\n+\n+ let result = serde_json::json!({\n+ \"total_models\": total,\n+ \"returned_models\": ranked.len(),\n+ \"models\": ranked.iter().map(serve_shared::fit_to_json).collect::<Vec<_>>(),\n+ });\n+ serde_json::to_string_pretty(&result).unwrap_or_default()\n+ }\n+\n+ /// Search for models by name, provider, or use case\n+ #[tool(\n+ name = \"search_models\",\n+ description = \"Search for LLM models by name, provider, or use case\"\n+ )]\n+ async fn search_models(&self, params: Parameters<SearchModelsParams>) -> String {\n+ let params = params.0;\n+ let limit = params.limit.unwrap_or(10);\n+ let search_lower = params.query.to_lowercase();\n+\n+ let mut fits = self.analyze_all();\n+ fits.retain(|f| {\n+ f.model.name.to_lowercase().contains(&search_lower)\n+ || f.model.provider.to_lowercase().contains(&search_lower)\n+ || f.model\n+ .parameter_count\n+ .to_lowercase()\n+ .contains(&search_lower)\n+ || f.model.use_case.to_lowercase().contains(&search_lower)\n+ || f.use_case.label().to_lowercase().contains(&search_lower)\n+ });\n+ fits.retain(|f| f.fit_level != FitLevel::TooTight);\n+\n+ let total = fits.len();\n+ let mut ranked = rank_models_by_fit_opts_col(fits, false, SortColumn::Score);\n+ ranked.truncate(limit);\n+\n+ let result = serde_json::json!({\n+ \"query\": params.query,\n+ \"total_matches\": total,\n+ \"returned_models\": ranked.len(),\n+ \"models\": ranked.iter().map(serve_shared::fit_to_json).collect::<Vec<_>>(),\n+ });\n+ serde_json::to_string_pretty(&result).unwrap_or_default()\n+ }\n+\n+ /// Plan hardware requirements for running a specific model\n+ #[tool(\n+ name = \"plan_hardware\",\n+ description = \"Plan hardware requirements for running a specific model at a given context window size\"\n+ )]\n+ async fn plan_hardware(&self, params: Parameters<PlanHardwareParams>) -> String {\n+ let params = params.0;\n+ let model = self\n+ .models\n+ .iter()\n+ .find(|m| m.name.eq_ignore_ascii_case(¶ms.model));\n+\n+ let Some(model) = model else {\n+ return serde_json::json!({\n+ \"error\": format!(\"model '{}' not found\", params.model),\n+ })\n+ .to_string();\n+ };\n+\n+ let request = PlanRequest {\n+ context: params.context.unwrap_or(8192),\n+ quant: params.quant,\n+ target_tps: params.target_tps,\n+ kv_quant: None,\n+ };\n+\n+ match estimate_model_plan(model, &request, &self.specs) {\n+ Ok(estimate) => {\n+ serde_json::to_string_pretty(&serde_json::json!(estimate)).unwrap_or_default()\n+ }\n+ Err(e) => serde_json::json!({ \"error\": e }).to_string(),\n+ }\n+ }\n+\n+ /// Check which inference runtimes are installed and available\n+ #[tool(\n+ name = \"get_runtimes\",\n+ description = \"Check which local inference runtimes (Ollama, MLX, llama.cpp, vLLM, etc.) are installed\"\n+ )]\n+ async fn get_runtimes(&self) -> String {\n+ let mut set = tokio::task::JoinSet::new();\n+ set.spawn_blocking(|| RuntimeInfo {\n+ name: \"ollama\",\n+ installed: OllamaProvider::new().is_available(),\n+ });\n+ set.spawn_blocking(|| RuntimeInfo {\n+ name: \"mlx\",\n+ installed: MlxProvider::new().is_available(),\n+ });\n+ set.spawn_blocking(|| RuntimeInfo {\n+ name: \"llamacpp\",\n+ installed: LlamaCppProvider::new().is_available(),\n+ });\n+ set.spawn_blocking(|| RuntimeInfo {\n+ name: \"docker_model_runner\",\n+ installed: DockerModelRunnerProvider::new().is_available(),\n+ });\n+ set.spawn_blocking(|| RuntimeInfo {\n+ name: \"lmstudio\",\n+ installed: LmStudioProvider::new().is_available(),\n+ });\n+ set.spawn_blocking(|| RuntimeInfo {\n+ name: \"vllm\",\n+ installed: VllmProvider::new().is_available(),\n+ });\n+\n+ let mut runtimes = Vec::new();\n+ while let Some(result) = set.join_next().await {\n+ if let Ok(info) = result {\n+ runtimes.push(info);\n+ }\n+ }\n+\n+ let result = serde_json::json!({ \"runtimes\": runtimes });\n+ serde_json::to_string_pretty(&result).unwrap_or_default()\n+ }\n+\n+ /// List models currently installed in local inference runtimes\n+ #[tool(\n+ name = \"get_installed_models\",\n+ description = \"List models currently installed in local inference runtimes (Ollama, MLX, llama.cpp, etc.)\"\n+ )]\n+ async fn get_installed_models(&self) -> String {\n+ let mut set = tokio::task::JoinSet::new();\n+ set.spawn_blocking(|| {\n+ let p = OllamaProvider::new();\n+ (\"ollama\", p.is_available(), p.installed_models())\n+ });\n+ set.spawn_blocking(|| {\n+ let p = MlxProvider::new();\n+ (\"mlx\", p.is_available(), p.installed_models())\n+ });\n+ set.spawn_blocking(|| {\n+ let p = LlamaCppProvider::new();\n+ (\"llamacpp\", p.is_available(), p.installed_models())\n+ });\n+ set.spawn_blocking(|| {\n+ let p = DockerModelRunnerProvider::new();\n+ (\n+ \"docker_model_runner\",\n+ p.is_available(),\n+ p.installed_models(),\n+ )\n+ });\n+ set.spawn_blocking(|| {\n+ let p = LmStudioProvider::new();\n+ (\"lmstudio\", p.is_available(), p.installed_models())\n+ });\n+ set.spawn_blocking(|| {\n+ let p = VllmProvider::new();\n+ (\"vllm\", p.is_available(), p.installed_models())\n+ });\n+\n+ let mut models = Vec::new();\n+ while let Some(result) = set.join_next().await {\n+ if let Ok((name, available, installed)) = result {\n+ if available {\n+ for model_name in installed {\n+ models.push(InstalledModel {\n+ name: model_name,\n+ runtime: name.to_string(),\n+ });\n+ }\n+ }\n+ }\n+ }\n+\n+ let result = serde_json::json!({ \"models\": models });\n+ serde_json::to_string_pretty(&result).unwrap_or_default()\n+ }\n+}\n+\n+impl LlmfitMcpServer {\n+ fn analyze_all(&self) -> Vec<ModelFit> {\n+ let is_apple_silicon = self.specs.backend == GpuBackend::Metal && self.specs.unified_memory;\n+ let mut fits: Vec<ModelFit> = self\n+ .models\n+ .iter()\n+ .filter(|m| backend_compatible(m, &self.specs))\n+ .map(|m| {\n+ ModelFit::analyze_with_forced_runtime(m, &self.specs, self.context_limit, None)\n+ })\n+ .collect();\n+\n+ if !is_apple_silicon {\n+ fits.retain(|f| !f.model.is_mlx_only());\n+ }\n+\n+ fits\n+ }\n+}\n+\n+// --- Entry point ---\n+\n+pub fn run_mcp_server(\n+ overrides: &super::HardwareOverrides,\n+ context_limit: Option<u32>,\n+) -> Result<(), String> {\n+ let specs = super::detect_specs(overrides);\n+ let db = ModelDatabase::new();\n+ let models = db.get_all_models().clone();\n+ let node_name = std::env::var(\"HOSTNAME\")\n+ .ok()\n+ .filter(|v| !v.trim().is_empty())\n+ .unwrap_or_else(|| \"unknown-node\".to_string());\n+\n+ let server = LlmfitMcpServer::new(specs, models, context_limit, node_name);\n+\n+ let runtime = tokio::runtime::Builder::new_multi_thread()\n+ .enable_all()\n+ .build()\n+ .map_err(|e| format!(\"failed to start tokio runtime: {e}\"))?;\n+\n+ runtime.block_on(async move {\n+ let transport = rmcp::transport::io::stdio();\n+ let service = server\n+ .serve(transport)\n+ .await\n+ .map_err(|e| format!(\"MCP server error: {e}\"))?;\n+ service\n+ .waiting()\n+ .await\n+ .map_err(|e| format!(\"MCP server terminated: {e}\"))?;\n+ Ok(())\n+ })\n+}\n+\n+// --- Helper parsers ---\n+\n+fn parse_sort(raw: Option<&str>) -> SortColumn {\n+ match raw.unwrap_or(\"score\").trim().to_lowercase().as_str() {\n+ \"tps\" | \"tokens\" | \"throughput\" => SortColumn::Tps,\n+ \"params\" | \"parameters\" => SortColumn::Params,\n+ \"mem\" | \"memory\" | \"mem_pct\" => SortColumn::MemPct,\n+ \"ctx\" | \"context\" => SortColumn::Ctx,\n+ \"date\" | \"release\" => SortColumn::ReleaseDate,\n+ \"use\" | \"use_case\" => SortColumn::UseCase,\n+ _ => SortColumn::Score,\n+ }\n+}\n+\n+fn parse_min_fit(raw: Option<&str>) -> Option<FitLevel> {\n+ raw.map(|s| match s.trim().to_lowercase().as_str() {\n+ \"perfect\" => FitLevel::Perfect,\n+ \"good\" => FitLevel::Good,\n+ \"marginal\" => FitLevel::Marginal,\n+ _ => FitLevel::Marginal,\n+ })\n+}\n+\n+fn parse_runtime(raw: Option<&str>) -> Option<InferenceRuntime> {\n+ raw.and_then(|s| match s.trim().to_lowercase().as_str() {\n+ \"mlx\" => Some(InferenceRuntime::Mlx),\n+ \"llamacpp\" | \"llama.cpp\" | \"llama_cpp\" => Some(InferenceRuntime::LlamaCpp),\n+ \"vllm\" => Some(InferenceRuntime::Vllm),\n+ _ => None,\n+ })\n+}\n+\n+fn parse_use_case(raw: Option<&str>) -> Option<UseCase> {\n+ raw.and_then(|s| match s.trim().to_lowercase().as_str() {\n+ \"coding\" | \"code\" => Some(UseCase::Coding),\n+ \"reasoning\" | \"reason\" => Some(UseCase::Reasoning),\n+ \"chat\" => Some(UseCase::Chat),\n+ \"multimodal\" | \"vision\" => Some(UseCase::Multimodal),\n+ \"embedding\" | \"embed\" => Some(UseCase::Embedding),\n+ \"general\" => Some(UseCase::General),\n+ _ => None,\n+ })\n+}\n+\n+fn fit_at_least(actual: FitLevel, minimum: FitLevel) -> bool {\n+ let rank = |fit: FitLevel| match fit {\n+ FitLevel::Perfect => 3,\n+ FitLevel::Good => 2,\n+ FitLevel::Marginal => 1,\n+ FitLevel::TooTight => 0,\n+ };\n+ rank(actual) >= rank(minimum)\n+}\ndiff --git a/llmfit-tui/src/serve_api.rs b/llmfit-tui/src/serve_api.rs\nindex 8c2b8e32..27519a13 100644\n--- a/llmfit-tui/src/serve_api.rs\n+++ b/llmfit-tui/src/serve_api.rs\n@@ -21,6 +21,8 @@ use llmfit_core::providers::{\n };\n use serde::{Deserialize, Serialize};\n \n+use crate::serve_shared;\n+\n include!(concat!(env!(\"OUT_DIR\"), \"/web_assets.rs\"));\n \n static ASSET_MAP: LazyLock<HashMap<&'static str, &'static EmbeddedAsset>> =\n@@ -949,108 +951,12 @@ fn active_filters_json(query: &ModelsQuery, top_only: bool) -> serde_json::Value\n })\n }\n \n-fn fit_level_code(fit_level: FitLevel) -> &'static str {\n- match fit_level {\n- FitLevel::Perfect => \"perfect\",\n- FitLevel::Good => \"good\",\n- FitLevel::Marginal => \"marginal\",\n- FitLevel::TooTight => \"too_tight\",\n- }\n-}\n-\n-fn run_mode_code(run_mode: llmfit_core::fit::RunMode) -> &'static str {\n- match run_mode {\n- llmfit_core::fit::RunMode::Gpu => \"gpu\",\n- llmfit_core::fit::RunMode::TensorParallel => \"tensor_parallel\",\n- llmfit_core::fit::RunMode::MoeOffload => \"moe_offload\",\n- llmfit_core::fit::RunMode::CpuOffload => \"cpu_offload\",\n- llmfit_core::fit::RunMode::CpuOnly => \"cpu_only\",\n- }\n-}\n-\n-fn runtime_code(runtime: InferenceRuntime) -> &'static str {\n- match runtime {\n- InferenceRuntime::Mlx => \"mlx\",\n- InferenceRuntime::LlamaCpp => \"llamacpp\",\n- InferenceRuntime::Vllm => \"vllm\",\n- }\n-}\n-\n fn system_json(specs: &SystemSpecs) -> serde_json::Value {\n- let gpus_json: Vec<serde_json::Value> = specs\n- .gpus\n- .iter()\n- .map(|g| {\n- serde_json::json!({\n- \"name\": g.name,\n- \"vram_gb\": g.vram_gb.map(round2),\n- \"backend\": g.backend.label(),\n- \"count\": g.count,\n- \"unified_memory\": g.unified_memory,\n- })\n- })\n- .collect();\n-\n- serde_json::json!({\n- \"total_ram_gb\": round2(specs.total_ram_gb),\n- \"available_ram_gb\": round2(specs.available_ram_gb),\n- \"cpu_cores\": specs.total_cpu_cores,\n- \"cpu_name\": specs.cpu_name,\n- \"has_gpu\": specs.has_gpu,\n- \"gpu_vram_gb\": specs.gpu_vram_gb.map(round2),\n- \"gpu_name\": specs.gpu_name,\n- \"gpu_count\": specs.gpu_count,\n- \"unified_memory\": specs.unified_memory,\n- \"backend\": specs.backend.label(),\n- \"gpus\": gpus_json,\n- })\n+ serve_shared::system_json(specs)\n }\n \n fn fit_to_json(fit: &ModelFit) -> serde_json::Value {\n- serde_json::json!({\n- \"name\": fit.model.name,\n- \"provider\": fit.model.provider,\n- \"parameter_count\": fit.model.parameter_count,\n- \"params_b\": round2(fit.model.params_b()),\n- \"context_length\": fit.model.context_length,\n- \"use_case\": fit.model.use_case,\n- \"category\": fit.use_case.label(),\n- \"release_date\": fit.model.release_date,\n- \"is_moe\": fit.model.is_moe,\n- \"fit_level\": fit_level_code(fit.fit_level),\n- \"fit_label\": fit.fit_text(),\n- \"run_mode\": run_mode_code(fit.run_mode),\n- \"run_mode_label\": fit.run_mode_text(),\n- \"score\": round1(fit.score),\n- \"score_components\": {\n- \"quality\": round1(fit.score_components.quality),\n- \"speed\": round1(fit.score_components.speed),\n- \"fit\": round1(fit.score_components.fit),\n- \"context\": round1(fit.score_components.context),\n- },\n- \"estimated_tps\": round1(fit.estimated_tps),\n- \"runtime\": runtime_code(fit.runtime),\n- \"runtime_label\": fit.runtime_text(),\n- \"best_quant\": fit.best_quant,\n- \"memory_required_gb\": round2(fit.memory_required_gb),\n- \"memory_available_gb\": round2(fit.memory_available_gb),\n- \"moe_offloaded_gb\": fit.moe_offloaded_gb.map(round2),\n- \"total_memory_gb\": round2(fit.memory_required_gb + fit.moe_offloaded_gb.unwrap_or(0.0)),\n- \"utilization_pct\": round1(fit.utilization_pct),\n- \"notes\": fit.notes,\n- \"gguf_sources\": fit.model.gguf_sources,\n- \"capabilities\": fit.model.capabilities,\n- \"license\": fit.model.license,\n- \"supports_tp\": fit.model.valid_tp_sizes(),\n- })\n-}\n-\n-fn round1(v: f64) -> f64 {\n- (v * 10.0).round() / 10.0\n-}\n-\n-fn round2(v: f64) -> f64 {\n- (v * 100.0).round() / 100.0\n+ serve_shared::fit_to_json(fit)\n }\n \n #[cfg(test)]\ndiff --git a/llmfit-tui/src/serve_shared.rs b/llmfit-tui/src/serve_shared.rs\nnew file mode 100644\nindex 00000000..ad13b64a\n--- /dev/null\n+++ b/llmfit-tui/src/serve_shared.rs\n@@ -0,0 +1,106 @@\n+use llmfit_core::fit::{FitLevel, InferenceRuntime, ModelFit, RunMode};\n+use llmfit_core::hardware::SystemSpecs;\n+\n+pub fn system_json(specs: &SystemSpecs) -> serde_json::Value {\n+ let gpus_json: Vec<serde_json::Value> = specs\n+ .gpus\n+ .iter()\n+ .map(|g| {\n+ serde_json::json!({\n+ \"name\": g.name,\n+ \"vram_gb\": g.vram_gb.map(round2),\n+ \"backend\": g.backend.label(),\n+ \"count\": g.count,\n+ \"unified_memory\": g.unified_memory,\n+ })\n+ })\n+ .collect();\n+\n+ serde_json::json!({\n+ \"total_ram_gb\": round2(specs.total_ram_gb),\n+ \"available_ram_gb\": round2(specs.available_ram_gb),\n+ \"cpu_cores\": specs.total_cpu_cores,\n+ \"cpu_name\": specs.cpu_name,\n+ \"has_gpu\": specs.has_gpu,\n+ \"gpu_vram_gb\": specs.gpu_vram_gb.map(round2),\n+ \"gpu_name\": specs.gpu_name,\n+ \"gpu_count\": specs.gpu_count,\n+ \"unified_memory\": specs.unified_memory,\n+ \"backend\": specs.backend.label(),\n+ \"gpus\": gpus_json,\n+ })\n+}\n+\n+pub fn fit_to_json(fit: &ModelFit) -> serde_json::Value {\n+ serde_json::json!({\n+ \"name\": fit.model.name,\n+ \"provider\": fit.model.provider,\n+ \"parameter_count\": fit.model.parameter_count,\n+ \"params_b\": round2(fit.model.params_b()),\n+ \"context_length\": fit.model.context_length,\n+ \"use_case\": fit.model.use_case,\n+ \"category\": fit.use_case.label(),\n+ \"release_date\": fit.model.release_date,\n+ \"is_moe\": fit.model.is_moe,\n+ \"fit_level\": fit_level_code(fit.fit_level),\n+ \"fit_label\": fit.fit_text(),\n+ \"run_mode\": run_mode_code(fit.run_mode),\n+ \"run_mode_label\": fit.run_mode_text(),\n+ \"score\": round1(fit.score),\n+ \"score_components\": {\n+ \"quality\": round1(fit.score_components.quality),\n+ \"speed\": round1(fit.score_components.speed),\n+ \"fit\": round1(fit.score_components.fit),\n+ \"context\": round1(fit.score_components.context),\n+ },\n+ \"estimated_tps\": round1(fit.estimated_tps),\n+ \"runtime\": runtime_code(fit.runtime),\n+ \"runtime_label\": fit.runtime_text(),\n+ \"best_quant\": fit.best_quant,\n+ \"memory_required_gb\": round2(fit.memory_required_gb),\n+ \"memory_available_gb\": round2(fit.memory_available_gb),\n+ \"moe_offloaded_gb\": fit.moe_offloaded_gb.map(round2),\n+ \"total_memory_gb\": round2(fit.memory_required_gb + fit.moe_offloaded_gb.unwrap_or(0.0)),\n+ \"utilization_pct\": round1(fit.utilization_pct),\n+ \"notes\": fit.notes,\n+ \"gguf_sources\": fit.model.gguf_sources,\n+ \"capabilities\": fit.model.capabilities,\n+ \"license\": fit.model.license,\n+ \"supports_tp\": fit.model.valid_tp_sizes(),\n+ })\n+}\n+\n+pub fn fit_level_code(fit_level: FitLevel) -> &'static str {\n+ match fit_level {\n+ FitLevel::Perfect => \"perfect\",\n+ FitLevel::Good => \"good\",\n+ FitLevel::Marginal => \"marginal\",\n+ FitLevel::TooTight => \"too_tight\",\n+ }\n+}\n+\n+pub fn run_mode_code(run_mode: RunMode) -> &'static str {\n+ match run_mode {\n+ RunMode::Gpu => \"gpu\",\n+ RunMode::TensorParallel => \"tensor_parallel\",\n+ RunMode::MoeOffload => \"moe_offload\",\n+ RunMode::CpuOffload => \"cpu_offload\",\n+ RunMode::CpuOnly => \"cpu_only\",\n+ }\n+}\n+\n+pub fn runtime_code(runtime: InferenceRuntime) -> &'static str {\n+ match runtime {\n+ InferenceRuntime::Mlx => \"mlx\",\n+ InferenceRuntime::LlamaCpp => \"llamacpp\",\n+ InferenceRuntime::Vllm => \"vllm\",\n+ }\n+}\n+\n+pub fn round1(v: f64) -> f64 {\n+ (v * 10.0).round() / 10.0\n+}\n+\n+pub fn round2(v: f64) -> f64 {\n+ (v * 100.0).round() / 100.0\n+}\n"},"changed_files":{"kind":"list like","value":["API.md","Cargo.lock","llmfit-tui/Cargo.toml","llmfit-tui/src/events.rs","llmfit-tui/src/main.rs","llmfit-tui/src/mcp_server.rs","llmfit-tui/src/serve_api.rs","llmfit-tui/src/serve_shared.rs"],"string":"[\n \"API.md\",\n \"Cargo.lock\",\n \"llmfit-tui/Cargo.toml\",\n \"llmfit-tui/src/events.rs\",\n \"llmfit-tui/src/main.rs\",\n \"llmfit-tui/src/mcp_server.rs\",\n \"llmfit-tui/src/serve_api.rs\",\n \"llmfit-tui/src/serve_shared.rs\"\n]","isConversational":false},"review_threads":{"kind":"list like","value":[],"string":"[]","isConversational":false},"test_patch":{"kind":"string","value":""},"merged":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":495,"cells":{"repo":{"kind":"string","value":"AlexsJones/llmfit"},"pr_number":{"kind":"number","value":540,"string":"540"},"task_type":{"kind":"string","value":"issue_to_patch"},"issue_text":{"kind":"string","value":""},"pr_title":{"kind":"string","value":"chore(main): release 0.9.23"},"pr_body":{"kind":"string","value":":robot: I have created a release *beep* *boop*\n---\n\n\n## [0.9.23](https://github.com/AlexsJones/llmfit/compare/v0.9.22...v0.9.23) (2026-05-10)\n\n\n### Features\n\n* populate architecture metadata for precise KV cache and MoE speed estimation ([#539](https://github.com/AlexsJones/llmfit/issues/539)) ([bab8c01](https://github.com/AlexsJones/llmfit/commit/bab8c012771acec3bbd4a051fd3720379bb01e5d))\n\n\n### Bug Fixes\n\n* **bench:** implement 5 follow-up improvements to PR [#278](https://github.com/AlexsJones/llmfit/issues/278) ([#523](https://github.com/AlexsJones/llmfit/issues/523)) ([91fee77](https://github.com/AlexsJones/llmfit/commit/91fee7702a5728fd1b0b64459c8be024fc82051a))\n\n---\nThis PR was generated with [Release Please](https://github.com/googleapis/release-please). See [documentation](https://github.com/googleapis/release-please#release-please)."},"base_sha":{"kind":"string","value":"91fee7702a5728fd1b0b64459c8be024fc82051a"},"head_sha":{"kind":"string","value":"e43d241b39b31bb5483e61ff4c89efd256df82ee"},"gold_diff":{"kind":"string","value":"diff --git a/.release-please-manifest.json b/.release-please-manifest.json\nindex 76d14cdd..e0891d89 100644\n--- a/.release-please-manifest.json\n+++ b/.release-please-manifest.json\n@@ -1,3 +1,3 @@\n {\n- \".\": \"0.9.22\"\n+ \".\": \"0.9.23\"\n }\ndiff --git a/CHANGELOG.md b/CHANGELOG.md\nindex 4aabf8b7..71cf341a 100644\n--- a/CHANGELOG.md\n+++ b/CHANGELOG.md\n@@ -1,5 +1,17 @@\n # Changelog\n \n+## [0.9.23](https://github.com/AlexsJones/llmfit/compare/v0.9.22...v0.9.23) (2026-05-10)\n+\n+\n+### Features\n+\n+* populate architecture metadata for precise KV cache and MoE speed estimation ([#539](https://github.com/AlexsJones/llmfit/issues/539)) ([bab8c01](https://github.com/AlexsJones/llmfit/commit/bab8c012771acec3bbd4a051fd3720379bb01e5d))\n+\n+\n+### Bug Fixes\n+\n+* **bench:** implement 5 follow-up improvements to PR [#278](https://github.com/AlexsJones/llmfit/issues/278) ([#523](https://github.com/AlexsJones/llmfit/issues/523)) ([91fee77](https://github.com/AlexsJones/llmfit/commit/91fee7702a5728fd1b0b64459c8be024fc82051a))\n+\n ## [0.9.22](https://github.com/AlexsJones/llmfit/compare/v0.9.21...v0.9.22) (2026-05-06)\n \n \ndiff --git a/Cargo.lock b/Cargo.lock\nindex f3a44abd..9329e8be 100644\n--- a/Cargo.lock\n+++ b/Cargo.lock\n@@ -2361,7 +2361,7 @@ checksum = \"11d3d7f243d5c5a8b9bb5d6dd2b1602c0cb0b9db1621bafc7ed66e35ff9fe092\"\n \n [[package]]\n name = \"llmfit\"\n-version = \"0.9.22\"\n+version = \"0.9.23\"\n dependencies = [\n \"arboard\",\n \"assert_cmd\",\n@@ -2383,7 +2383,7 @@ dependencies = [\n \n [[package]]\n name = \"llmfit-core\"\n-version = \"0.9.22\"\n+version = \"0.9.23\"\n dependencies = [\n \"dirs\",\n \"http\",\n@@ -2398,7 +2398,7 @@ dependencies = [\n \n [[package]]\n name = \"llmfit-desktop\"\n-version = \"0.9.22\"\n+version = \"0.9.23\"\n dependencies = [\n \"llmfit-core\",\n \"serde\",\ndiff --git a/Cargo.toml b/Cargo.toml\nindex e1a37976..3b4954d7 100644\n--- a/Cargo.toml\n+++ b/Cargo.toml\n@@ -4,5 +4,5 @@ default-members = [\"llmfit-core\", \"llmfit-tui\"]\n resolver = \"3\"\n \n [workspace.package]\n-version = \"0.9.22\"\n+version = \"0.9.23\"\n license = \"MIT\"\ndiff --git a/version.txt b/version.txt\nindex ea3f0d7a..68a59457 100644\n--- a/version.txt\n+++ b/version.txt\n@@ -1,1 +1,1 @@\n-0.9.22\n+0.9.23\n"},"changed_files":{"kind":"list like","value":[".release-please-manifest.json","CHANGELOG.md","Cargo.lock","Cargo.toml","version.txt"],"string":"[\n \".release-please-manifest.json\",\n \"CHANGELOG.md\",\n \"Cargo.lock\",\n \"Cargo.toml\",\n \"version.txt\"\n]","isConversational":false},"review_threads":{"kind":"list like","value":[],"string":"[]","isConversational":false},"test_patch":{"kind":"string","value":""},"merged":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":496,"cells":{"repo":{"kind":"string","value":"AlexsJones/llmfit"},"pr_number":{"kind":"number","value":523,"string":"523"},"task_type":{"kind":"string","value":"issue_to_patch"},"issue_text":{"kind":"string","value":""},"pr_title":{"kind":"string","value":"fix(bench): 5 follow-up improvements to #278 (keybinding I, labels, UTF-8, dedup HTTP types, README)"},"pr_body":{"kind":"string","value":"Follow-up to #278. Implements the 5 improvements @AlexsJones described in [this comment on #278](https://github.com/AlexsJones/llmfit/pull/278#issuecomment-4365354134) — the `de4522b` commit referenced there wasn't actually pushed (SHA isn't reachable from any branch), so this PR re-implements the same items from the description.\n\n### What changed\n\n| # | Item | Where |\n|---|---|---|\n| 1 | Keybinding `B` → `I` for the inference bench view (deconflicts with `b` leaderboard) | `tui_events.rs`, `tui_ui.rs` |\n| 2 | TUI mode label `LIVE BENCH` → `INFERENCE BENCH`; `BENCHMARKS` → `COMMUNITY LEADERBOARD`; help popup, pane titles, quit-confirm text | `tui_ui.rs`, `tui_events.rs` |\n| 3 | UTF-8-safe `truncate_str` (was panicking on multi-byte chars in HF model names — real bug) + 2 new unit tests | `llmfit-tui/src/main.rs`, `llmfit-tui/src/tui_ui.rs` |\n| 4 | Deduplicated 5 HTTP response types (`OllamaGenResponse`, `ChatCompletionResponse`, `ChatChoice`, `ChatMessage`, `ChatUsage`); kept canonical in `bench.rs` as `pub(crate)`, imported in `quality.rs` | `bench.rs`, `quality.rs` |\n| 5 | README: renamed \"Community Benchmarks\" section → \"Community Leaderboard (b)\"; added new \"Inference Bench (I)\" section with keybindings, CLI usage, cache path, env vars | `README.md` |\n\n### Diff\n\n\\`\\`\\`\n README.md | +67 / -4\n llmfit-core/src/bench.rs | +38 / -19\n llmfit-core/src/quality.rs | +5 / -50\n llmfit-tui/src/main.rs | +22 / -2\n llmfit-tui/src/tui_events.rs | +8 / -4\n llmfit-tui/src/tui_ui.rs | +49 / -8\n 6 files, +147 / -92\n\\`\\`\\`\n\n### Verification\n\n- `cargo fmt --all`: clean\n- `cargo build`: clean\n- `cargo test --all`: **353 passing** (was 351, +2 from new UTF-8 truncate tests), 0 failed, 1 ignored\n- `cargo clippy`: zero new warnings; pre-existing baseline preserved\n\nSingle squashed commit. Ready for review.\n"},"base_sha":{"kind":"string","value":"8896fdb861fa6950081c144ddf23d9eb24aef7ed"},"head_sha":{"kind":"string","value":"e3405d08443a3dcf86370ae4afc5e73ae9fafc8a"},"gold_diff":{"kind":"string","value":"diff --git a/README.md b/README.md\nindex d1a884e6..19d277ea 100644\n--- a/README.md\n+++ b/README.md\n@@ -16,7 +16,7 @@\n <a href=\"https://about.signpath.io\"><img src=\"https://img.shields.io/badge/SignPath-signed-brightgreen?logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgaGVpZ2h0PSIxNiIgZmlsbD0id2hpdGUiIHZpZXdCb3g9IjAgMCAxNiAxNiI+PHBhdGggZD0iTTEwLjA2NyA0LjU2N2wtNC43MzQgNC43MzMtMS40LTEuNGExIDEgMCAwIDAtMS40MTQgMS40MTRsMi4xIDIuMWExIDEgMCAwIDAgMS40MTQgMGw1LjQ0LTUuNDRhMSAxIDAgMCAwLTEuNDE0LTEuNDE0eiIvPjwvc3ZnPg==\" alt=\"Signed with SignPath\"></a>\n </p>\n \n-> **New: [Community Benchmarks](#community-benchmarks-b)** — Browse real-world performance data from actual users. Press `b` to see measured tok/s, TTFT, and VRAM for any GPU — not just yours. Pick from 27+ hardware presets (RTX 5090 to Apple M1) with `H` to compare real numbers before you buy or build.\n+> **New: [Community Leaderboard](#community-leaderboard-b)** — Browse real-world performance data from actual users. Press `b` to see measured tok/s, TTFT, and VRAM for any GPU — not just yours. Pick from 27+ hardware presets (RTX 5090 to Apple M1) with `H` to compare real numbers before you buy or build.\n \n **Hundreds of models & providers. One command to find what runs on your hardware.**\n \n@@ -24,7 +24,7 @@ A terminal tool that right-sizes LLM models to your system's RAM, CPU, and GPU.\n \n Ships with an interactive TUI (default) and a classic CLI mode. Supports multi-GPU setups, MoE architectures, dynamic quantization selection, speed estimation, and local runtime providers (Ollama, llama.cpp, MLX, Docker Model Runner, LM Studio).\n \n-**New: [Community Benchmarks](#community-benchmarks-b) (`b`)** — See real-world tok/s, TTFT, and VRAM usage from other users running the same hardware as you. Powered by [localmaxxing.com](https://localmaxxing.com), this bridges the gap between estimated and actual performance.\n+**New: [Community Leaderboard](#community-leaderboard-b) (`b`)** — See real-world tok/s, TTFT, and VRAM usage from other users running the same hardware as you. Powered by [localmaxxing.com](https://localmaxxing.com), this bridges the gap between estimated and actual performance.\n \n Also: [Download Manager](#download-manager-d) (`D`), [Advanced Configuration](#advanced-configuration-a) (`A`), and [Hardware Simulation](#hardware-simulation-s) — Press `D` to manage downloads, view history, delete models, and configure the download directory. Press `A` to tune TPS efficiency, run mode factors, and scoring weights. Press `S` to simulate different hardware.\n \n@@ -132,7 +132,8 @@ Launches the interactive terminal UI. Your system specs (CPU, RAM, GPU name, VRA\n | `R` | Open runtime/backend filter popup (llama.cpp, MLX, vLLM) |\n | `S` | Open hardware simulation popup (override RAM/VRAM/CPU) |\n | `A` | Open advanced configuration popup (tune efficiency, run mode factors) |\n-| `b` | Open community benchmarks view (localmaxxing.com) |\n+| `b` | Open community leaderboard view (localmaxxing.com) |\n+| `I` | Open inference bench view (local quality scoring against your models) |\n | `h` | Open help popup (all key bindings) |\n | `m` | Mark selected model for compare |\n | `c` | Open compare view (marked vs selected) |\n@@ -269,11 +270,11 @@ Use `Tab` / `Shift-Tab` to cycle focus between sections.\n \n For failed downloads (e.g. 404 errors), `x` removes the entry from history. For successful downloads, it deletes the model from the provider (supported for Ollama and llama.cpp).\n \n-### Community Benchmarks (`b`)\n+### Community Leaderboard (`b`)\n \n-Press `b` to open the Community Benchmarks view. Instead of relying solely on llmfit's theoretical speed estimates, this view shows **real-world performance data** from other users with the same hardware — actual measured tok/s, time-to-first-token, and peak VRAM usage.\n+Press `b` to open the Community Leaderboard view. Instead of relying solely on llmfit's theoretical speed estimates, this view shows **real-world performance data** from other users with the same hardware — actual measured tok/s, time-to-first-token, and peak VRAM usage.\n \n-\n+\n \n Data is sourced from [localmaxxing.com](https://localmaxxing.com), a community benchmark database. When you open the view, llmfit auto-detects your hardware (GPU model, VRAM tier, Apple Silicon chip family, OS) and queries for matching results.\n \n@@ -315,6 +316,60 @@ llmfit --api-key \"bhk_your_key_here\"\n |---|---|\n | `LOCALMAXXING_API_KEY` | Bearer token for localmaxxing.com API |\n \n+### Inference Bench (`I`)\n+\n+Press `I` (uppercase) to open the Inference Bench view. This runs **live inference benchmarks against your locally running providers** — Ollama, vLLM, and MLX — measuring time-to-first-token (TTFT), tokens per second (TPS), and total latency with real inference requests.\n+\n+Unlike the Community Leaderboard (which shows crowd-sourced data from other users), Inference Bench measures your actual hardware with your actual models.\n+\n+#### TUI usage\n+\n+| Key | Action |\n+|-----|--------|\n+| `I` | Open inference bench (auto-detects provider and runs benchmarks) |\n+| `I` (again) | Rerun benchmarks from within the bench view |\n+| `j` / `k` or arrows | Navigate model results |\n+| `Enter` | Open detail view for selected model |\n+| `r` | Switch to routing matrix view |\n+| `q` / `Esc` | Close bench view |\n+\n+Results are cached to `~/.config/llmfit/bench-cache.json` and loaded instantly on subsequent opens.\n+\n+#### CLI usage\n+\n+```sh\n+# Auto-detect provider and benchmark\n+llmfit bench\n+\n+# Benchmark all discovered models across all running providers\n+llmfit bench --all\n+\n+# Benchmark a specific model via Ollama\n+llmfit bench --provider ollama llama3.2\n+\n+# Override endpoint URL\n+llmfit bench --provider ollama --url http://my-server:11434 llama3.2\n+\n+# Override vLLM endpoint\n+llmfit bench --provider vllm --url http://localhost:8000\n+\n+# Output as JSON (for scripting)\n+llmfit bench --json\n+\n+# Run quality benchmarks (role-based scoring for routing)\n+llmfit bench --quality\n+\n+# Output routing matrix\n+llmfit bench --quality --routing\n+```\n+\n+#### Environment variables\n+\n+| Variable | Default | Description |\n+|---|---|---|\n+| `OLLAMA_HOST` | `http://localhost:11434` | Ollama API base URL |\n+| `VLLM_PORT` | `8000` | vLLM server port (used as `http://localhost:$VLLM_PORT`) |\n+\n ### Themes\n \n Press `t` to cycle through 10 built-in color themes. Your selection is saved automatically to `~/.config/llmfit/theme` and restored on next launch.\ndiff --git a/llmfit-core/src/bench.rs b/llmfit-core/src/bench.rs\nindex e6f03113..2209326e 100644\n--- a/llmfit-core/src/bench.rs\n+++ b/llmfit-core/src/bench.rs\n@@ -88,21 +88,22 @@ const BENCH_PROMPTS: &[&str] = &[\n // ── Ollama benchmarking ────────────────────────────────────────────\n \n /// Ollama /api/generate response fields we care about.\n+/// Shared with `quality.rs` — both modules talk to the same endpoints.\n #[derive(serde::Deserialize, Default)]\n #[allow(dead_code)]\n-struct OllamaGenResponse {\n+pub(crate) struct OllamaGenResponse {\n #[serde(default)]\n- response: String,\n+ pub(crate) response: String,\n #[serde(default)]\n- eval_count: Option<u64>,\n+ pub(crate) eval_count: Option<u64>,\n #[serde(default)]\n- eval_duration: Option<u64>, // nanoseconds\n+ pub(crate) eval_duration: Option<u64>, // nanoseconds\n #[serde(default)]\n- prompt_eval_count: Option<u64>,\n+ pub(crate) prompt_eval_count: Option<u64>,\n #[serde(default)]\n- prompt_eval_duration: Option<u64>, // nanoseconds\n+ pub(crate) prompt_eval_duration: Option<u64>, // nanoseconds\n #[serde(default)]\n- total_duration: Option<u64>, // nanoseconds\n+ pub(crate) total_duration: Option<u64>, // nanoseconds\n }\n \n /// Benchmark a model via Ollama's /api/generate endpoint.\n@@ -209,36 +210,37 @@ fn ollama_generate(\n \n // ── OpenAI-compatible benchmarking (vLLM, MLX) ────────────────────\n \n-/// OpenAI chat completion response fields we care about.\n+/// OpenAI-compatible chat completion response fields we care about.\n+/// Shared with `quality.rs` — both modules talk to the same endpoints.\n #[derive(serde::Deserialize)]\n #[allow(dead_code)]\n-struct ChatCompletionResponse {\n+pub(crate) struct ChatCompletionResponse {\n #[serde(default)]\n- choices: Vec<ChatChoice>,\n+ pub(crate) choices: Vec<ChatChoice>,\n #[serde(default)]\n- usage: Option<ChatUsage>,\n+ pub(crate) usage: Option<ChatUsage>,\n }\n \n #[derive(serde::Deserialize)]\n #[allow(dead_code)]\n-struct ChatChoice {\n+pub(crate) struct ChatChoice {\n #[serde(default)]\n- message: Option<ChatMessage>,\n+ pub(crate) message: Option<ChatMessage>,\n }\n \n #[derive(serde::Deserialize)]\n #[allow(dead_code)]\n-struct ChatMessage {\n+pub(crate) struct ChatMessage {\n #[serde(default)]\n- content: Option<String>,\n+ pub(crate) content: Option<String>,\n }\n \n #[derive(serde::Deserialize)]\n-struct ChatUsage {\n+pub(crate) struct ChatUsage {\n #[serde(default)]\n- prompt_tokens: u32,\n+ pub(crate) prompt_tokens: u32,\n #[serde(default)]\n- completion_tokens: u32,\n+ pub(crate) completion_tokens: u32,\n }\n \n /// Benchmark a model via OpenAI-compatible /v1/chat/completions (vLLM, MLX).\ndiff --git a/llmfit-core/src/quality.rs b/llmfit-core/src/quality.rs\nindex 8d86058f..9ec02dfb 100644\n--- a/llmfit-core/src/quality.rs\n+++ b/llmfit-core/src/quality.rs\n@@ -9,6 +9,10 @@ use std::time::{Duration, Instant};\n use regex::Regex;\n use serde::{Deserialize, Serialize};\n \n+// HTTP response types are defined once in bench.rs and shared here to prevent\n+// the two modules from silently diverging if fields are added or changed.\n+use crate::bench::{ChatCompletionResponse, ChatUsage, OllamaGenResponse};\n+\n // ── Types ──────────────────────────────────────────────────────────\n \n /// A single scoring rule applied to a model response.\n@@ -114,57 +118,6 @@ pub struct RoutingRecommendation {\n pub note: Option<String>,\n }\n \n-// ── Ollama response types (reuse pattern from bench.rs) ────────────\n-\n-#[derive(Deserialize, Default)]\n-#[allow(dead_code)]\n-struct OllamaGenResponse {\n- #[serde(default)]\n- response: String,\n- #[serde(default)]\n- eval_count: Option<u64>,\n- #[serde(default)]\n- eval_duration: Option<u64>,\n- #[serde(default)]\n- prompt_eval_count: Option<u64>,\n- #[serde(default)]\n- prompt_eval_duration: Option<u64>,\n- #[serde(default)]\n- total_duration: Option<u64>,\n-}\n-\n-#[derive(Deserialize)]\n-#[allow(dead_code)]\n-struct ChatCompletionResponse {\n- #[serde(default)]\n- choices: Vec<ChatChoice>,\n- #[serde(default)]\n- usage: Option<ChatUsage>,\n-}\n-\n-#[derive(Deserialize)]\n-#[allow(dead_code)]\n-struct ChatChoice {\n- #[serde(default)]\n- message: Option<ChatMessage>,\n-}\n-\n-#[derive(Deserialize)]\n-#[allow(dead_code)]\n-struct ChatMessage {\n- #[serde(default)]\n- content: Option<String>,\n-}\n-\n-#[derive(Deserialize)]\n-#[allow(dead_code)]\n-struct ChatUsage {\n- #[serde(default)]\n- prompt_tokens: u32,\n- #[serde(default)]\n- completion_tokens: u32,\n-}\n-\n // ── Scoring ────────────────────────────────────────────────────────\n \n /// Evaluate a model response against a set of scoring rules.\ndiff --git a/llmfit-tui/src/main.rs b/llmfit-tui/src/main.rs\nindex 411f044d..4a467ca0 100644\n--- a/llmfit-tui/src/main.rs\n+++ b/llmfit-tui/src/main.rs\n@@ -1943,11 +1943,15 @@ fn target_info(target: &bench::BenchTarget) -> (&str, &str, &str) {\n }\n }\n \n+/// Returns at most `max` characters of `s`, appending `…` if truncated.\n+/// Uses char-aware slicing to avoid panics on multi-byte UTF-8 characters\n+/// (e.g. CJK ideographs, emoji) that appear in HuggingFace model names.\n fn truncate_str(s: &str, max: usize) -> String {\n- if s.len() <= max {\n+ if s.chars().count() <= max {\n s.to_string()\n } else {\n- format!(\"{}…\", &s[..max - 1])\n+ let head: String = s.chars().take(max.saturating_sub(1)).collect();\n+ format!(\"{head}…\")\n }\n }\n \n@@ -2935,4 +2939,18 @@ mod tests {\n .expect(\"should resolve exact model\");\n assert_eq!(idx, 1);\n }\n+\n+ #[test]\n+ fn truncate_str_handles_multibyte_utf8() {\n+ // ASCII — no truncation needed\n+ assert_eq!(truncate_str(\"hello\", 10), \"hello\");\n+ // ASCII — truncation\n+ assert_eq!(truncate_str(\"hello world\", 5), \"hell…\");\n+ // CJK ideographs (3-byte UTF-8 each) — must not panic\n+ assert_eq!(truncate_str(\"こんにちは世界\", 4), \"こんに…\");\n+ // Single emoji (4-byte UTF-8) — must not panic on byte boundary\n+ assert_eq!(truncate_str(\"🚀 hello\", 4), \"🚀 h…\");\n+ // Exact max length — no truncation\n+ assert_eq!(truncate_str(\"abc\", 3), \"abc\");\n+ }\n }\ndiff --git a/llmfit-tui/src/tui_events.rs b/llmfit-tui/src/tui_events.rs\nindex 16ddcd47..1b84db35 100644\n--- a/llmfit-tui/src/tui_events.rs\n+++ b/llmfit-tui/src/tui_events.rs\n@@ -71,7 +71,7 @@ fn handle_normal_mode(app: &mut App, key: KeyEvent) {\n } else if app.bench_running {\n app.bench_confirm_quit = true;\n app.bench_progress =\n- \"Benchmarks running! Press q again to exit bench, any key to cancel\"\n+ \"Inference bench running! Press q again to exit, any key to cancel\"\n .to_string();\n } else {\n app.close_bench();\n@@ -213,9 +213,9 @@ fn handle_normal_mode(app: &mut App, key: KeyEvent) {\n // Benchmarks view (localmaxxing.com community leaderboard)\n KeyCode::Char('b') => app.open_benchmarks(),\n \n- // Live inference-bench view (llmfit bench — B=open, B again=rerun)\n- KeyCode::Char('B') if app.show_bench => app.rerun_bench(),\n- KeyCode::Char('B') => app.open_bench(),\n+ // Live inference-bench view (llmfit bench — I=open, I again=rerun)\n+ KeyCode::Char('I') if app.show_bench => app.rerun_bench(),\n+ KeyCode::Char('I') => app.open_bench(),\n \n // Advanced Config popup\n KeyCode::Char('A') => app.open_advanced_config_popup(),\ndiff --git a/llmfit-tui/src/tui_ui.rs b/llmfit-tui/src/tui_ui.rs\nindex d75507d6..b99bdb81 100644\n--- a/llmfit-tui/src/tui_ui.rs\n+++ b/llmfit-tui/src/tui_ui.rs\n@@ -1567,11 +1567,15 @@ fn draw_multi_compare(frame: &mut Frame, app: &App, area: Rect, tc: &ThemeColors\n frame.render_widget(table, area);\n }\n \n+/// Returns at most `max_len` characters of `s`, appending `~` if truncated.\n+/// Uses char-aware slicing to avoid panics on multi-byte UTF-8 characters\n+/// (e.g. CJK ideographs, emoji) that appear in HuggingFace model names.\n fn truncate_str(s: &str, max_len: usize) -> String {\n- if s.len() <= max_len {\n+ if s.chars().count() <= max_len {\n s.to_string()\n } else {\n- format!(\"{}~\", &s[..max_len.saturating_sub(1)])\n+ let head: String = s.chars().take(max_len.saturating_sub(1)).collect();\n+ format!(\"{head}~\")\n }\n }\n \n@@ -2764,12 +2768,12 @@ fn status_keys_and_mode(app: &App) -> (String, String) {\n if app.bench_show_detail {\n \" j/k:scroll Enter/q:close detail r:routing\".to_string()\n } else {\n- \" j/k:select Enter:detail r:routing B:rerun q:back\".to_string()\n+ \" j/k:select Enter:detail r:routing I:rerun q:back\".to_string()\n }\n }\n BenchViewMode::Routing => \" r:results q:back\".to_string(),\n };\n- return (keys, \"LIVE BENCH\".to_string());\n+ return (keys, \"INFERENCE BENCH\".to_string());\n }\n if app.show_multi_compare {\n return (\n@@ -2899,7 +2903,7 @@ fn status_keys_and_mode(app: &App) -> (String, String) {\n ),\n InputMode::Benchmarks => (\n \" ↑/k:up ↓/j:down H:change GPU r:refresh b/q/Esc:close\".to_string(),\n- \"BENCHMARKS\".to_string(),\n+ \"COMMUNITY LEADERBOARD\".to_string(),\n ),\n }\n }\n@@ -3268,8 +3272,12 @@ fn draw_help_popup(frame: &mut Frame, app: &App, tc: &ThemeColors) {\n (\" d\", \"Download/pull model\"),\n (\" r\", \"Refresh installed models\"),\n (\" p\", \"Plan mode\"),\n- (\" b\", \"Benchmarks (localmaxxing.com)\"),\n- (\" H\", \"Change GPU (in benchmarks view)\"),\n+ (\" b\", \"Community Leaderboard (localmaxxing.com)\"),\n+ (\n+ \" I\",\n+ \"Inference Bench (local quality scoring against your models)\",\n+ ),\n+ (\" H\", \"Change GPU (in community leaderboard view)\"),\n (\" y\", \"Copy model name\"),\n (\"\", \"\"),\n (\"Comparison\", \"\"),\n@@ -4006,7 +4014,7 @@ fn draw_benchmarks(frame: &mut Frame, app: &mut App, area: Rect, tc: &ThemeColor\n let block = Block::default()\n .borders(Borders::ALL)\n .border_style(Style::default().fg(tc.accent))\n- .title(\" Benchmarks \")\n+ .title(\" Community Leaderboard \")\n .title_style(Style::default().fg(tc.accent).add_modifier(Modifier::BOLD));\n \n let inner = block.inner(area);\n@@ -4501,12 +4509,12 @@ fn draw_bench(frame: &mut Frame, app: &App, area: Rect, tc: &ThemeColors) {\n let title = match app.bench_view_mode {\n BenchViewMode::Results => {\n if app.bench_show_detail {\n- \" BENCH: Quality Benchmarks (j/k=scroll, Enter/q=close detail) \"\n+ \" INFERENCE BENCH: Quality Benchmarks (j/k=scroll, Enter/q=close detail) \"\n } else {\n- \" BENCH: Quality Benchmarks (j/k=select, Enter=detail, r=routing) \"\n+ \" INFERENCE BENCH: Quality Benchmarks (j/k=select, Enter=detail, r=routing) \"\n }\n }\n- BenchViewMode::Routing => \" BENCH: Routing Matrix (r=results, q=back) \",\n+ BenchViewMode::Routing => \" INFERENCE BENCH: Routing Matrix (r=results, q=back) \",\n };\n \n let block = Block::default()\n@@ -5158,3 +5166,22 @@ fn draw_bench(frame: &mut Frame, app: &App, area: Rect, tc: &ThemeColors) {\n }\n }\n }\n+\n+#[cfg(test)]\n+mod tests {\n+ use super::*;\n+\n+ #[test]\n+ fn truncate_str_handles_multibyte_utf8() {\n+ // ASCII — no truncation needed\n+ assert_eq!(truncate_str(\"hello\", 10), \"hello\");\n+ // ASCII — truncation with tilde marker\n+ assert_eq!(truncate_str(\"hello world\", 5), \"hell~\");\n+ // CJK ideographs (3-byte UTF-8 each) — must not panic\n+ assert_eq!(truncate_str(\"こんにちは世界\", 4), \"こんに~\");\n+ // Single emoji (4-byte UTF-8) — must not panic on byte boundary\n+ assert_eq!(truncate_str(\"🚀 hello\", 4), \"🚀 h~\");\n+ // Exact max length — no truncation\n+ assert_eq!(truncate_str(\"abc\", 3), \"abc\");\n+ }\n+}\n"},"changed_files":{"kind":"list like","value":["README.md","llmfit-core/src/bench.rs","llmfit-core/src/quality.rs","llmfit-tui/src/main.rs","llmfit-tui/src/tui_events.rs","llmfit-tui/src/tui_ui.rs"],"string":"[\n \"README.md\",\n \"llmfit-core/src/bench.rs\",\n \"llmfit-core/src/quality.rs\",\n \"llmfit-tui/src/main.rs\",\n \"llmfit-tui/src/tui_events.rs\",\n \"llmfit-tui/src/tui_ui.rs\"\n]","isConversational":false},"review_threads":{"kind":"list like","value":[],"string":"[]","isConversational":false},"test_patch":{"kind":"string","value":""},"merged":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":497,"cells":{"repo":{"kind":"string","value":"AlexsJones/llmfit"},"pr_number":{"kind":"number","value":539,"string":"539"},"task_type":{"kind":"string","value":"issue_to_patch"},"issue_text":{"kind":"string","value":""},"pr_title":{"kind":"string","value":"feat: populate architecture metadata for precise KV cache and MoE speed estimation"},"pr_body":{"kind":"string","value":"## Summary\n\n- **Populate architecture metadata** (`hidden_size`, `vocab_size`, `moe_intermediate_size`, `shared_expert_intermediate_size`) from HuggingFace config.json for both curated and auto-discovered models, enabling the existing but previously starved Tier 1 MoE speed estimator and precise KV cache formula\n- **Fix wildly optimistic MoE speed estimates** caused by FP8/INT4/INT8 repos reporting quantized element counts as `parameters_raw` (e.g. `unsloth/Qwen3-Coder-Next-FP8` reported 2.1B instead of 79.7B, giving 1606 tok/s instead of 43 tok/s)\n- **Expand model catalog** from 1,520 to 1,852 models with 10K min-downloads discovery\n\n### Before → After\n| Metric | Before | After |\n|--------|--------|-------|\n| Architecture metadata coverage | 78/1520 (5%) | 1597/1852 (86%) |\n| MoE Tier 1 (accurate speed) | 0% | 353/356 (99%) |\n| Qwen3-Coder-Next-FP8 tok/s | 1606 (wrong) | 43 (correct) |\n| Total models | 1,520 | 1,852 |\n\n## Test plan\n\n- [x] All 358 Rust tests pass\n- [x] Verified no MoE models >5B params show >500 tok/s\n- [x] Architecture estimate validated against known models (Llama-3.1-8B → 8.0B, Mixtral-8x7B → 46.7B, Qwen3-Coder-Next → 79.0B)\n- [x] `text_config` fallback tested for multimodal models (Llama 4, Qwen-VL)\n- [x] List-type config values handled (ERNIE-4.5-VL edge case)"},"base_sha":{"kind":"string","value":"4624ae88fa3ed7d59753a93bec09df47838e8679"},"head_sha":{"kind":"string","value":"55fa5f574d8e3596fa874817611b6d6ea8e18ebe"},"gold_diff":{"kind":"string","value":"diff --git a/data/hf_models.json b/data/hf_models.json\nindex c7945062..6070caa4 100644\n--- a/data/hf_models.json\n+++ b/data/hf_models.json\n@@ -1,4 +1,60 @@\n [\n+ {\n+ \"name\": \"brain-bzh/reve-positions\",\n+ \"provider\": \"brain-bzh\",\n+ \"parameter_count\": \"2K\",\n+ \"parameters_raw\": 1629,\n+ \"min_ram_gb\": 1.0,\n+ \"recommended_ram_gb\": 2.0,\n+ \"min_vram_gb\": 0.5,\n+ \"quantization\": \"Q4_K_M\",\n+ \"format\": \"gguf\",\n+ \"context_length\": 4096,\n+ \"use_case\": \"General purpose\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"reve-position-bank\",\n+ \"hf_downloads\": 34113,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": null,\n+ \"num_attention_heads\": null,\n+ \"num_key_value_heads\": null,\n+ \"head_dim\": null,\n+ \"hidden_size\": null,\n+ \"vocab_size\": null,\n+ \"moe_intermediate_size\": null,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true\n+ },\n+ {\n+ \"name\": \"optimum-intel-internal-testing/tiny-random-squeezebert\",\n+ \"provider\": \"optimum-intel-internal-testing\",\n+ \"parameter_count\": \"78K\",\n+ \"parameters_raw\": 78336,\n+ \"min_ram_gb\": 1.0,\n+ \"recommended_ram_gb\": 2.0,\n+ \"min_vram_gb\": 0.5,\n+ \"quantization\": \"Q4_K_M\",\n+ \"format\": \"gguf\",\n+ \"context_length\": 512,\n+ \"use_case\": \"Lightweight, edge deployment\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"squeezebert\",\n+ \"hf_downloads\": 39446,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": 5,\n+ \"num_attention_heads\": 4,\n+ \"num_key_value_heads\": 4,\n+ \"head_dim\": 8,\n+ \"hidden_size\": 32,\n+ \"vocab_size\": 1124,\n+ \"moe_intermediate_size\": 64,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true\n+ },\n {\n \"name\": \"echarlaix/tiny-random-PhiForCausalLM\",\n \"provider\": \"echarlaix\",\n@@ -14,9 +70,73 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"phi\",\n- \"hf_downloads\": 27280,\n+ \"hf_downloads\": 29465,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": 2,\n+ \"num_attention_heads\": 4,\n+ \"num_key_value_heads\": 4,\n+ \"head_dim\": 8,\n+ \"hidden_size\": 32,\n+ \"vocab_size\": 1024,\n+ \"moe_intermediate_size\": 37,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true\n+ },\n+ {\n+ \"name\": \"optimum-intel-internal-testing/tiny-random-bigbird_pegasus\",\n+ \"provider\": \"optimum-intel-internal-testing\",\n+ \"parameter_count\": \"82K\",\n+ \"parameters_raw\": 82396,\n+ \"min_ram_gb\": 1.0,\n+ \"recommended_ram_gb\": 2.0,\n+ \"min_vram_gb\": 0.5,\n+ \"quantization\": \"Q4_K_M\",\n+ \"format\": \"gguf\",\n+ \"context_length\": 260,\n+ \"use_case\": \"Lightweight, edge deployment\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"bigbird_pegasus\",\n+ \"hf_downloads\": 71969,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": 2,\n+ \"num_attention_heads\": null,\n+ \"num_key_value_heads\": null,\n+ \"head_dim\": null,\n+ \"hidden_size\": null,\n+ \"vocab_size\": 1007,\n+ \"moe_intermediate_size\": null,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true\n+ },\n+ {\n+ \"name\": \"optimum-intel-internal-testing/tiny-random-deberta\",\n+ \"provider\": \"optimum-intel-internal-testing\",\n+ \"parameter_count\": \"83K\",\n+ \"parameters_raw\": 82745,\n+ \"min_ram_gb\": 1.0,\n+ \"recommended_ram_gb\": 2.0,\n+ \"min_vram_gb\": 0.5,\n+ \"quantization\": \"Q4_K_M\",\n+ \"format\": \"gguf\",\n+ \"context_length\": 512,\n+ \"use_case\": \"Lightweight, edge deployment\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"deberta\",\n+ \"hf_downloads\": 23764,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 5,\n+ \"num_attention_heads\": 4,\n+ \"num_key_value_heads\": 4,\n+ \"head_dim\": 8,\n+ \"hidden_size\": 32,\n+ \"vocab_size\": 1000,\n+ \"moe_intermediate_size\": 37,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true\n },\n {\n@@ -34,9 +154,129 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"gpt2\",\n- \"hf_downloads\": 55814,\n+ \"hf_downloads\": 57787,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": null,\n+ \"num_attention_heads\": null,\n+ \"num_key_value_heads\": null,\n+ \"head_dim\": null,\n+ \"hidden_size\": null,\n+ \"vocab_size\": 1024,\n+ \"moe_intermediate_size\": null,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true\n+ },\n+ {\n+ \"name\": \"peft-internal-testing/tiny-random-DebertaModel\",\n+ \"provider\": \"peft-internal-testing\",\n+ \"parameter_count\": \"84K\",\n+ \"parameters_raw\": 83513,\n+ \"min_ram_gb\": 1.0,\n+ \"recommended_ram_gb\": 2.0,\n+ \"min_vram_gb\": 0.5,\n+ \"quantization\": \"Q4_K_M\",\n+ \"format\": \"gguf\",\n+ \"context_length\": 512,\n+ \"use_case\": \"Lightweight, edge deployment\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"deberta\",\n+ \"hf_downloads\": 24600,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": 5,\n+ \"num_attention_heads\": 4,\n+ \"num_key_value_heads\": 4,\n+ \"head_dim\": 8,\n+ \"hidden_size\": 32,\n+ \"vocab_size\": 1024,\n+ \"moe_intermediate_size\": 37,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true\n+ },\n+ {\n+ \"name\": \"peft-internal-testing/tiny-random-RobertaModel\",\n+ \"provider\": \"peft-internal-testing\",\n+ \"parameter_count\": \"85K\",\n+ \"parameters_raw\": 84729,\n+ \"min_ram_gb\": 1.0,\n+ \"recommended_ram_gb\": 2.0,\n+ \"min_vram_gb\": 0.5,\n+ \"quantization\": \"Q4_K_M\",\n+ \"format\": \"gguf\",\n+ \"context_length\": 512,\n+ \"use_case\": \"Lightweight, edge deployment\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"roberta\",\n+ \"hf_downloads\": 35260,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": 5,\n+ \"num_attention_heads\": 4,\n+ \"num_key_value_heads\": 4,\n+ \"head_dim\": 8,\n+ \"hidden_size\": 32,\n+ \"vocab_size\": 1024,\n+ \"moe_intermediate_size\": 37,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true\n+ },\n+ {\n+ \"name\": \"peft-internal-testing/tiny-random-BertModel\",\n+ \"provider\": \"peft-internal-testing\",\n+ \"parameter_count\": \"88K\",\n+ \"parameters_raw\": 87929,\n+ \"min_ram_gb\": 1.0,\n+ \"recommended_ram_gb\": 2.0,\n+ \"min_vram_gb\": 0.5,\n+ \"quantization\": \"Q4_K_M\",\n+ \"format\": \"gguf\",\n+ \"context_length\": 512,\n+ \"use_case\": \"Lightweight, edge deployment\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"bert\",\n+ \"hf_downloads\": 56265,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": 5,\n+ \"num_attention_heads\": 4,\n+ \"num_key_value_heads\": 4,\n+ \"head_dim\": 8,\n+ \"hidden_size\": 32,\n+ \"vocab_size\": 1124,\n+ \"moe_intermediate_size\": 37,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true\n+ },\n+ {\n+ \"name\": \"peft-internal-testing/tiny-random-BloomForCausalLM\",\n+ \"provider\": \"peft-internal-testing\",\n+ \"parameter_count\": \"96K\",\n+ \"parameters_raw\": 96416,\n+ \"min_ram_gb\": 1.0,\n+ \"recommended_ram_gb\": 2.0,\n+ \"min_vram_gb\": 0.5,\n+ \"quantization\": \"Q4_K_M\",\n+ \"format\": \"gguf\",\n+ \"context_length\": 7,\n+ \"use_case\": \"Lightweight, edge deployment\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"bloom\",\n+ \"hf_downloads\": 11089,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": null,\n+ \"num_attention_heads\": null,\n+ \"num_key_value_heads\": null,\n+ \"head_dim\": null,\n+ \"hidden_size\": 32,\n+ \"vocab_size\": 1024,\n+ \"moe_intermediate_size\": null,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true\n },\n {\n@@ -54,9 +294,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"llama\",\n- \"hf_downloads\": 144815,\n+ \"hf_downloads\": 137555,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 2,\n+ \"num_attention_heads\": 4,\n+ \"num_key_value_heads\": 4,\n+ \"head_dim\": 4,\n+ \"hidden_size\": 16,\n+ \"vocab_size\": 3000,\n+ \"moe_intermediate_size\": 64,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true\n },\n {\n@@ -74,9 +322,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"gpt2\",\n- \"hf_downloads\": 47224,\n+ \"hf_downloads\": 48925,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": null,\n+ \"num_attention_heads\": null,\n+ \"num_key_value_heads\": null,\n+ \"head_dim\": null,\n+ \"hidden_size\": null,\n+ \"vocab_size\": 1000,\n+ \"moe_intermediate_size\": 37,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"gguf_sources\": [\n {\n@@ -100,70 +356,46 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"gptj\",\n- \"hf_downloads\": 59115,\n+ \"hf_downloads\": 60417,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": null,\n+ \"num_attention_heads\": null,\n+ \"num_key_value_heads\": null,\n+ \"head_dim\": null,\n+ \"hidden_size\": null,\n+ \"vocab_size\": 1024,\n+ \"moe_intermediate_size\": 37,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true\n },\n {\n- \"name\": \"allenai/Olmo-3-7B-Instruct\",\n- \"provider\": \"allenai\",\n- \"parameter_count\": \"528K\",\n- \"parameters_raw\": 528384,\n- \"min_ram_gb\": 1.0,\n- \"recommended_ram_gb\": 2.0,\n- \"min_vram_gb\": 0.5,\n- \"quantization\": \"Q4_K_M\",\n- \"format\": \"gguf\",\n- \"context_length\": 524288,\n- \"use_case\": \"Instruction following, chat\",\n- \"capabilities\": [],\n- \"pipeline_tag\": \"unknown\",\n- \"architecture\": \"olmo3\",\n- \"hf_downloads\": 672103,\n- \"hf_likes\": 0,\n- \"release_date\": null,\n- \"_discovered\": true,\n- \"gguf_sources\": [\n- {\n- \"repo\": \"unsloth/Olmo-3-7B-Instruct-GGUF\",\n- \"provider\": \"unsloth\"\n- },\n- {\n- \"repo\": \"mradermacher/Olmo-3-7B-Instruct-GGUF\",\n- \"provider\": \"mradermacher\"\n- }\n- ]\n- },\n- {\n- \"name\": \"allenai/Olmo-3-7B-Think\",\n- \"provider\": \"allenai\",\n- \"parameter_count\": \"528K\",\n- \"parameters_raw\": 528384,\n+ \"name\": \"yujiepan/llama-2-tiny-random\",\n+ \"provider\": \"yujiepan\",\n+ \"parameter_count\": \"513K\",\n+ \"parameters_raw\": 513050,\n \"min_ram_gb\": 1.0,\n \"recommended_ram_gb\": 2.0,\n \"min_vram_gb\": 0.5,\n \"quantization\": \"Q4_K_M\",\n \"format\": \"gguf\",\n- \"context_length\": 524288,\n- \"use_case\": \"General purpose\",\n+ \"context_length\": 4096,\n+ \"use_case\": \"Lightweight, edge deployment\",\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n- \"architecture\": \"olmo3\",\n- \"hf_downloads\": 56804,\n+ \"architecture\": \"llama\",\n+ \"hf_downloads\": 20083,\n \"hf_likes\": 0,\n \"release_date\": null,\n- \"_discovered\": true,\n- \"gguf_sources\": [\n- {\n- \"repo\": \"unsloth/Olmo-3-7B-Think-GGUF\",\n- \"provider\": \"unsloth\"\n- },\n- {\n- \"repo\": \"mradermacher/Olmo-3-7B-Think-GGUF\",\n- \"provider\": \"mradermacher\"\n- }\n- ]\n+ \"num_hidden_layers\": 1,\n+ \"num_attention_heads\": 2,\n+ \"num_key_value_heads\": 2,\n+ \"head_dim\": 4,\n+ \"hidden_size\": 8,\n+ \"vocab_size\": 32000,\n+ \"moe_intermediate_size\": 32,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true\n },\n {\n \"name\": \"optimum-intel-internal-testing/tiny-random-llava-next-mistral\",\n@@ -182,9 +414,17 @@\n ],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"llava_next\",\n- \"hf_downloads\": 47527,\n+ \"hf_downloads\": 51618,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 2,\n+ \"num_attention_heads\": 4,\n+ \"num_key_value_heads\": 2,\n+ \"head_dim\": 2,\n+ \"hidden_size\": 8,\n+ \"vocab_size\": 32005,\n+ \"moe_intermediate_size\": 32,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true\n },\n {\n@@ -202,9 +442,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"llama\",\n- \"hf_downloads\": 110905,\n+ \"hf_downloads\": 109704,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 2,\n+ \"num_attention_heads\": 2,\n+ \"num_key_value_heads\": 2,\n+ \"head_dim\": 6,\n+ \"hidden_size\": 12,\n+ \"vocab_size\": 32000,\n+ \"moe_intermediate_size\": 24,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true\n },\n {\n@@ -222,59 +470,73 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"opt\",\n- \"hf_downloads\": 615390,\n+ \"hf_downloads\": 640193,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 5,\n+ \"num_attention_heads\": 4,\n+ \"num_key_value_heads\": 4,\n+ \"head_dim\": 4,\n+ \"hidden_size\": 16,\n+ \"vocab_size\": 50265,\n+ \"moe_intermediate_size\": null,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true\n },\n {\n- \"name\": \"zai-org/AutoGLM-Phone-9B\",\n- \"provider\": \"zai-org\",\n- \"parameter_count\": \"934K\",\n- \"parameters_raw\": 934400,\n+ \"name\": \"yujiepan/llama-3-tiny-random\",\n+ \"provider\": \"yujiepan\",\n+ \"parameter_count\": \"1M\",\n+ \"parameters_raw\": 1026500,\n \"min_ram_gb\": 1.0,\n \"recommended_ram_gb\": 2.0,\n \"min_vram_gb\": 0.5,\n \"quantization\": \"Q4_K_M\",\n 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131072,\n+ \"moe_intermediate_size\": 1856,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 128,\n@@ -29132,9 +46321,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"unknown\",\n- \"hf_downloads\": 59619,\n+ \"hf_downloads\": 52109,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 52,\n+ \"num_attention_heads\": 32,\n+ \"num_key_value_heads\": 2,\n+ \"head_dim\": 128,\n+ \"hidden_size\": 2688,\n+ \"vocab_size\": 131072,\n+ \"moe_intermediate_size\": 1856,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 128,\n@@ -29156,9 +46353,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"nemotron_h\",\n- \"hf_downloads\": 14996,\n+ \"hf_downloads\": 14821,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 52,\n+ \"num_attention_heads\": 32,\n+ 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\"num_key_value_heads\": 128,\n+ \"head_dim\": 56,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 129280,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true,\n+ \"is_moe\": true,\n+ \"num_experts\": 256,\n+ \"active_experts\": 8,\n+ \"active_parameters\": 54548594820\n+ },\n+ {\n+ \"name\": \"deepseek-ai/DeepSeek-V3.1\",\n+ \"provider\": \"DeepSeek\",\n+ \"parameter_count\": \"684.5B\",\n+ \"parameters_raw\": 684531386000,\n+ \"min_ram_gb\": 382.5,\n+ \"recommended_ram_gb\": 637.5,\n+ \"min_vram_gb\": 350.6,\n+ \"quantization\": \"Q4_K_M\",\n+ \"format\": \"gguf\",\n+ \"context_length\": 6553600,\n+ \"use_case\": \"General purpose\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"deepseek_v3\",\n+ \"hf_downloads\": 189544,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": 61,\n+ \"num_attention_heads\": 128,\n+ \"num_key_value_heads\": 128,\n+ \"head_dim\": 56,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 129280,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 256,\n@@ -35213,9 +56208,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"deepseek_v32\",\n- \"hf_downloads\": 200205,\n+ \"hf_downloads\": 273151,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 61,\n+ \"num_attention_heads\": 128,\n+ \"num_key_value_heads\": 128,\n+ \"head_dim\": 56,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 129280,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 256,\n@@ -35237,13 +56240,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"text-generation\",\n \"architecture\": \"deepseek_v32\",\n- \"hf_downloads\": 11633275,\n- \"hf_likes\": 1427,\n+ \"hf_downloads\": 11943356,\n+ \"hf_likes\": 1429,\n \"release_date\": \"2025-12-01\",\n \"num_hidden_layers\": 61,\n \"num_attention_heads\": 128,\n \"num_key_value_heads\": 128,\n \"head_dim\": 56,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 129280,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"is_moe\": true,\n \"num_experts\": 256,\n \"active_experts\": 8,\n@@ -35255,6 +56262,70 @@\n }\n ]\n },\n+ {\n+ \"name\": \"deepseek-ai/DeepSeek-V3.2-Exp\",\n+ \"provider\": \"DeepSeek\",\n+ \"parameter_count\": \"685.4B\",\n+ \"parameters_raw\": 685396921376,\n+ \"min_ram_gb\": 383.0,\n+ \"recommended_ram_gb\": 638.3,\n+ \"min_vram_gb\": 351.1,\n+ \"quantization\": \"Q4_K_M\",\n+ \"format\": \"gguf\",\n+ \"context_length\": 6553600,\n+ \"use_case\": \"General purpose\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"deepseek_v32\",\n+ \"hf_downloads\": 212828,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": 61,\n+ \"num_attention_heads\": 128,\n+ \"num_key_value_heads\": 128,\n+ \"head_dim\": 56,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 129280,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true,\n+ \"is_moe\": true,\n+ \"num_experts\": 256,\n+ \"active_experts\": 8,\n+ \"active_parameters\": 54617567164\n+ },\n+ {\n+ \"name\": \"inferencerlabs/GLM-5.1-MLX-4.8bit-INF\",\n+ \"provider\": \"inferencerlabs\",\n+ \"parameter_count\": \"743.9B\",\n+ \"parameters_raw\": 743911218432,\n+ \"min_ram_gb\": 415.7,\n+ \"recommended_ram_gb\": 692.8,\n+ \"min_vram_gb\": 381.1,\n+ \"quantization\": \"Q4_K_M\",\n+ \"format\": \"mlx\",\n+ \"context_length\": 202752,\n+ \"use_case\": \"General purpose\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"glm_moe_dsa\",\n+ \"hf_downloads\": 10742,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": 78,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 64,\n+ \"hidden_size\": 6144,\n+ \"vocab_size\": 154880,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true,\n+ \"is_moe\": true,\n+ \"num_experts\": 256,\n+ \"active_experts\": 8,\n+ \"active_parameters\": 59280425217\n+ },\n {\n \"name\": \"zai-org/GLM-5\",\n \"provider\": \"zai-org\",\n@@ -35270,13 +56341,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"text-generation\",\n \"architecture\": \"glm_moe_dsa\",\n- \"hf_downloads\": 315464,\n- \"hf_likes\": 2082,\n+ \"hf_downloads\": 308513,\n+ \"hf_likes\": 2084,\n \"release_date\": \"2026-02-11\",\n \"num_hidden_layers\": 78,\n \"num_attention_heads\": 64,\n \"num_key_value_heads\": 64,\n \"head_dim\": 64,\n+ \"hidden_size\": 6144,\n+ \"vocab_size\": 154880,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"is_moe\": true,\n \"num_experts\": 256,\n \"active_experts\": 8,\n@@ -35303,9 +56378,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"glm_moe_dsa\",\n- \"hf_downloads\": 295110,\n+ \"hf_downloads\": 292743,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 78,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 64,\n+ \"hidden_size\": 6144,\n+ \"vocab_size\": 154880,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 256,\n@@ -35327,9 +56410,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"glm_moe_dsa\",\n- \"hf_downloads\": 1609767,\n+ \"hf_downloads\": 1148578,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 78,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 64,\n+ \"hidden_size\": 6144,\n+ \"vocab_size\": 154880,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 256,\n@@ -35351,15 +56442,55 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"glm_moe_dsa\",\n- \"hf_downloads\": 877686,\n+ \"hf_downloads\": 934979,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 78,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 64,\n+ \"hidden_size\": 6144,\n+ \"vocab_size\": 154880,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 256,\n \"active_experts\": 8,\n \"active_parameters\": 60077205033\n },\n+ {\n+ \"name\": \"cyankiwi/GLM-5.1-AWQ-4bit\",\n+ \"provider\": \"cyankiwi\",\n+ \"parameter_count\": \"766.9B\",\n+ \"parameters_raw\": 766909554882,\n+ \"min_ram_gb\": 428.5,\n+ \"recommended_ram_gb\": 714.2,\n+ \"min_vram_gb\": 392.8,\n+ \"quantization\": \"AWQ-4bit\",\n+ \"format\": \"awq\",\n+ \"context_length\": 202752,\n+ \"use_case\": \"General purpose\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"glm_moe_dsa\",\n+ \"hf_downloads\": 14401,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": 78,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 64,\n+ \"hidden_size\": 6144,\n+ \"vocab_size\": 154880,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true,\n+ \"is_moe\": true,\n+ \"num_experts\": 256,\n+ \"active_experts\": 8,\n+ \"active_parameters\": 61113105152\n+ },\n {\n \"name\": \"deepseek-ai/DeepSeek-V4-Pro\",\n \"provider\": \"DeepSeek\",\n@@ -35375,13 +56506,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"text-generation\",\n \"architecture\": \"deepseek_v4\",\n- \"hf_downloads\": 786631,\n- \"hf_likes\": 3602,\n+ \"hf_downloads\": 1167697,\n+ \"hf_likes\": 3780,\n \"release_date\": \"2026-04-22\",\n \"num_hidden_layers\": 61,\n \"num_attention_heads\": 128,\n \"num_key_value_heads\": 1,\n \"head_dim\": 512,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 129280,\n+ \"moe_intermediate_size\": 3072,\n+ \"shared_expert_intermediate_size\": null,\n \"is_moe\": true,\n \"num_experts\": 384,\n \"active_experts\": 6,\n@@ -35402,9 +56537,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"bailing_hybrid\",\n- \"hf_downloads\": 31310,\n+ \"hf_downloads\": 33493,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 80,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 128,\n+ \"hidden_size\": 8192,\n+ \"vocab_size\": 157184,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 256,\n@@ -35426,9 +56569,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"mimo_v2\",\n- \"hf_downloads\": 16030,\n+ \"hf_downloads\": 31447,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 70,\n+ \"num_attention_heads\": 128,\n+ \"num_key_value_heads\": 8,\n+ \"head_dim\": 192,\n+ \"hidden_size\": 6144,\n+ \"vocab_size\": 152576,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 384,\n@@ -35450,9 +56601,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"kimi_k25\",\n- \"hf_downloads\": 128730,\n+ \"hf_downloads\": 155785,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 61,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 112,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 163840,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 384,\n@@ -35474,13 +56633,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"text-generation\",\n \"architecture\": \"kimi_k2\",\n- \"hf_downloads\": 720892,\n- \"hf_likes\": 2356,\n+ \"hf_downloads\": 769312,\n+ \"hf_likes\": 2357,\n \"release_date\": \"2025-07-11\",\n \"num_hidden_layers\": 61,\n \"num_attention_heads\": 64,\n \"num_key_value_heads\": 64,\n \"head_dim\": 112,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 163840,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"is_moe\": true,\n \"num_experts\": 384,\n \"active_experts\": 8,\n@@ -35507,9 +56670,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"kimi_k2\",\n- \"hf_downloads\": 742257,\n+ \"hf_downloads\": 793288,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 61,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 112,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 163840,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 384,\n@@ -35531,9 +56702,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"kimi_k2\",\n- \"hf_downloads\": 156992,\n+ \"hf_downloads\": 156530,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 61,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 112,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 163840,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 384,\n@@ -35557,13 +56736,17 @@\n ],\n \"pipeline_tag\": \"image-text-to-text\",\n \"architecture\": \"kimi_k25\",\n- \"hf_downloads\": 1958899,\n- \"hf_likes\": 2775,\n+ \"hf_downloads\": 1895422,\n+ \"hf_likes\": 2777,\n \"release_date\": \"2026-01-01\",\n- \"num_hidden_layers\": null,\n- \"num_attention_heads\": null,\n- \"num_key_value_heads\": null,\n- \"head_dim\": null,\n+ \"num_hidden_layers\": 61,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 112,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 163840,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"is_moe\": true,\n \"num_experts\": 384,\n \"active_experts\": 8,\n@@ -35590,9 +56773,49 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"kimi_k25\",\n- \"hf_downloads\": 997278,\n+ \"hf_downloads\": 1239626,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": 61,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 112,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 163840,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true,\n+ \"is_moe\": true,\n+ \"num_experts\": 384,\n+ \"active_experts\": 8,\n+ \"active_parameters\": 73880719970\n+ },\n+ {\n+ \"name\": \"unsloth/Kimi-K2.6\",\n+ \"provider\": \"unsloth\",\n+ \"parameter_count\": \"1058.6B\",\n+ \"parameters_raw\": 1058589420528,\n+ \"min_ram_gb\": 591.5,\n+ \"recommended_ram_gb\": 985.9,\n+ \"min_vram_gb\": 542.2,\n+ \"quantization\": \"Q4_K_M\",\n+ \"format\": \"gguf\",\n+ \"context_length\": 16777216,\n+ \"use_case\": \"General purpose\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"kimi_k25\",\n+ \"hf_downloads\": 13540,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 61,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 112,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 163840,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 384,\n@@ -35614,13 +56837,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"deepseek_v4\",\n- \"hf_downloads\": 5033,\n- \"hf_likes\": 257,\n+ \"hf_downloads\": 11203,\n+ \"hf_likes\": 261,\n \"release_date\": \"2026-04-22\",\n \"num_hidden_layers\": 61,\n \"num_attention_heads\": 128,\n \"num_key_value_heads\": 1,\n \"head_dim\": 512,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 129280,\n+ \"moe_intermediate_size\": 3072,\n+ \"shared_expert_intermediate_size\": null,\n \"is_moe\": true,\n \"num_experts\": 384,\n \"active_experts\": 6,\ndiff --git a/llmfit-core/data/hf_models.json b/llmfit-core/data/hf_models.json\nindex c7945062..6070caa4 100644\n--- a/llmfit-core/data/hf_models.json\n+++ b/llmfit-core/data/hf_models.json\n@@ -1,4 +1,60 @@\n [\n+ {\n+ \"name\": \"brain-bzh/reve-positions\",\n+ \"provider\": \"brain-bzh\",\n+ \"parameter_count\": \"2K\",\n+ \"parameters_raw\": 1629,\n+ \"min_ram_gb\": 1.0,\n+ \"recommended_ram_gb\": 2.0,\n+ \"min_vram_gb\": 0.5,\n+ \"quantization\": \"Q4_K_M\",\n+ \"format\": \"gguf\",\n+ \"context_length\": 4096,\n+ \"use_case\": \"General purpose\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"reve-position-bank\",\n+ \"hf_downloads\": 34113,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": null,\n+ \"num_attention_heads\": null,\n+ \"num_key_value_heads\": null,\n+ \"head_dim\": null,\n+ \"hidden_size\": null,\n+ \"vocab_size\": null,\n+ \"moe_intermediate_size\": null,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true\n+ },\n+ {\n+ \"name\": \"optimum-intel-internal-testing/tiny-random-squeezebert\",\n+ \"provider\": \"optimum-intel-internal-testing\",\n+ \"parameter_count\": \"78K\",\n+ \"parameters_raw\": 78336,\n+ \"min_ram_gb\": 1.0,\n+ \"recommended_ram_gb\": 2.0,\n+ \"min_vram_gb\": 0.5,\n+ \"quantization\": \"Q4_K_M\",\n+ \"format\": \"gguf\",\n+ \"context_length\": 512,\n+ \"use_case\": \"Lightweight, edge deployment\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"squeezebert\",\n+ \"hf_downloads\": 39446,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": 5,\n+ \"num_attention_heads\": 4,\n+ 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null,\n+ \"moe_intermediate_size\": null,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true\n },\n {\n@@ -31661,9 +50454,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"qwen2_moe\",\n- \"hf_downloads\": 17313,\n+ \"hf_downloads\": 17941,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 28,\n+ \"num_attention_heads\": 28,\n+ \"num_key_value_heads\": 4,\n+ \"head_dim\": 128,\n+ \"hidden_size\": 3584,\n+ \"vocab_size\": 151936,\n+ \"moe_intermediate_size\": 2560,\n+ \"shared_expert_intermediate_size\": 20480,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 64,\n@@ -31671,26 +50472,36 @@\n \"active_parameters\": 9687711195\n },\n {\n- \"name\": \"unsloth/Llama-3.3-70B-Instruct-GGUF\",\n- \"provider\": \"unsloth\",\n- \"parameter_count\": \"60.1B\",\n- \"parameters_raw\": 60106473472,\n- \"min_ram_gb\": 33.6,\n- \"recommended_ram_gb\": 56.0,\n- \"min_vram_gb\": 30.8,\n+ \"name\": 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\"is_moe\": true,\n+ \"num_experts\": 64,\n+ \"active_experts\": 8,\n+ \"active_parameters\": 9687711195\n },\n {\n \"name\": \"hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4\",\n@@ -31709,14 +50520,22 @@\n ],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"llama\",\n- \"hf_downloads\": 211997,\n+ \"hf_downloads\": 223918,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 80,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 8,\n+ \"head_dim\": 128,\n+ \"hidden_size\": 8192,\n+ \"vocab_size\": 128256,\n+ \"moe_intermediate_size\": 28672,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true\n },\n {\n- \"name\": \"deepseek-ai/DeepSeek-R1-Distill-Llama-70B\",\n- \"provider\": \"DeepSeek\",\n+ \"name\": \"unsloth/Llama-3.3-70B-Instruct-GGUF\",\n+ \"provider\": \"unsloth\",\n \"parameter_count\": \"60.1B\",\n \"parameters_raw\": 60106473472,\n \"min_ram_gb\": 33.6,\n@@ -31725,13 +50544,23 @@\n \"quantization\": \"Q4_K_M\",\n 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\"head_dim\": 64,\n+ \"hidden_size\": 6144,\n+ \"vocab_size\": 154880,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true,\n+ \"is_moe\": true,\n+ \"num_experts\": 256,\n+ \"active_experts\": 8,\n+ \"active_parameters\": 59280425217\n+ },\n {\n \"name\": \"zai-org/GLM-5\",\n \"provider\": \"zai-org\",\n@@ -35270,13 +56341,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"text-generation\",\n \"architecture\": \"glm_moe_dsa\",\n- \"hf_downloads\": 315464,\n- \"hf_likes\": 2082,\n+ \"hf_downloads\": 308513,\n+ \"hf_likes\": 2084,\n \"release_date\": \"2026-02-11\",\n \"num_hidden_layers\": 78,\n \"num_attention_heads\": 64,\n \"num_key_value_heads\": 64,\n \"head_dim\": 64,\n+ \"hidden_size\": 6144,\n+ \"vocab_size\": 154880,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"is_moe\": true,\n \"num_experts\": 256,\n \"active_experts\": 8,\n@@ -35303,9 +56378,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"glm_moe_dsa\",\n- \"hf_downloads\": 295110,\n+ \"hf_downloads\": 292743,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 78,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 64,\n+ \"hidden_size\": 6144,\n+ \"vocab_size\": 154880,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 256,\n@@ -35327,9 +56410,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"glm_moe_dsa\",\n- \"hf_downloads\": 1609767,\n+ \"hf_downloads\": 1148578,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 78,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 64,\n+ \"hidden_size\": 6144,\n+ \"vocab_size\": 154880,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 256,\n@@ -35351,15 +56442,55 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"glm_moe_dsa\",\n- \"hf_downloads\": 877686,\n+ \"hf_downloads\": 934979,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 78,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 64,\n+ \"hidden_size\": 6144,\n+ \"vocab_size\": 154880,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 256,\n \"active_experts\": 8,\n \"active_parameters\": 60077205033\n },\n+ {\n+ \"name\": \"cyankiwi/GLM-5.1-AWQ-4bit\",\n+ \"provider\": \"cyankiwi\",\n+ \"parameter_count\": \"766.9B\",\n+ \"parameters_raw\": 766909554882,\n+ \"min_ram_gb\": 428.5,\n+ \"recommended_ram_gb\": 714.2,\n+ \"min_vram_gb\": 392.8,\n+ \"quantization\": \"AWQ-4bit\",\n+ \"format\": \"awq\",\n+ \"context_length\": 202752,\n+ \"use_case\": \"General purpose\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"glm_moe_dsa\",\n+ \"hf_downloads\": 14401,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": 78,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 64,\n+ \"hidden_size\": 6144,\n+ \"vocab_size\": 154880,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true,\n+ \"is_moe\": true,\n+ \"num_experts\": 256,\n+ \"active_experts\": 8,\n+ \"active_parameters\": 61113105152\n+ },\n {\n \"name\": \"deepseek-ai/DeepSeek-V4-Pro\",\n \"provider\": \"DeepSeek\",\n@@ -35375,13 +56506,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"text-generation\",\n \"architecture\": \"deepseek_v4\",\n- \"hf_downloads\": 786631,\n- \"hf_likes\": 3602,\n+ \"hf_downloads\": 1167697,\n+ \"hf_likes\": 3780,\n \"release_date\": \"2026-04-22\",\n \"num_hidden_layers\": 61,\n \"num_attention_heads\": 128,\n \"num_key_value_heads\": 1,\n \"head_dim\": 512,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 129280,\n+ \"moe_intermediate_size\": 3072,\n+ \"shared_expert_intermediate_size\": null,\n \"is_moe\": true,\n \"num_experts\": 384,\n \"active_experts\": 6,\n@@ -35402,9 +56537,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"bailing_hybrid\",\n- \"hf_downloads\": 31310,\n+ \"hf_downloads\": 33493,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 80,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 128,\n+ \"hidden_size\": 8192,\n+ \"vocab_size\": 157184,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 256,\n@@ -35426,9 +56569,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"mimo_v2\",\n- \"hf_downloads\": 16030,\n+ \"hf_downloads\": 31447,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 70,\n+ \"num_attention_heads\": 128,\n+ \"num_key_value_heads\": 8,\n+ \"head_dim\": 192,\n+ \"hidden_size\": 6144,\n+ \"vocab_size\": 152576,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 384,\n@@ -35450,9 +56601,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"kimi_k25\",\n- \"hf_downloads\": 128730,\n+ \"hf_downloads\": 155785,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 61,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 112,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 163840,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 384,\n@@ -35474,13 +56633,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"text-generation\",\n \"architecture\": \"kimi_k2\",\n- \"hf_downloads\": 720892,\n- \"hf_likes\": 2356,\n+ \"hf_downloads\": 769312,\n+ \"hf_likes\": 2357,\n \"release_date\": \"2025-07-11\",\n \"num_hidden_layers\": 61,\n \"num_attention_heads\": 64,\n \"num_key_value_heads\": 64,\n \"head_dim\": 112,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 163840,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"is_moe\": true,\n \"num_experts\": 384,\n \"active_experts\": 8,\n@@ -35507,9 +56670,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"kimi_k2\",\n- \"hf_downloads\": 742257,\n+ \"hf_downloads\": 793288,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 61,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 112,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 163840,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 384,\n@@ -35531,9 +56702,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"kimi_k2\",\n- \"hf_downloads\": 156992,\n+ \"hf_downloads\": 156530,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 61,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 112,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 163840,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 384,\n@@ -35557,13 +56736,17 @@\n ],\n \"pipeline_tag\": \"image-text-to-text\",\n \"architecture\": \"kimi_k25\",\n- \"hf_downloads\": 1958899,\n- \"hf_likes\": 2775,\n+ \"hf_downloads\": 1895422,\n+ \"hf_likes\": 2777,\n \"release_date\": \"2026-01-01\",\n- \"num_hidden_layers\": null,\n- \"num_attention_heads\": null,\n- \"num_key_value_heads\": null,\n- \"head_dim\": null,\n+ \"num_hidden_layers\": 61,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 112,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 163840,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"is_moe\": true,\n \"num_experts\": 384,\n \"active_experts\": 8,\n@@ -35590,9 +56773,49 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"kimi_k25\",\n- \"hf_downloads\": 997278,\n+ \"hf_downloads\": 1239626,\n+ \"hf_likes\": 0,\n+ \"release_date\": null,\n+ \"num_hidden_layers\": 61,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 112,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 163840,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n+ \"_discovered\": true,\n+ \"is_moe\": true,\n+ \"num_experts\": 384,\n+ \"active_experts\": 8,\n+ \"active_parameters\": 73880719970\n+ },\n+ {\n+ \"name\": \"unsloth/Kimi-K2.6\",\n+ \"provider\": \"unsloth\",\n+ \"parameter_count\": \"1058.6B\",\n+ \"parameters_raw\": 1058589420528,\n+ \"min_ram_gb\": 591.5,\n+ \"recommended_ram_gb\": 985.9,\n+ \"min_vram_gb\": 542.2,\n+ \"quantization\": \"Q4_K_M\",\n+ \"format\": \"gguf\",\n+ \"context_length\": 16777216,\n+ \"use_case\": \"General purpose\",\n+ \"capabilities\": [],\n+ \"pipeline_tag\": \"unknown\",\n+ \"architecture\": \"kimi_k25\",\n+ \"hf_downloads\": 13540,\n \"hf_likes\": 0,\n \"release_date\": null,\n+ \"num_hidden_layers\": 61,\n+ \"num_attention_heads\": 64,\n+ \"num_key_value_heads\": 64,\n+ \"head_dim\": 112,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 163840,\n+ \"moe_intermediate_size\": 2048,\n+ \"shared_expert_intermediate_size\": null,\n \"_discovered\": true,\n \"is_moe\": true,\n \"num_experts\": 384,\n@@ -35614,13 +56837,17 @@\n \"capabilities\": [],\n \"pipeline_tag\": \"unknown\",\n \"architecture\": \"deepseek_v4\",\n- \"hf_downloads\": 5033,\n- \"hf_likes\": 257,\n+ \"hf_downloads\": 11203,\n+ \"hf_likes\": 261,\n \"release_date\": \"2026-04-22\",\n \"num_hidden_layers\": 61,\n \"num_attention_heads\": 128,\n \"num_key_value_heads\": 1,\n \"head_dim\": 512,\n+ \"hidden_size\": 7168,\n+ \"vocab_size\": 129280,\n+ \"moe_intermediate_size\": 3072,\n+ \"shared_expert_intermediate_size\": null,\n \"is_moe\": true,\n \"num_experts\": 384,\n \"active_experts\": 6,\ndiff --git a/llmfit-core/src/models.rs b/llmfit-core/src/models.rs\nindex 02f9925e..2cb25424 100644\n--- a/llmfit-core/src/models.rs\n+++ b/llmfit-core/src/models.rs\n@@ -776,6 +776,14 @@ struct HfModelEntry {\n #[serde(default)]\n head_dim: Option<u32>,\n #[serde(default)]\n+ hidden_size: Option<u32>,\n+ #[serde(default)]\n+ vocab_size: Option<u32>,\n+ #[serde(default)]\n+ moe_intermediate_size: Option<u32>,\n+ #[serde(default)]\n+ shared_expert_intermediate_size: Option<u32>,\n+ #[serde(default)]\n license: Option<String>,\n }\n \n@@ -927,10 +935,10 @@ fn load_embedded() -> Vec<LlmModel> {\n num_hidden_layers: e.num_hidden_layers,\n head_dim: e.head_dim,\n attention_layout: None,\n- hidden_size: None,\n- moe_intermediate_size: None,\n- vocab_size: None,\n- shared_expert_intermediate_size: None,\n+ hidden_size: e.hidden_size,\n+ moe_intermediate_size: e.moe_intermediate_size,\n+ vocab_size: e.vocab_size,\n+ shared_expert_intermediate_size: e.shared_expert_intermediate_size,\n license: e.license,\n };\n model.capabilities = Capability::infer(&model);\n@@ -1832,6 +1840,10 @@ mod tests {\n num_key_value_heads: None,\n num_hidden_layers: Some(48),\n head_dim: None,\n+ hidden_size: None,\n+ vocab_size: None,\n+ moe_intermediate_size: None,\n+ shared_expert_intermediate_size: None,\n license: Some(\"apache-2.0\".to_string()),\n },\n // Entry 2: higher params, higher context, ToolUse capability, MoE\n@@ -1863,6 +1875,10 @@ mod tests {\n num_key_value_heads: Some(8),\n num_hidden_layers: None,\n head_dim: Some(128),\n+ hidden_size: None,\n+ vocab_size: None,\n+ moe_intermediate_size: None,\n+ shared_expert_intermediate_size: None,\n license: None,\n },\n ]);\ndiff --git a/scripts/scrape_hf_models.py b/scripts/scrape_hf_models.py\nindex 9dba325a..717941d7 100644\n--- a/scripts/scrape_hf_models.py\n+++ b/scripts/scrape_hf_models.py\n@@ -398,6 +398,74 @@ def estimate_vram(total_params: int, quant: str) -> float:\n return round(max(vram_gb, 0.5), 1)\n \n \n+def extract_arch_metadata(config: dict | None) -> dict:\n+ \"\"\"Extract architecture fields for precise KV cache and MoE speed estimation.\n+\n+ Checks top-level config and falls back to ``text_config`` for multimodal\n+ models (e.g. Llama 4 Scout, Qwen-VL). Returns a dict with architecture\n+ fields (any may be ``None``).\n+ \"\"\"\n+ cfg = config or {}\n+ # Try top-level first, then text_config for multimodal wrappers.\n+ sources = [cfg]\n+ if isinstance(cfg.get(\"text_config\"), dict):\n+ sources.append(cfg[\"text_config\"])\n+\n+ num_hidden_layers = None\n+ num_attention_heads = None\n+ num_key_value_heads = None\n+ head_dim = None\n+ hidden_size = None\n+ vocab_size = None\n+ moe_intermediate_size = None\n+ shared_expert_intermediate_size = None\n+\n+ for src in sources:\n+ if num_hidden_layers is None:\n+ num_hidden_layers = src.get(\"num_hidden_layers\")\n+ if num_attention_heads is None:\n+ num_attention_heads = src.get(\"num_attention_heads\")\n+ if num_key_value_heads is None:\n+ num_key_value_heads = src.get(\"num_key_value_heads\")\n+ if head_dim is None:\n+ head_dim = src.get(\"head_dim\")\n+ if hidden_size is None:\n+ hidden_size = src.get(\"hidden_size\")\n+ if head_dim is None and num_attention_heads and hidden_size:\n+ head_dim = hidden_size // num_attention_heads\n+ if vocab_size is None:\n+ vocab_size = src.get(\"vocab_size\")\n+ if moe_intermediate_size is None:\n+ # Prefer explicit moe_intermediate_size (Qwen, DeepSeek), fall\n+ # back to intermediate_size which is the per-expert FFN dim in\n+ # Mixtral-style MoE models that don't use a separate key.\n+ v = src.get(\"moe_intermediate_size\") or src.get(\"intermediate_size\")\n+ # Some models (e.g. ERNIE-4.5-VL) use a list; take first element.\n+ if isinstance(v, list):\n+ v = v[0] if v else None\n+ moe_intermediate_size = v\n+ if shared_expert_intermediate_size is None:\n+ v = src.get(\"shared_expert_intermediate_size\")\n+ if isinstance(v, list):\n+ v = v[0] if v else None\n+ shared_expert_intermediate_size = v\n+\n+ # GQA default: if num_key_value_heads missing, assume MHA\n+ if num_key_value_heads is None:\n+ num_key_value_heads = num_attention_heads\n+\n+ return {\n+ \"num_hidden_layers\": num_hidden_layers,\n+ \"num_attention_heads\": num_attention_heads,\n+ \"num_key_value_heads\": num_key_value_heads,\n+ \"head_dim\": head_dim,\n+ \"hidden_size\": hidden_size,\n+ \"vocab_size\": vocab_size,\n+ \"moe_intermediate_size\": moe_intermediate_size,\n+ \"shared_expert_intermediate_size\": shared_expert_intermediate_size,\n+ }\n+\n+\n def detect_moe(repo_id: str, config: dict | None, architecture: str,\n total_params: int) -> dict:\n \"\"\"Detect MoE architecture and compute active parameters.\"\"\"\n@@ -455,6 +523,62 @@ def estimate_active_params(total_params: int, num_experts: int,\n return shared + active_experts * per_expert\n \n \n+def estimate_params_from_arch(config: dict | None) -> int | None:\n+ \"\"\"Estimate total parameter count from architecture metadata.\n+\n+ Uses the transformer parameter formula accounting for MoE expert weights.\n+ Returns None if insufficient metadata is available.\n+ \"\"\"\n+ cfg = config or {}\n+ # Check text_config for multimodal wrappers\n+ for src in [cfg, cfg.get(\"text_config\", {})]:\n+ hidden = src.get(\"hidden_size\")\n+ layers = src.get(\"num_hidden_layers\")\n+ vocab = src.get(\"vocab_size\")\n+ if hidden and layers and vocab:\n+ break\n+ else:\n+ return None\n+\n+ n_heads = src.get(\"num_attention_heads\") or 1\n+ n_kv = src.get(\"num_key_value_heads\") or n_heads\n+ head_dim = src.get(\"head_dim\") or (hidden // n_heads if n_heads else hidden)\n+\n+ # Attention: Q + K + V + O projections per layer\n+ attn = 2 * n_heads * head_dim * hidden + 2 * n_kv * head_dim * hidden\n+\n+ # FFN / expert weights\n+ def _scalar(v, default=None):\n+ \"\"\"Coerce list values (e.g. ERNIE-4.5-VL) to a single int.\"\"\"\n+ if isinstance(v, list):\n+ return v[0] if v else default\n+ return v if v is not None else default\n+\n+ num_experts = src.get(\"num_local_experts\") or src.get(\"num_experts\")\n+ moe_inter = _scalar(src.get(\"moe_intermediate_size\"))\n+ shared_inter = _scalar(src.get(\"shared_expert_intermediate_size\"), 0)\n+ intermediate = _scalar(src.get(\"intermediate_size\"))\n+\n+ if num_experts and moe_inter:\n+ # MoE: per-expert FFN + shared expert\n+ expert_ffn = num_experts * 3 * hidden * moe_inter\n+ shared_ffn = 3 * hidden * shared_inter if shared_inter else 0\n+ router = num_experts * hidden\n+ ffn_total = expert_ffn + shared_ffn + router\n+ elif intermediate:\n+ # Dense: standard SwiGLU FFN (gate + up + down)\n+ ffn_total = 3 * hidden * intermediate\n+ else:\n+ # Fallback: assume 4x hidden\n+ ffn_total = 4 * hidden * hidden\n+\n+ per_layer = attn + ffn_total\n+ embedding = 2 * vocab * hidden # embedding + lm_head\n+\n+ total = layers * per_layer + embedding\n+ return total if total > 1_000_000 else None\n+\n+\n def infer_use_case(repo_id: str, pipeline_tag: str | None, config: dict | None) -> str:\n \"\"\"Infer a brief use-case description from model metadata.\"\"\"\n rid = repo_id.lower()\n@@ -705,6 +829,12 @@ def scrape_model(repo_id: str) -> dict | None:\n model_format, default_quant = detect_quant_format(repo_id, full_config)\n context_length = infer_context_length(full_config) if full_config else infer_context_length(config)\n \n+ # Correct parameters_raw when safetensors reports quantized element counts\n+ # instead of true parameter count (common in FP8/INT4/INT8 repos).\n+ arch_params = estimate_params_from_arch(full_config)\n+ if arch_params and arch_params > total_params * 2:\n+ total_params = arch_params\n+\n min_ram, rec_ram = estimate_ram(total_params, default_quant)\n min_vram = estimate_vram(total_params, default_quant)\n \n@@ -717,14 +847,7 @@ def scrape_model(repo_id: str) -> dict | None:\n \n # Architecture metadata for the precise KV cache formula. All optional;\n # absent fields cause the Rust side to fall back to the linear approx.\n- num_hidden_layers = (full_config or {}).get(\"num_hidden_layers\")\n- num_attention_heads = (full_config or {}).get(\"num_attention_heads\")\n- num_key_value_heads = (full_config or {}).get(\"num_key_value_heads\") or num_attention_heads\n- head_dim = (full_config or {}).get(\"head_dim\")\n- if head_dim is None and num_attention_heads:\n- hidden_size = (full_config or {}).get(\"hidden_size\")\n- if hidden_size:\n- head_dim = hidden_size // num_attention_heads\n+ arch_meta = extract_arch_metadata(full_config)\n \n result = {\n \"name\": repo_id,\n@@ -744,10 +867,7 @@ def scrape_model(repo_id: str) -> dict | None:\n \"hf_downloads\": info.get(\"downloads\", 0),\n \"hf_likes\": info.get(\"likes\", 0),\n \"release_date\": (info.get(\"createdAt\") or \"\")[:10] or None,\n- \"num_hidden_layers\": num_hidden_layers,\n- \"num_attention_heads\": num_attention_heads,\n- \"num_key_value_heads\": num_key_value_heads,\n- \"head_dim\": head_dim,\n+ **arch_meta,\n }\n \n # Add MoE fields if detected\n@@ -1269,6 +1389,11 @@ def _build_discovered_model(listing: dict) -> dict | None:\n context_length = (infer_context_length(full_config) if full_config\n else infer_context_length(config))\n \n+ # Correct parameters_raw when safetensors reports quantized element counts\n+ arch_params = estimate_params_from_arch(full_config)\n+ if arch_params and arch_params > total_params * 2:\n+ total_params = arch_params\n+\n min_ram, rec_ram = estimate_ram(total_params, default_quant)\n min_vram = estimate_vram(total_params, default_quant)\n \n@@ -1276,6 +1401,9 @@ def _build_discovered_model(listing: dict) -> dict | None:\n moe_info = detect_moe(repo_id, full_config, architecture, total_params)\n use_case_str = infer_use_case(repo_id, pipeline_tag, config)\n \n+ # Architecture metadata for the precise KV cache formula.\n+ arch_meta = extract_arch_metadata(full_config)\n+\n model = {\n \"name\": repo_id,\n \"provider\": extract_provider(repo_id),\n@@ -1294,6 +1422,7 @@ def _build_discovered_model(listing: dict) -> dict | None:\n \"hf_downloads\": listing.get(\"downloads\", 0),\n \"hf_likes\": listing.get(\"likes\", 0),\n \"release_date\": (listing.get(\"createdAt\") or \"\")[:10] or None,\n+ **arch_meta,\n \"_discovered\": True,\n }\n \n"},"changed_files":{"kind":"list like","value":["data/hf_models.json","llmfit-core/data/hf_models.json","llmfit-core/src/models.rs","scripts/scrape_hf_models.py"],"string":"[\n \"data/hf_models.json\",\n \"llmfit-core/data/hf_models.json\",\n \"llmfit-core/src/models.rs\",\n \"scripts/scrape_hf_models.py\"\n]","isConversational":false},"review_threads":{"kind":"list like","value":[],"string":"[]","isConversational":false},"test_patch":{"kind":"string","value":""},"merged":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":498,"cells":{"repo":{"kind":"string","value":"AlexsJones/llmfit"},"pr_number":{"kind":"number","value":536,"string":"536"},"task_type":{"kind":"string","value":"issue_to_patch"},"issue_text":{"kind":"string","value":""},"pr_title":{"kind":"string","value":"chore(main): release 0.9.22"},"pr_body":{"kind":"string","value":":robot: I have created a release *beep* *boop*\n---\n\n\n## [0.9.22](https://github.com/AlexsJones/llmfit/compare/v0.9.21...v0.9.22) (2026-05-06)\n\n\n### Features\n\n* overhaul model discovery with cursor pagination, trending, and additive DB ([#537](https://github.com/AlexsJones/llmfit/issues/537)) ([93e9d67](https://github.com/AlexsJones/llmfit/commit/93e9d673cf54bde62dc7b24a1400c1cd89f70c1c))\n\n---\nThis PR was generated with [Release Please](https://github.com/googleapis/release-please). See [documentation](https://github.com/googleapis/release-please#release-please)."},"base_sha":{"kind":"string","value":"93e9d673cf54bde62dc7b24a1400c1cd89f70c1c"},"head_sha":{"kind":"string","value":"ddbd10c8204b416bb2db2f64c39c5c529d0227ee"},"gold_diff":{"kind":"string","value":"diff --git a/.release-please-manifest.json b/.release-please-manifest.json\nindex 507bde5f..76d14cdd 100644\n--- a/.release-please-manifest.json\n+++ b/.release-please-manifest.json\n@@ -1,3 +1,3 @@\n {\n- \".\": \"0.9.21\"\n+ \".\": \"0.9.22\"\n }\ndiff --git a/CHANGELOG.md b/CHANGELOG.md\nindex 87dad45d..4aabf8b7 100644\n--- a/CHANGELOG.md\n+++ b/CHANGELOG.md\n@@ -1,5 +1,12 @@\n # Changelog\n \n+## [0.9.22](https://github.com/AlexsJones/llmfit/compare/v0.9.21...v0.9.22) (2026-05-06)\n+\n+\n+### Features\n+\n+* overhaul model discovery with cursor pagination, trending, and additive DB ([#537](https://github.com/AlexsJones/llmfit/issues/537)) ([93e9d67](https://github.com/AlexsJones/llmfit/commit/93e9d673cf54bde62dc7b24a1400c1cd89f70c1c))\n+\n ## [0.9.21](https://github.com/AlexsJones/llmfit/compare/v0.9.20...v0.9.21) (2026-05-05)\n \n \ndiff --git a/Cargo.lock b/Cargo.lock\nindex 4957dfed..f3a44abd 100644\n--- a/Cargo.lock\n+++ b/Cargo.lock\n@@ -2361,7 +2361,7 @@ checksum = \"11d3d7f243d5c5a8b9bb5d6dd2b1602c0cb0b9db1621bafc7ed66e35ff9fe092\"\n \n [[package]]\n name = \"llmfit\"\n-version = \"0.9.21\"\n+version = \"0.9.22\"\n dependencies = [\n \"arboard\",\n \"assert_cmd\",\n@@ -2383,7 +2383,7 @@ dependencies = [\n \n [[package]]\n name = \"llmfit-core\"\n-version = \"0.9.21\"\n+version = \"0.9.22\"\n dependencies = [\n \"dirs\",\n \"http\",\n@@ -2398,7 +2398,7 @@ dependencies = [\n \n [[package]]\n name = \"llmfit-desktop\"\n-version = \"0.9.21\"\n+version = \"0.9.22\"\n dependencies = [\n \"llmfit-core\",\n \"serde\",\ndiff --git a/Cargo.toml b/Cargo.toml\nindex 914d62fe..e1a37976 100644\n--- a/Cargo.toml\n+++ b/Cargo.toml\n@@ -4,5 +4,5 @@ default-members = [\"llmfit-core\", \"llmfit-tui\"]\n resolver = \"3\"\n \n [workspace.package]\n-version = \"0.9.21\"\n+version = \"0.9.22\"\n license = \"MIT\"\ndiff --git a/version.txt b/version.txt\nindex b2160230..ea3f0d7a 100644\n--- a/version.txt\n+++ b/version.txt\n@@ -1,1 +1,1 @@\n-0.9.21\n+0.9.22\n"},"changed_files":{"kind":"list like","value":[".release-please-manifest.json","CHANGELOG.md","Cargo.lock","Cargo.toml","version.txt"],"string":"[\n \".release-please-manifest.json\",\n \"CHANGELOG.md\",\n \"Cargo.lock\",\n \"Cargo.toml\",\n \"version.txt\"\n]","isConversational":false},"review_threads":{"kind":"list like","value":[],"string":"[]","isConversational":false},"test_patch":{"kind":"string","value":""},"merged":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":499,"cells":{"repo":{"kind":"string","value":"Anankke/SSPanel-UIM"},"pr_number":{"kind":"number","value":2552,"string":"2,552"},"task_type":{"kind":"string","value":"issue_to_patch"},"issue_text":{"kind":"string","value":""},"pr_title":{"kind":"string","value":"for sing-box 1.14.0+"},"pr_body":{"kind":"string","value":""},"base_sha":{"kind":"string","value":"c3ea6113c58d946fc3442cdadc13d30b681cf171"},"head_sha":{"kind":"string","value":"bbc388544530e2aaaabf5ccea1d342ec19963b66"},"gold_diff":{"kind":"string","value":"diff --git a/config/appprofile.example.php b/config/appprofile.example.php\nindex f07527dc46..e57a5e24fa 100644\n--- a/config/appprofile.example.php\n+++ b/config/appprofile.example.php\n@@ -61,6 +61,7 @@\n 'type' => 'quic',\n 'server' => '223.6.6.6',\n 'server_port' => 853,\n+ //'domain_resolver' => 'local',\n //'detour' => 'direct',\n ],\n [\n@@ -68,6 +69,7 @@\n 'type' => 'tls',\n 'server' => '1.1.1.1',\n 'server_port' => 853,\n+ //'domain_resolver' => 'local',\n 'detour' => 'select',\n ],\n [\n@@ -75,6 +77,7 @@\n 'type' => 'tls',\n 'server' => '8.8.8.8',\n 'server_port' => 853,\n+ //'domain_resolver' => 'local',\n 'detour' => 'select',\n ],\n [\n@@ -82,6 +85,7 @@\n 'type' => 'tls',\n 'server' => '208.67.222.222',\n 'server_port' => 853,\n+ //'domain_resolver' => 'local',\n 'detour' => 'select',\n ],\n [\n@@ -137,6 +141,20 @@\n ],\n 'server' => 'fakeip',\n ],\n+ [\n+ 'action' => 'evaluate',\n+ 'server' => $_ENV['dns_select'],\n+ 'disable_cache' => true,\n+ 'client_subnet' => '111.222.0.0'\n+ ],\n+ [\n+ 'rule_set' => [\n+ 'geosite-geolocation-!cn',\n+ ],\n+ 'server' => $_ENV['dns_select'],\n+ 'disable_cache' => true,\n+ 'client_subnet' => '111.222.0.0'\n+ ],\n [\n 'type' => 'logical',\n 'mode' => 'and',\n@@ -148,6 +166,7 @@\n 'invert' => true,\n ],\n [\n+ 'match_response' => true,\n 'rule_set' => [\n 'geoip-cn',\n ],\n@@ -169,7 +188,7 @@\n ],\n 'final' => 'cloudflare',\n 'disable_cache' => true,\n- 'independent_cache' => true,\n+ //'independent_cache' => true,\n ],\n 'inbounds' => [\n [\n@@ -205,17 +224,6 @@\n 'idle_timeout' => '30m',\n 'interrupt_exist_connections' => true,\n ],\n- [\n- 'tag' => 'rules_download',\n- 'type' => 'selector',\n- 'outbounds' => [\n- 'select',\n- 'auto',\n- 'direct',\n- ],\n- 'default' => 'select',\n- 'interrupt_exist_connections' => true,\n- ],\n [\n 'type' => 'direct',\n 'tag' => 'direct',\n@@ -338,56 +346,84 @@\n 'type' => 'remote',\n 'format' => 'binary',\n 'url' => 'https://' . $_ENV['jsdelivr_url'] . '/gh/SagerNet/sing-geoip@rule-set/geoip-cn.srs',\n- 'download_detour' => 'rules_download',\n 'update_interval' => '1d',\n+ 'http_client' => [\n+ 'engine' => 'go',\n+ 'version' => 2,\n+ 'disable_version_fallback' => false,\n+ ],\n ],\n [\n 'tag' => 'geosite-cn',\n 'type' => 'remote',\n 'format' => 'binary',\n 'url' => 'https://' . $_ENV['jsdelivr_url'] . '/gh/SagerNet/sing-geosite@rule-set/geosite-cn.srs',\n- 'download_detour' => 'rules_download',\n 'update_interval' => '1d',\n+ 'http_client' => [\n+ 'engine' => 'go',\n+ 'version' => 2,\n+ 'disable_version_fallback' => false,\n+ ],\n ],\n [\n 'tag' => 'geosite-category-ads-all',\n 'type' => 'remote',\n 'format' => 'binary',\n 'url' => 'https://' . $_ENV['jsdelivr_url'] . '/gh/SagerNet/sing-geosite@rule-set/geosite-category-ads-all.srs',\n- 'download_detour' => 'rules_download',\n 'update_interval' => '1d',\n+ 'http_client' => [\n+ 'engine' => 'go',\n+ 'version' => 2,\n+ 'disable_version_fallback' => false,\n+ ],\n ],\n [\n 'tag' => 'geosite-geolocation-cn',\n 'type' => 'remote',\n 'format' => 'binary',\n 'url' => 'https://' . $_ENV['jsdelivr_url'] . '/gh/SagerNet/sing-geosite@rule-set/geosite-geolocation-cn.srs',\n- 'download_detour' => 'rules_download',\n 'update_interval' => '1d',\n+ 'http_client' => [\n+ 'engine' => 'go',\n+ 'version' => 2,\n+ 'disable_version_fallback' => false,\n+ ],\n ],\n [\n 'tag' => 'geosite-geolocation-!cn',\n 'type' => 'remote',\n 'format' => 'binary',\n 'url' => 'https://' . $_ENV['jsdelivr_url'] . '/gh/SagerNet/sing-geosite@rule-set/geosite-geolocation-!cn.srs',\n- 'download_detour' => 'rules_download',\n 'update_interval' => '1d',\n+ 'http_client' => [\n+ 'engine' => 'go',\n+ 'version' => 2,\n+ 'disable_version_fallback' => false,\n+ ],\n ],\n [\n 'tag' => 'geosite-netease',\n 'type' => 'remote',\n 'format' => 'binary',\n 'url' => 'https://' . $_ENV['jsdelivr_url'] . '/gh/SagerNet/sing-geosite@rule-set/geosite-netease.srs',\n- 'download_detour' => 'rules_download',\n 'update_interval' => '1d',\n+ 'http_client' => [\n+ 'engine' => 'go',\n+ 'version' => 2,\n+ 'disable_version_fallback' => false,\n+ ],\n ],\n [\n 'tag' => 'geosite-bilibili',\n 'type' => 'remote',\n 'format' => 'binary',\n 'url' => 'https://' . $_ENV['jsdelivr_url'] . '/gh/SagerNet/sing-geosite@rule-set/geosite-bilibili.srs',\n- 'download_detour' => 'rules_download',\n 'update_interval' => '1d',\n+ 'http_client' => [\n+ 'engine' => 'go',\n+ 'version' => 2,\n+ 'disable_version_fallback' => false,\n+ ],\n ],\n ],\n 'final' => 'select',\ndiff --git a/update.sh b/update.sh\nindex ae0b97c7c4..79c38eb82d 100644\n--- a/update.sh\n+++ b/update.sh\n@@ -3,6 +3,7 @@\n cat << \"EOF\"\n Usage:\n ./update.sh dev --> Upgrade to the latest development version\n+./update.sh master --> Upgrade to the latest master version\n ./update.sh release $release_version $db_version --> Upgrade to the release version with the specified database version\n ./update.sh release-nogit --> Upgrade to the current release version without git(You will need to manually download the latest release version)\n EOF\n@@ -19,6 +20,18 @@ do_update_sspanel_dev(){\n php xcat Migration latest\n }\n \n+do_update_sspanel_master(){\n+ git pull origin master\n+ git reset --hard origin/master\n+ git fetch --prune --prune-tags\n+ rm -r storage/framework/smarty/compile/*\n+ php composer.phar install --no-dev\n+ php composer.phar selfupdate\n+ php xcat Update\n+ php xcat Tool importSetting\n+ php xcat Migration latest\n+}\n+\n do_update_sspanel_release(){\n tag=$1\n db_version=$2\n@@ -46,6 +59,11 @@ if [[ $1 == \"dev\" ]]; then\n exit 0\n fi\n \n+if [[ $1 == \"master\" ]]; then\n+ do_update_sspanel_master\n+ exit 0\n+fi\n+\n if [[ $1 == \"release\" ]]; then\n if [[ $2 == \"\" ]]; then\n echo \"Error: The release version cannot be empty!\"\n"},"changed_files":{"kind":"list like","value":["config/appprofile.example.php","update.sh"],"string":"[\n \"config/appprofile.example.php\",\n \"update.sh\"\n]","isConversational":false},"review_threads":{"kind":"list like","value":[],"string":"[]","isConversational":false},"test_patch":{"kind":"string","value":""},"merged":{"kind":"bool","value":true,"string":"true"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":4,"numItemsPerPage":100,"numTotalItems":87912,"offset":400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6Im1sR1pFYktIVHRVSGRhX1RRdEczQ0N1S3k5ME9Qd25DNHY5elRjM3FVYzgifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4MzMzNTc5OCwianRpIjoiZDExODQwNjUtMjA3OS00ZjFhLWJiY2UtMzYyYTdjNzEzY2FmIiwic3ViIjoiL2RhdGFzZXRzL1BvdGF0b0hEL2dpdGh1Yi1wci1yZXNvbHZlIiwiZXhwIjoxNzgzMzM5Mzk4LCJpc3MiOiJodHRwczovL2h1Z2dpbmdmYWNlLmNvIn0.rqE89mfC04l1VRRnNRs3gCD9ypfXXH3qTnDfi7O_x_M8dJL_MC3uq0dE4v4z2_Uk8Y3ocgBYl28qJ6enNVWqAA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":87912,"numBytesParquet":1412740926}]},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
repo stringlengths 5 53 | pr_number int32 1 321k | task_type stringclasses 2
values | issue_text stringlengths 0 81.2k | pr_title stringlengths 1 319 | pr_body stringlengths 0 105k | base_sha stringlengths 40 40 | head_sha stringlengths 40 40 | gold_diff stringlengths 0 202M | changed_files listlengths 0 100 | review_threads listlengths 0 100 | test_patch stringlengths 0 23.4M | merged bool 1
class |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Alexays/Waybar | 5,007 | issue_to_patch | Various toctou fixes | There are several [toctou](https://en.wikipedia.org/wiki/Time-of-check_to_time-of-use) bugs, especially in `battery.cpp`. Waybar checks if a file exists, _then after that_ opens it. If the file disappears in the time between the check and the open/read, Waybar will crash. This fixes many of those bugs by removing the `... | cca8dc38b6ed25ef78920669c5653748f31e4ba6 | 5af324f375edf8a6a033869d5386e711b4e49f10 | diff --git a/src/AAppIconLabel.cpp b/src/AAppIconLabel.cpp
index b72906c3aa..784f30c2e4 100644
--- a/src/AAppIconLabel.cpp
+++ b/src/AAppIconLabel.cpp
@@ -33,21 +33,21 @@ std::string toLowerCase(const std::string& input) {
std::optional<std::string> getFileBySuffix(const std::string& dir, const std::string& suffix,
... | [
"src/AAppIconLabel.cpp",
"src/modules/battery.cpp",
"src/modules/cpu_frequency/linux.cpp"
] | [] | true | ||
Alexays/Waybar | 4,982 | issue_to_patch | fix the io failure for hotplug-in device | related issue #4901. When remove the device that need power-management, Waybar would crash due to uncatch exception | 202ae4bd5f2d8865704761211b3004d2fcd2d7d8 | ac62754b289569b71da33f6982c63eb927796182 | diff --git a/src/modules/battery.cpp b/src/modules/battery.cpp
index d39c49204..30be3857e 100644
--- a/src/modules/battery.cpp
+++ b/src/modules/battery.cpp
@@ -126,7 +126,14 @@ void waybar::modules::Battery::refreshBatteries() {
// Ignore non-system power supplies unless explicitly requested
if (... | [
"src/modules/battery.cpp"
] | [] | true | ||
Alexays/Waybar | 4,990 | issue_to_patch | fix(sni): use std::make_unique for Item allocation | Replace raw allocation of Item with std::make_unique to improve memory safety
and align with modern C++ practices.
Using std::make_unique avoids manual ownership handling and ensures exception
safety during object construction, while preserving existing behavior.
Tested:
-Built successfully with Meson/Ninja
-Ra... | 3a321d934dda790f8ce8dc76d8e545abe1ec3251 | 7e9c46e4d15563ba6b4dc6e470b7a4b445c26c63 | diff --git a/src/modules/sni/host.cpp b/src/modules/sni/host.cpp
index 18eac643bf..567fbf9f7b 100644
--- a/src/modules/sni/host.cpp
+++ b/src/modules/sni/host.cpp
@@ -178,9 +178,11 @@ void Host::addRegisteredItem(const std::string& service) {
return bus_name == item->bus_name && object_path == item->object_path;
... | [
"src/modules/sni/host.cpp"
] | [] | true | ||
Alexays/Waybar | 4,975 | issue_to_patch | memory: feature - add "unit" config option | This PR adds a "unit" config option to the memory module to fix issue #2572.
I also manually updated the documentation in /man. | 1e965ccce06faf9515d0570329c12f7245d958ed | fc11789a4f6d437fd329cf3dada6ffb4b9aaa9e0 | diff --git a/include/modules/memory.hpp b/include/modules/memory.hpp
index 3b6342b344..c73ece23ac 100644
--- a/include/modules/memory.hpp
+++ b/include/modules/memory.hpp
@@ -19,9 +19,13 @@ class Memory : public ALabel {
private:
void parseMeminfo();
+ static float calc_divisor(const std::string& divisor);
+
... | [
"include/modules/memory.hpp",
"man/waybar-memory.5.scd",
"src/modules/memory/common.cpp"
] | [] | true | ||
Alexays/Waybar | 4,967 | issue_to_patch | Network: fix: delete correct address type | This fixes at least two situations where Waybar thinks the interface is in "linked" (no IP) state even though there is an IP address assigned:
1. When only one of the address type is deleted (For example IPv6 with `ip addr del dev wlan0 <IPv6 here>`), even though the other type (IPv4) is still available.
2. In my s... | 1e965ccce06faf9515d0570329c12f7245d958ed | 16886117b3645db1cc32055a9732b4b9cf1562d9 | diff --git a/src/modules/network.cpp b/src/modules/network.cpp
index a39a5ed3d..6dbb8bf05 100644
--- a/src/modules/network.cpp
+++ b/src/modules/network.cpp
@@ -677,12 +677,15 @@ int waybar::modules::Network::handleEvents(struct nl_msg* msg, void* data) {
changed_cidr);
}
... | [
"src/modules/network.cpp"
] | [] | true | ||
Alexays/Waybar | 4,964 | issue_to_patch | fix(network): prevent near-zero bandwidth on rapid event-driven updates | ## Summary
- Network bandwidth shows near-zero values during downloads when netlink events fire between timer intervals
- `update()` is called from both the timer thread and event handlers (link/addr/route changes), each call consuming the byte delta and resetting `bandwidth_down_total_`
- Cache the last computed ba... | 49460defdc9876acae4212769daab9bbe0666d61 | 753294dbf4da6a7276e072c314fc7c078a8404c4 | diff --git a/include/modules/network.hpp b/include/modules/network.hpp
index 3bc43b2381..66fc6d04c7 100644
--- a/include/modules/network.hpp
+++ b/include/modules/network.hpp
@@ -70,6 +70,8 @@ class Network : public ALabel {
unsigned long long bandwidth_down_total_{0};
unsigned long long bandwidth_up_total_{0};... | [
"include/modules/network.hpp",
"src/modules/network.cpp"
] | [] | true | ||
Alexays/Waybar | 4,970 | issue_to_patch | fix(bar): ensure exception safety for group module allocation | Replace raw allocation of group modules with std::unique_ptr
to ensure exception safety during initialization.
If getModules throws or a module fails to initialize, the allocated
object is properly cleaned up before ownership transfer.
Tested with:
- simple group modules
- nested groups
- invalid module conf... | 1e965ccce06faf9515d0570329c12f7245d958ed | 36518e4eca9ccfc09403b80466621fd58a1fabfb | diff --git a/src/bar.cpp b/src/bar.cpp
index 6a78707eb..6e81332c7 100644
--- a/src/bar.cpp
+++ b/src/bar.cpp
@@ -545,9 +545,11 @@ void waybar::Bar::getModules(const Factory& factory, const std::string& pos,
if (group_config["modules"].isNull()) {
spdlog::warn("Group definition '{}' has not been ... | [
"src/bar.cpp"
] | [] | true | ||
Alexays/Waybar | 4,966 | issue_to_patch | niri/workspaces: feature - add "hide-empty" config option | This PR adds "hide-empty" option for niri/workspace module. #4965 | 1e965ccce06faf9515d0570329c12f7245d958ed | 8b1e5740634093b89582d9708981aeb37b8f09ab | diff --git a/src/modules/niri/workspaces.cpp b/src/modules/niri/workspaces.cpp
index 3e8a432efa..97d15215a0 100644
--- a/src/modules/niri/workspaces.cpp
+++ b/src/modules/niri/workspaces.cpp
@@ -114,6 +114,11 @@ void Workspaces::doUpdate() {
button.show();
else
button.hide();
+ } else if (co... | [
"src/modules/niri/workspaces.cpp"
] | [] | true | ||
Alexays/Waybar | 4,973 | issue_to_patch | Group ref fix | #4969 | 1e965ccce06faf9515d0570329c12f7245d958ed | ae11954398e5d9692805d46f8d52b6ffbf7e3597 | diff --git a/src/bar.cpp b/src/bar.cpp
index 6a78707eb9..f42f31a39f 100644
--- a/src/bar.cpp
+++ b/src/bar.cpp
@@ -541,7 +541,7 @@ void waybar::Bar::getModules(const Factory& factory, const std::string& pos,
auto vertical = (group != nullptr ? group->getBox().get_orientation()
... | [
"src/bar.cpp",
"src/modules/niri/workspaces.cpp"
] | [] | true | ||
Alexays/Waybar | 4,960 | issue_to_patch | feat(client): add support for 8-bit hex color codes in CSS | This allows users to use #RRGGBBAA format in their style.css. The client now detects 8-bit hex codes, transforms them into GTK-compatible rgba() syntax, and loads the modified data into the CSS provider.
- Added utility to detect 8-bit hex patterns.
- Added transformation logic to convert hex alpha to decimal.
- I... | 49460defdc9876acae4212769daab9bbe0666d61 | a9aab4e3569a0c771a873e2a4b1c8695f70430e9 | diff --git a/include/util/hex_checker.hpp b/include/util/hex_checker.hpp
new file mode 100644
index 0000000000..27a84d1f68
--- /dev/null
+++ b/include/util/hex_checker.hpp
@@ -0,0 +1,17 @@
+#pragma once
+
+#include <string>
+
+/**
+ * Result of transforming 8-bit hex codes to rgba().
+ */
+struct TransformResult {
+ s... | [
"include/util/hex_checker.hpp",
"meson.build",
"src/client.cpp",
"src/util/transform_8bit_to_rgba.cpp"
] | [] | true | ||
Alexays/Waybar | 4,962 | issue_to_patch | fix: prevent resource leak when rebinding dwl globals | zdwl_ipc_manager_v2 and wl_seat are assigned via wl_registry_bind without checking for existing instances.
If handle_global is triggered multiple times, previously bound objects may be overwritten without being released, leading to potential resource leaks.
Destroy existing objects before rebinding to prevent res... | 49460defdc9876acae4212769daab9bbe0666d61 | 3f69bacff018371507c9a8f8dbd78aa558a02cd1 | diff --git a/src/modules/dwl/tags.cpp b/src/modules/dwl/tags.cpp
index 399c7d4b0a..243aaa927c 100644
--- a/src/modules/dwl/tags.cpp
+++ b/src/modules/dwl/tags.cpp
@@ -70,17 +70,33 @@ static const zdwl_ipc_output_v2_listener output_status_listener_impl{
static void handle_global(void* data, struct wl_registry* regist... | [
"src/modules/dwl/tags.cpp"
] | [] | true | ||
Alexays/Waybar | 1,784 | issue_to_patch | exec runs after on-* events | This is a fix for issue #1681. `exec` now runs after `on-click`, `on-click-right`, `on-*` events.
Test sample:
```json
"custom/camera": {
"exec": "notify-send exec; echo exec",
"interval": "once",
"on-click": "notify-send on-click && sleep 5",
"exec-on-event": true,
"tooltip": false,
... | 6206cebd758aee14980d3d617d80dd934bd4db6c | d6b6158ae97651cb556ad388b365069843877ed0 | diff --git a/include/AModule.hpp b/include/AModule.hpp
index 2ffc429ca..2fcbfc23d 100644
--- a/include/AModule.hpp
+++ b/include/AModule.hpp
@@ -36,6 +36,7 @@ class AModule : public IModule {
SCROLL_DIR getScrollDir(GdkEventScroll *e);
bool tooltipEnabled() const;
+ std::vector<int> pid_children_;
const std... | [
"include/AModule.hpp",
"src/AModule.cpp",
"src/modules/custom.cpp"
] | [] | true | ||
Alexays/Waybar | 4,789 | issue_to_patch | fix(json): use local CharReaderBuilder for thread safety | ## Problem
Crash with:
```
unhandled exception (type std::exception) in signal handler:
what: in Json::Value::find(begin, end): requires objectValue or nullValue
```
## Root Cause
The `JsonParser` class uses a shared `CharReaderBuilder` member that gets accessed concurrently from multiple threads:
- **IPC thread**:... | b4854f96a3a1f89ddae66f8216ea79411371167a | 1639dec7d87e1aefc15bcd1603d7634844ee3d26 | diff --git a/include/util/json.hpp b/include/util/json.hpp
index f0736f9ba..5f756f353 100644
--- a/include/util/json.hpp
+++ b/include/util/json.hpp
@@ -30,15 +30,16 @@ class JsonParser {
std::istringstream jsonStream(modifiedJsonStr);
std::string errs;
- if (!Json::parseFromStream(m_readerBuilder, jsonS... | [
"include/util/json.hpp"
] | [] | true | ||
Alexays/Waybar | 4,943 | issue_to_patch | Network bandwidth is not calculated correctly if forced to reload
Using the (somewhat) new bandwidth format options in the network module,
I can force the bandwidth to update by clicking on the module twice (switching in and out of alt-mode),
but the displayed bandwidth is too small (checked by running a speedtest).... | Fix network bandwidth rate calculation for early updates | ### Summary
Compute network bandwidth rates using the actual elapsed time between samples instead of the configured polling interval.
This fixes #3718 , i.e. underreported bandwidth values when the network module is updated more frequently than its nominal interval, such as when multiple bars are active on differen... | ab7bfdb297357633b80eb146c419d8905e362e32 | dc31db6d0c79e30fe5c5f219d8c53f1d4bfdb74c | diff --git a/include/modules/network.hpp b/include/modules/network.hpp
index 4abe26bd1..3bc43b238 100644
--- a/include/modules/network.hpp
+++ b/include/modules/network.hpp
@@ -70,6 +70,7 @@ class Network : public ALabel {
unsigned long long bandwidth_down_total_{0};
unsigned long long bandwidth_up_total_{0};
+... | [
"include/modules/network.hpp",
"src/modules/network.cpp"
] | [] | true | |
Alexays/Waybar | 4,946 | issue_to_patch | fix: prevent resource leak when rebinding Wayland globals | Wayland globals (xdg_output_manager and idle_inhibit_manager) are assigned via wl_registry_bind
without checking for existing instances.
If handleGlobal is triggered multiple times, previously bound objects may be overwritten
without being released, leading to potential resource leaks.
This patch ensures that e... | ab7bfdb297357633b80eb146c419d8905e362e32 | 1aa32def3bff7a1af47f20440f443d53a60a9c46 | diff --git a/src/client.cpp b/src/client.cpp
index 103b093f5..1da3ffcb3 100644
--- a/src/client.cpp
+++ b/src/client.cpp
@@ -19,11 +19,26 @@ waybar::Client* waybar::Client::inst() {
void waybar::Client::handleGlobal(void* data, struct wl_registry* registry, uint32_t name,
const char*... | [
"src/client.cpp"
] | [] | true | ||
Alexays/Waybar | 4,953 | issue_to_patch | Fix unnecessary allocation in GTK callback | This reverts #4925.
After reviewing the lifetime guarantees of menuActionsMap_,
the original c_str() usage is already safe in this context.
The use of g_strdup is unnecessary here.
Restoring the original implementation. | 06c3d4393cdba6ee82cdbda43f8e73e04f91f81f | e55bff662e18cc853d6df5c820d05e3823f00ef1 | diff --git a/src/ALabel.cpp b/src/ALabel.cpp
index b23926d8f..795f87f19 100644
--- a/src/ALabel.cpp
+++ b/src/ALabel.cpp
@@ -116,9 +116,8 @@ ALabel::ALabel(const Json::Value& config, const std::string& name, const std::st
}
submenus_[key] = GTK_MENU_ITEM(item);
menuActionsMap_[key] = it->asSt... | [
"src/ALabel.cpp"
] | [] | true | ||
Alexays/Waybar | 4,925 | issue_to_patch | Fix menu-actions GTK callback pointer handling | Fix pointer lifetime handling for menu-actions GTK callbacks.
The previous implementation passed a temporary pointer (`c_str()`) to the GTK callback, which could become invalid after the function scope ended.
This patch ensures the pointer remains valid by allocating a copy of the string using `g_strdup`, providi... | 100349a5c7f45a032712c1ec85696164d44e5430 | 196589cf321ef4fdf848d18f1e249199bc7821e9 | diff --git a/src/ALabel.cpp b/src/ALabel.cpp
index 795f87f19..b23926d8f 100644
--- a/src/ALabel.cpp
+++ b/src/ALabel.cpp
@@ -116,8 +116,9 @@ ALabel::ALabel(const Json::Value& config, const std::string& name, const std::st
}
submenus_[key] = GTK_MENU_ITEM(item);
menuActionsMap_[key] = it->asSt... | [
"src/ALabel.cpp"
] | [] | true | ||
Alexays/Waybar | 4,945 | issue_to_patch | clarify logical condition in wlr taskbar module | Clarify logical condition in wlr taskbar module
The original condition relies on operator precedence between && and ||,
which can be harder to read and may lead to misinterpretation.
This change adds explicit parentheses to make the intended logic clear
without altering behavior.
No functional change. | ab7bfdb297357633b80eb146c419d8905e362e32 | 60c57b7195dabc750dfe1faca01ad936961b120b | diff --git a/src/modules/wlr/taskbar.cpp b/src/modules/wlr/taskbar.cpp
index 2aef0ae37..bde909106 100644
--- a/src/modules/wlr/taskbar.cpp
+++ b/src/modules/wlr/taskbar.cpp
@@ -198,7 +198,7 @@ void Task::handle_title(const char* title) {
title_ = title;
hide_if_ignored();
- if (!with_icon_ && !with_name_ || ap... | [
"src/modules/wlr/taskbar.cpp"
] | [] | true | ||
Alexays/Waybar | 4,948 | issue_to_patch | Network: Fix default interface selection. | When an interface is not specified for the network module, we parse the routing table to look for default routes.
We have defined a default route to:
- have a gateway specified, and
- have no destination specified, or have an all-zero destination.
Previous versions of Waybar had the second condition inverted, c... | ab7bfdb297357633b80eb146c419d8905e362e32 | b64265bdf71eba390b0dbebcea674bfa5434c231 | diff --git a/src/modules/network.cpp b/src/modules/network.cpp
index a4f2bcc29..6c31324a9 100644
--- a/src/modules/network.cpp
+++ b/src/modules/network.cpp
@@ -744,16 +744,20 @@ int waybar::modules::Network::handleEvents(struct nl_msg* msg, void* data) {
/* The destination address.
* Should ... | [
"src/modules/network.cpp"
] | [] | true | ||
Alexays/Waybar | 4,939 | issue_to_patch | chore: Stop using deprecated/removed Nerdfonts codepoints | These Nerdfonts codepoints were removed in [Nerdfonts 3.0.0](https://www.nerdfonts.com/releases#v3.0.0):
> **Breaking 2: Material Design Icons Codepoints**
>
> The old Material Design Icon codepoints are finally dropped. Due to an historic mistake we placed them in between some asiatic glyphs, breaking that script... | 6cb68737e64a851797a26566c7859b53eb2fd8bc | a05c7bc28ffaf889826102cb8e9ac75e9577b4ce | diff --git a/include/modules/gamemode.hpp b/include/modules/gamemode.hpp
index f63861a38..89f66272b 100644
--- a/include/modules/gamemode.hpp
+++ b/include/modules/gamemode.hpp
@@ -26,7 +26,7 @@ class Gamemode : public AModule {
const std::string DEFAULT_FORMAT = "{glyph}";
const std::string DEFAULT_FORMAT_ALT = ... | [
"include/modules/gamemode.hpp",
"man/waybar-clock.5.scd",
"man/waybar-gamemode.5.scd",
"man/waybar-pulseaudio.5.scd",
"man/waybar-river-mode.5.scd",
"man/waybar-sway-mode.5.scd",
"man/waybar-wireplumber.5.scd",
"resources/config.jsonc"
] | [] | true | ||
Alexays/Waybar | 4,929 | issue_to_patch | systemd-failed-units: add tooltip with list of failed units | Something I've been missing in the `systemd-failed-units` module: a way to see _which_ units failed. This implements it as a customizable tooltip.
<img width="1018" height="318" alt="tooltip" src="https://github.com/user-attachments/assets/7d522f35-6505-40e9-8ba7-d720193ff6d9" />
Currently, the tooltip defaults t... | 1019c9d2fe9432e7151cb336ab6b3d523d8cf123 | e7c077ab9a5cd959bd412d18df84462cbd97e3b1 | diff --git a/include/modules/systemd_failed_units.hpp b/include/modules/systemd_failed_units.hpp
index ffb250821..3eda799a2 100644
--- a/include/modules/systemd_failed_units.hpp
+++ b/include/modules/systemd_failed_units.hpp
@@ -3,6 +3,7 @@
#include <giomm/dbusproxy.h>
#include <string>
+#include <vector>
#inclu... | [
"include/modules/systemd_failed_units.hpp",
"man/waybar-systemd-failed-units.5.scd",
"src/modules/systemd_failed_units.cpp"
] | [] | true | ||
Alexays/Waybar | 3,863 | issue_to_patch | add module wayfire/window, wayfire/workspaces | I implemented Wayfire integration (especially for workspaces and window) since it did not seem to exist.
I have confirmed that it works in my environment, but please let me know if there is anything else I can do. | e9d0f2254de7d0bfb1c222cadfe866b830209f31 | a88e5795a448ece7a19b88fc9a8b77a74b62726b | diff --git a/include/modules/wayfire/backend.hpp b/include/modules/wayfire/backend.hpp
new file mode 100644
index 000000000..9d55c8200
--- /dev/null
+++ b/include/modules/wayfire/backend.hpp
@@ -0,0 +1,122 @@
+#pragma once
+
+#include <json/json.h>
+#include <unistd.h>
+
+#include <functional>
+#include <list>
+#includ... | [
"include/modules/wayfire/backend.hpp",
"include/modules/wayfire/window.hpp",
"include/modules/wayfire/workspaces.hpp",
"man/waybar-wayfire-window.5.scd",
"man/waybar-wayfire-workspaces.5.scd",
"meson.build",
"src/factory.cpp",
"src/modules/wayfire/backend.cpp",
"src/modules/wayfire/window.cpp",
"s... | [] | true | ||
Alexays/Waybar | 4,938 | issue_to_patch | Hyprland IPC Crash with hyprland/window
I am running into a seg fault when using hyprland/window with hyprland v0.54.1 on nixos. It fails to parse the new ipc is my guess as gdb is giving
```
Thread 9 ".waybar-wrapped" received signal SIGSEGV, Segmentation fault.
[Switching to Thread 0x7fffdb6fc6c0 (LWP 33538)]
0x000... | fix(hyprland/window): Fix segfault caused by use-after-free | The window module registers itself with the Hyprland IPC singleton at
the start of its constructor, before calling update(). If update()
throws an exception (e.g. from an invalid format string), the object is
destroyed without the destructor running, leaving a dangling pointer in
the IPC callback list. When the IPC thr... | 6cb68737e64a851797a26566c7859b53eb2fd8bc | 83e1949dd8435d5f8991de366a8327a2a6e58786 | diff --git a/src/modules/hyprland/window.cpp b/src/modules/hyprland/window.cpp
index d3fe6edf9..2e3e0bb9e 100644
--- a/src/modules/hyprland/window.cpp
+++ b/src/modules/hyprland/window.cpp
@@ -19,20 +19,19 @@ std::shared_mutex windowIpcSmtx;
Window::Window(const std::string& id, const Bar& bar, const Json::Value& co... | [
"src/modules/hyprland/window.cpp"
] | [] | true | |
Alexays/Waybar | 4,937 | issue_to_patch | Add an option for groups to start expanded by default
Currently, group modules configured as drawers always initialize in a collapsed state. I would prefer my modules to be visible at a glance when Waybar starts up, giving me the option to collapse them later if I want a cleaner bar. Right now, I have to manually hove... | Add start-expanded option to group | Fixes #4936
This adds a new "start-expanded" option for the "drawer" object in the "group" module. Defaults to false to preserve backwards compatibility. | 6cb68737e64a851797a26566c7859b53eb2fd8bc | 6afe10864279dd09a66c13d222f61e9d1bcaa260 | diff --git a/man/waybar.5.scd.in b/man/waybar.5.scd.in
index 624798ed8..8b923eef9 100644
--- a/man/waybar.5.scd.in
+++ b/man/waybar.5.scd.in
@@ -363,6 +363,11 @@ A group may hide all but one element, showing them only on mouse hover. In order
default: false ++
Whether left click should reveal the content rather tha... | [
"man/waybar.5.scd.in",
"src/group.cpp"
] | [] | true | |
Alexays/Waybar | 4,931 | issue_to_patch | chore: remove heaptrack dump | Accidentally committed... | b77b1818f63492870f2e467d913d90d23d1c4c14 | 86234a8946787798ba39530ea3c74b017efe87d2 | diff --git a/.gitignore b/.gitignore
index f42bf85e5..f21700d6b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -52,3 +52,4 @@ result-*
.ccls-cache
_codeql_detected_source_root
+heaptrack*
diff --git a/heaptrack.waybar.2711357.zst b/heaptrack.waybar.2711357.zst
deleted file mode 100644
index 88921cba1..000000000
Binary... | [
".gitignore",
"heaptrack.waybar.2711357.zst"
] | [] | true | ||
Alexays/Waybar | 4,710 | issue_to_patch | cava man | Hi @Alexays , this PR updates man file to align the documentation with the current behavior of the module. Wiki is updated as well | 97eb60677e555221326009e0bc3def67e85d33f4 | c8c3287be3a3c13701795c6dfb0f8fdfa40312d1 | diff --git a/man/waybar-cava.5.scd b/man/waybar-cava.5.scd
index 7825c38a2..95336e248 100644
--- a/man/waybar-cava.5.scd
+++ b/man/waybar-cava.5.scd
@@ -8,6 +8,8 @@ waybar - cava module
*cava* module for karlstav/cava project. See it on github: https://github.com/karlstav/cava.
+Module supports two different front... | [
"man/waybar-cava.5.scd"
] | [] | true | ||
Alexays/Waybar | 4,932 | issue_to_patch | cava bump | Hi @Alexays,
This PR is self-descriptive ) | b77b1818f63492870f2e467d913d90d23d1c4c14 | 95f9922ccc6e931d161b6d846f83b747f63b01af | diff --git a/nix/default.nix b/nix/default.nix
index 43b6e0970..1850a4149 100644
--- a/nix/default.nix
+++ b/nix/default.nix
@@ -6,13 +6,13 @@
}:
let
libcava = rec {
- version = "0.10.7-beta";
+ version = "0.10.7";
src = pkgs.fetchFromGitHub {
owner = "LukashonakV";
repo = "cava";
#... | [
"nix/default.nix",
"subprojects/libcava.wrap"
] | [] | true | ||
Alexays/Waybar | 4,924 | issue_to_patch | Add simple calendar tooltip example for clock module | Adds a simple example showing how to display the calendar in the clock tooltip using `{calendar}`.
This helps users understand how to configure a calendar tooltip in Waybar. | 100349a5c7f45a032712c1ec85696164d44e5430 | 558c2753d79bcbaa87ef4c3c2cf4f63578bd6484 | diff --git a/man/waybar-clock.5.scd b/man/waybar-clock.5.scd
index b4b5d5b72..5c5509f69 100644
--- a/man/waybar-clock.5.scd
+++ b/man/waybar-clock.5.scd
@@ -259,7 +259,19 @@ View all valid format options in *strftime(3)* or have a look https://en.cpprefe
"tooltip-format": "{tz_list}"
}
```
+5. Simple calendar toolt... | [
"man/waybar-clock.5.scd"
] | [] | true | ||
Alexays/Waybar | 4,913 | issue_to_patch | fix(tray): complete attention/overlay implementation | Top is current version, bottom shows the overlay/attention working with PR
<img width="51" height="114" alt="image" src="https://github.com/user-attachments/assets/19ba21f1-24f9-4594-ab97-9c4e5fb97650" />
| e425423648ea620627a0ba0c9c81173af18c0254 | 8e2e437ec665af43a7d16d80b53934ea3fefe733 | diff --git a/include/modules/sni/host.hpp b/include/modules/sni/host.hpp
index 7248ad2f4..d76ec74a4 100644
--- a/include/modules/sni/host.hpp
+++ b/include/modules/sni/host.hpp
@@ -16,7 +16,7 @@ class Host {
public:
Host(const std::size_t id, const Json::Value&, const Bar&,
const std::function<void(std::un... | [
"include/modules/sni/host.hpp",
"include/modules/sni/item.hpp",
"include/modules/sni/tray.hpp",
"src/modules/sni/host.cpp",
"src/modules/sni/item.cpp",
"src/modules/sni/tray.cpp",
"src/modules/sni/watcher.cpp"
] | [] | true | ||
Alexays/Waybar | 4,910 | issue_to_patch | fix(hyprland): misc hardening with ipc socket and events | Formatting handled in https://github.com/Alexays/Waybar/pull/4911 | e425423648ea620627a0ba0c9c81173af18c0254 | 6317022304a49c0033c52f0c6cdfe038e88dd314 | diff --git a/include/modules/hyprland/window.hpp b/include/modules/hyprland/window.hpp
index 2be645942..9725d33a1 100644
--- a/include/modules/hyprland/window.hpp
+++ b/include/modules/hyprland/window.hpp
@@ -20,8 +20,8 @@ class Window : public waybar::AAppIconLabel, public EventHandler {
private:
struct Workspa... | [
"include/modules/hyprland/window.hpp",
"include/modules/hyprland/workspaces.hpp",
"src/modules/hyprland/backend.cpp",
"src/modules/hyprland/language.cpp",
"src/modules/hyprland/submap.cpp",
"src/modules/hyprland/window.cpp",
"src/modules/hyprland/workspaces.cpp",
"test/hyprland/backend.cpp"
] | [] | diff --git a/test/hyprland/backend.cpp b/test/hyprland/backend.cpp
index cc7295eca..ccc2da65a 100644
--- a/test/hyprland/backend.cpp
+++ b/test/hyprland/backend.cpp
@@ -86,6 +86,24 @@ TEST_CASE("XDGRuntimeDirExistsNoHyprDir", "[getSocketFolder]") {
fs::remove_all(tempDir, ec);
}
+TEST_CASE("Socket folder is resol... | true | |
Alexays/Waybar | 4,911 | issue_to_patch | menus not showing after ef3d55980e
The pop-up menu stopped popping up for me on Artix. With increased logging, the following errors occurred when attempting to show the menu:
```
(waybar:26625): Gtk-CRITICAL **: 22:12:48.111: gtk_widget_show_all: assertion 'GTK_IS_WIDGET (widget)' failed
(waybar:26625): ... | fix(menu): keep popup menus alive after builder teardown | The popup menu was retrieved from GtkBuilder and stored in menu_, but the builder was unref'd immediately after construction. That left the later popup path operating on a builder-owned GtkMenu whose lifetime was no longer guaranteed, which matches the GTK_IS_WIDGET and GTK_IS_MENU assertions from the regression report... | 68d4360c262f3aa77ae69ea04ad92114ca7ab7d4 | 790101f824513a6b48396fd956dd3a9dfae5ad4b | diff --git a/include/modules/sway/ipc/client.hpp b/include/modules/sway/ipc/client.hpp
index eb0f32f9c..f6eb7c40e 100644
--- a/include/modules/sway/ipc/client.hpp
+++ b/include/modules/sway/ipc/client.hpp
@@ -13,8 +13,8 @@
#include "ipc.hpp"
#include "util/SafeSignal.hpp"
-#include "util/sleeper_thread.hpp"
#inclu... | [
"include/modules/sway/ipc/client.hpp",
"src/ALabel.cpp",
"src/AModule.cpp",
"src/modules/custom.cpp",
"src/modules/hyprland/window.cpp",
"src/modules/niri/backend.cpp",
"test/utils/command.cpp"
] | [] | diff --git a/test/utils/command.cpp b/test/utils/command.cpp
index 2ccb33830..053a2b77a 100644
--- a/test/utils/command.cpp
+++ b/test/utils/command.cpp
@@ -4,11 +4,12 @@
#include <catch2/catch.hpp>
#endif
+#include <sys/wait.h>
+#include <unistd.h>
+
#include <cerrno>
#include <list>
#include <mutex>
-#include ... | true |
Alexays/Waybar | 4,897 | issue_to_patch | [BUG][Network] Inconsistent bandwidth calculation in tooltip (missing unit conversion)
## Description
In **NETWORK** module, I noticed that the network bandwidth calculation logic in the `tooltip_text` is inconsistent with the `text`, resulting in incorrect values (off by a factor of **1000**).
## Context
In a prev... | fix(network): align tooltip and tooltip text | Closes https://github.com/Alexays/Waybar/issues/4867
Also a small tweak to prevent the massive network spam i was seeing in logs. | fd086d0f3366206417bcf59cc18679e32154072f | c5449bd361bed8be9875c1322fd0d693ba975cdd | diff --git a/src/modules/network.cpp b/src/modules/network.cpp
index 34dcc03c1..a25024e80 100644
--- a/src/modules/network.cpp
+++ b/src/modules/network.cpp
@@ -373,18 +373,26 @@ auto waybar::modules::Network::update() -> void {
fmt::arg("cidr6", cidr6_), fmt::arg("frequency", fmt::format("{:.1f}", frequency... | [
"src/modules/network.cpp"
] | [] | true | |
Alexays/Waybar | 4,892 | issue_to_patch | fix(command): return non-zero when child exec fails | Child exec failure paths were returning success, which masked command launch
errors from callers.
I switched the child-side failure exits to _exit(127) and added errno-specific
logging so failures propagate with actionable diagnostics. | fd086d0f3366206417bcf59cc18679e32154072f | 79fb1d9f58d15cb3704a62359a7457451cbc0164 | diff --git a/include/util/command.hpp b/include/util/command.hpp
index b1adcd7c9..f6d2cabf8 100644
--- a/include/util/command.hpp
+++ b/include/util/command.hpp
@@ -20,6 +20,8 @@ extern std::list<pid_t> reap;
namespace waybar::util::command {
+constexpr int kExecFailureExitCode = 127;
+
struct res {
int exit_c... | [
"include/util/command.hpp",
"test/utils/command.cpp",
"test/utils/meson.build"
] | [] | diff --git a/test/utils/command.cpp b/test/utils/command.cpp
new file mode 100644
index 000000000..2ccb33830
--- /dev/null
+++ b/test/utils/command.cpp
@@ -0,0 +1,56 @@
+#if __has_include(<catch2/catch_test_macros.hpp>)
+#include <catch2/catch_test_macros.hpp>
+#else
+#include <catch2/catch.hpp>
+#endif
+
+#include <ce... | true | |
Alexays/Waybar | 4,891 | issue_to_patch | fix(hyprland-ipc): harden fd lifecycle and listener loop | Another slight implementation shift to protect against memory / open file leaks.
Hyprland IPC had fd lifecycle risks on failure/shutdown paths and used a
spin-sleep listener model.
I initialized fd state defensively, tightened connect/close/shutdown handling,
moved to blocking read with newline framing, and ad... | fd086d0f3366206417bcf59cc18679e32154072f | 8d22d3e07a629470114edcebabe6d6fb6afe85cf | diff --git a/include/modules/hyprland/backend.hpp b/include/modules/hyprland/backend.hpp
index 2e0ef6571..a6ebd1917 100644
--- a/include/modules/hyprland/backend.hpp
+++ b/include/modules/hyprland/backend.hpp
@@ -1,5 +1,6 @@
#pragma once
+#include <atomic>
#include <filesystem>
#include <list>
#include <mutex>
@@... | [
"include/modules/hyprland/backend.hpp",
"include/modules/sway/ipc/client.hpp",
"include/modules/wayfire/backend.hpp",
"include/util/scoped_fd.hpp",
"src/modules/hyprland/backend.cpp",
"src/modules/niri/backend.cpp",
"src/modules/sway/ipc/client.cpp",
"src/modules/wayfire/backend.cpp",
"test/hyprland... | [] | diff --git a/test/hyprland/backend.cpp b/test/hyprland/backend.cpp
index b83b839cb..cc7295eca 100644
--- a/test/hyprland/backend.cpp
+++ b/test/hyprland/backend.cpp
@@ -4,56 +4,114 @@
#include <catch2/catch.hpp>
#endif
-#include "fixtures/IPCTestFixture.hpp"
+#include <system_error>
+
+#include "modules/hyprland/ba... | true | |
Alexays/Waybar | 4,898 | issue_to_patch | perf(memory): optimize string operations; remove deep copies, memdup, and icon theme rescanning | Bunch of different optimizations while profiling with heaptrack and valgrind to reduce memory churn. Mostly minimal gains from a lot of it but the sni optimizations do seem to help reduce a lot. | fd086d0f3366206417bcf59cc18679e32154072f | fe03dfaa3bb32622139a1a3b124d31af631e7837 | diff --git a/heaptrack.waybar.2711357.zst b/heaptrack.waybar.2711357.zst
new file mode 100644
index 000000000..88921cba1
Binary files /dev/null and b/heaptrack.waybar.2711357.zst differ
diff --git a/include/modules/disk.hpp b/include/modules/disk.hpp
index 1b4f31761..b576e19fc 100644
--- a/include/modules/disk.hpp
+++ ... | [
"heaptrack.waybar.2711357.zst",
"include/modules/disk.hpp",
"include/modules/hyprland/windowcreationpayload.hpp",
"include/modules/hyprland/workspaces.hpp",
"include/modules/mpd/mpd.hpp",
"include/modules/sni/host.hpp",
"include/modules/sway/bar.hpp",
"include/modules/sway/language.hpp",
"include/mo... | [] | diff --git a/test/utils/sleeper_thread.cpp b/test/utils/sleeper_thread.cpp
index 7a9a3ca12..9e1a1ef3d 100644
--- a/test/utils/sleeper_thread.cpp
+++ b/test/utils/sleeper_thread.cpp
@@ -4,11 +4,12 @@
#include <catch2/catch.hpp>
#endif
-#include <chrono>
#include <sys/wait.h>
-#include <thread>
#include <unistd.h>
... | true | |
Alexays/Waybar | 4,895 | issue_to_patch | Float Interval < 1 always creates 1ms interval for custom modules
This always evaluates to 0 for any float interval between 0 and 1:
https://github.com/Alexays/Waybar/blob/fd086d0f3366206417bcf59cc18679e32154072f/src/ALabel.cpp#L29
So the std::max call evaluates to 1ms
Looks to have been introduced in https://github... | fix: float interval cast | Fixes https://github.com/Alexays/Waybar/issues/4894
I did test the fix locally and it worked and passed all tests. I was unsure of where to add a regression test.
Apologies for lack of direct evidence. I would normally put up a video, but my linux workstation is where I tested this and that is a separate computer... | fd086d0f3366206417bcf59cc18679e32154072f | d929f1a62c764a4cff0897540bc00bf24beb0d5e | diff --git a/src/ALabel.cpp b/src/ALabel.cpp
index 0d92c3720..fe87e0988 100644
--- a/src/ALabel.cpp
+++ b/src/ALabel.cpp
@@ -26,7 +26,7 @@ ALabel::ALabel(const Json::Value& config, const std::string& name, const std::st
: std::chrono::milliseconds(
(config_["interval"].is... | [
"src/ALabel.cpp"
] | [] | true | |
Alexays/Waybar | 4,905 | issue_to_patch | fix(mpris): disconnect GLib signals before destroying objects | Waybar SEGVs in `Glib::DispatchNotifier::pipe_io_handler` when the MPRIS module is enabled. The crash is intermittent because it requires a race between signal emission and object destruction: a playerctl GLib signal callback (e.g. onPlayerPlay) calls `dp.emit()`, which writes a pointer to the Dispatcher into an intern... | fd086d0f3366206417bcf59cc18679e32154072f | a81621863790ce42cff243b215a01c229cef9142 | diff --git a/src/modules/mpris/mpris.cpp b/src/modules/mpris/mpris.cpp
index 1bdd7df66..5771a5baf 100644
--- a/src/modules/mpris/mpris.cpp
+++ b/src/modules/mpris/mpris.cpp
@@ -161,8 +161,7 @@ Mpris::Mpris(const std::string& id, const Json::Value& config)
if (player) {
g_object_connect(player, "signal::play", G... | [
"src/modules/mpris/mpris.cpp"
] | [] | true | ||
Alexays/Waybar | 4,900 | issue_to_patch | All tooltips stop rendering across the bar when user includes a custom module with a menu, tooltip enabled, and custom tooltip-format.
## Issue
**All tooltips stop rendering across the bar** when there is at least one `custom/*` module that simultaneously has:
- `"tooltip": true`
- `"tooltip-format": "<any value, incl... | fix: sync tooltip updates without resetting hover state | Keep custom module tooltip content in sync while avoiding hover regressions.
Instead of calling set_tooltip_markup() on every refresh, compute the desired
tooltip string and only update the GTK tooltip when the value changes using a
local cache (last_tooltip_markup_).
This preserves tooltip-format/{tooltip} beh... | fd086d0f3366206417bcf59cc18679e32154072f | 3eb2c7e8f43b25387e68b9e320321edf274840ee | diff --git a/include/modules/custom.hpp b/include/modules/custom.hpp
index 6c17c6e45..a345a33bc 100644
--- a/include/modules/custom.hpp
+++ b/include/modules/custom.hpp
@@ -35,6 +35,7 @@ class Custom : public ALabel {
std::string id_;
std::string alt_;
std::string tooltip_;
+ std::string last_tooltip_markup_;... | [
"include/modules/custom.hpp",
"src/modules/custom.cpp"
] | [] | true | |
Alexays/Waybar | 4,844 | issue_to_patch | Battery module continuously outputs battery state to the console.
<img width="334" height="984" alt="Image" src="https://github.com/user-attachments/assets/bf78fc06-ab98-49fa-991c-958f667c419a" />
yeah so basically after every second, the battery status is constantly being outputted to the console for some reason.
T... | fix: remove unnecessary console log for battery status | ### Description
This PR removes the battery status log printed on every module update.
Closes #4840
Closes #4887
<img width="936" height="506" alt="image" src="https://github.com/user-attachments/assets/40bf6b87-1d43-4d34-9a80-2c318850c8f9" />
| d527ccd4c1f53f4bb161677b451aabb89556f2d5 | bd222984bb61ce09f942cba94204d1d53b41e365 | diff --git a/src/modules/battery.cpp b/src/modules/battery.cpp
index c706eb3cd..8bf4a431c 100644
--- a/src/modules/battery.cpp
+++ b/src/modules/battery.cpp
@@ -686,7 +686,7 @@ auto waybar::modules::Battery::update() -> void {
status = getAdapterStatus(capacity);
}
auto status_pretty = status;
- puts(status... | [
"src/modules/battery.cpp"
] | [] | true | |
Alexays/Waybar | 4,890 | issue_to_patch | fix(util): bound SafeSignal queue growth under burst load | SafeSignal could queue events forever when worker threads emitted faster than the main loop could consume, which risks memory growth and stale updates.
I added a queue cap with a drop-oldest policy so growth stays bounded under burst load, plus a regression test that validates bounded delivery. | 31b373b9849091ab95aa8cf31c606e87da95e608 | e4ff024fa87af9ad63b6a9fc9da8f0beb5afa7c6 | diff --git a/include/util/SafeSignal.hpp b/include/util/SafeSignal.hpp
index 340f74eeb..1e901792e 100644
--- a/include/util/SafeSignal.hpp
+++ b/include/util/SafeSignal.hpp
@@ -6,6 +6,7 @@
#include <functional>
#include <mutex>
#include <queue>
+#include <cstddef>
#include <thread>
#include <tuple>
#include <type... | [
"include/util/SafeSignal.hpp",
"test/utils/SafeSignal.cpp"
] | [] | diff --git a/test/utils/SafeSignal.cpp b/test/utils/SafeSignal.cpp
index e7e096b09..1502b7d92 100644
--- a/test/utils/SafeSignal.cpp
+++ b/test/utils/SafeSignal.cpp
@@ -9,6 +9,7 @@
#endif
#include <thread>
#include <type_traits>
+#include <vector>
#include "fixtures/GlibTestsFixture.hpp"
@@ -141,3 +142,33 @@ TE... | true | |
Alexays/Waybar | 4,889 | issue_to_patch | fix(util): ensure SleeperThread lifecycle safety and thread sync | This PR addresses critical stability issues in the SleeperThread utility class. It fixes a
known crash during thread reassignment and resolves potential data races on
control flags.
Motivation
1. Crash on Reassignment: The operator= implementation previously replaced\
the std::thread object without ensuring... | 31b373b9849091ab95aa8cf31c606e87da95e608 | 864523772d427fea72f99a6fe9dcc100dc708acc | diff --git a/include/util/sleeper_thread.hpp b/include/util/sleeper_thread.hpp
index 62d129317..966772a20 100644
--- a/include/util/sleeper_thread.hpp
+++ b/include/util/sleeper_thread.hpp
@@ -1,5 +1,6 @@
#pragma once
+#include <atomic>
#include <chrono>
#include <condition_variable>
#include <ctime>
@@ -31,8 +32... | [
"include/util/sleeper_thread.hpp",
"test/utils/meson.build",
"test/utils/sleeper_thread.cpp"
] | [] | diff --git a/test/utils/meson.build b/test/utils/meson.build
index b7b3665a8..050af262b 100644
--- a/test/utils/meson.build
+++ b/test/utils/meson.build
@@ -13,6 +13,7 @@ test_src = files(
'../../src/config.cpp',
'JsonParser.cpp',
'SafeSignal.cpp',
+ 'sleeper_thread.cpp',
'css_reload_helper.cpp',... | true | |
Alexays/Waybar | 4,888 | issue_to_patch | Waybar just stops working/deadlocking
My waybar just stops working correctly. At first it was just laggy and stuttering, couldn't keep up when I interact with it ( like changing volume). After I switched PulseAudio with PipeWire and WirePlumber and also changed it in my waybar config, It just stops responding and can'... | [WIP] Fix waybar responsiveness and interaction issues | - [x] Fix AudioBackend destructor: lock mainloop before disconnecting context
- [x] Fix AudioBackend PA_CONTEXT_FAILED reconnect: unref old context to prevent leaks
- [x] Fix critical bug: PA_CONTEXT_TERMINATED unconditionally killing mainloop on reconnect
- [x] Fix Wireplumber use-after-free: disconnect signal handler... | 7744320ab2c793d092620c2ec859327280b676a6 | d5297bc42444f22a7266415d9f3042566c101043 | diff --git a/src/modules/wireplumber.cpp b/src/modules/wireplumber.cpp
index e9cb72065..0c0d1dde7 100644
--- a/src/modules/wireplumber.cpp
+++ b/src/modules/wireplumber.cpp
@@ -54,6 +54,15 @@ waybar::modules::Wireplumber::Wireplumber(const std::string& id, const Json::Val
waybar::modules::Wireplumber::~Wireplumber()... | [
"src/modules/wireplumber.cpp",
"src/util/audio_backend.cpp"
] | [] | true | |
Alexays/Waybar | 4,834 | issue_to_patch | feat(niri): niri depends on socket | Don't attempt to use niri modules when socket connection fails. Prevents rendering modules when running another compositor. In same concept as previous Hyprland change https://github.com/Alexays/Waybar/commit/4295faa7c4b23b7f6e86669d1fe8c93562e10241
Trying niri out and noticed this issue we previously had with Hyprl... | d31b131f800d0b2124148f3507e9a4e3ccafff99 | ae60ca62331edeba08d75ef68d0c6c5fe53c9c79 | diff --git a/include/modules/niri/backend.hpp b/include/modules/niri/backend.hpp
index 42b9ff7f8..07be039af 100644
--- a/include/modules/niri/backend.hpp
+++ b/include/modules/niri/backend.hpp
@@ -17,7 +17,7 @@ class EventHandler {
class IPC {
public:
- IPC() { startIPC(); }
+ IPC();
void registerForIPC(con... | [
"include/modules/niri/backend.hpp",
"src/modules/niri/backend.cpp"
] | [] | true | ||
Alexays/Waybar | 4,880 | issue_to_patch | mpd: fix socket FD leak on system-level connection errors | The MPD module leaks socket file descriptors when socket-level errors (`ECONNRESET`, `EPIPE`, `ETIMEDOUT`, etc.) occur, causing Waybar to crash after 12–48h once the process FD table exceeds 1024 entries and `mpd_connection_new()` triggers glibc's fortified `FD_SET` check (`__fdelt_warn → abort`).
## Root cause
`chec... | 64ecdcfa879b9577dc47b6ace02edda92935d12e | 82f076c6c200d0d7e29b0bc4cfd0a8955f3d1ead | diff --git a/src/modules/mpd/mpd.cpp b/src/modules/mpd/mpd.cpp
index f0cc5757f..1e28a58a3 100644
--- a/src/modules/mpd/mpd.cpp
+++ b/src/modules/mpd/mpd.cpp
@@ -323,6 +323,7 @@ void waybar::modules::MPD::checkErrors(mpd_connection* conn) {
case MPD_ERROR_SYSTEM:
if (auto ec = mpd_connection_get_system_error... | [
"src/modules/mpd/mpd.cpp"
] | [] | true | ||
Alexays/Waybar | 4,881 | issue_to_patch | battery: fix SIGABRT on USB device unplug race condition | When a USB device with a battery (e.g. a security key) is unplugged, there's a race between the `fs::directory_iterator` finding the power supply path and `inotify_add_watch` being called on its `uevent` file. If the device disappears in between, `inotify_add_watch` returns -1 and the previous code threw `std::runtime_... | 64ecdcfa879b9577dc47b6ace02edda92935d12e | 49d4049ea3f347f207abafb03eea0bcd5f698838 | diff --git a/src/modules/battery.cpp b/src/modules/battery.cpp
index 58a7f50e7..bdf29ffb9 100644
--- a/src/modules/battery.cpp
+++ b/src/modules/battery.cpp
@@ -139,7 +139,8 @@ void waybar::modules::Battery::refreshBatteries() {
auto event_path = (node.path() / "uevent");
auto wd = inotify_add... | [
"src/modules/battery.cpp"
] | [] | true | ||
Alexays/Waybar | 4,879 | issue_to_patch | bluetooth: fix segfault when DBus manager fails to initialize | When `g_dbus_object_manager_client_new_for_bus_sync()` times out or fails, it returns `nullptr`. That null pointer was used unconditionally in `findCurController()`, `findConnectedDevices()`, and `g_signal_connect()` calls — causing an immediate segfault.
## Changes
- **`findCurController()`** — return `std::nullopt`... | 64ecdcfa879b9577dc47b6ace02edda92935d12e | 0c46818e95fb694394f5c06e3965c18d377cacd6 | diff --git a/src/modules/bluetooth.cpp b/src/modules/bluetooth.cpp
index f491f19b3..c59af3b59 100644
--- a/src/modules/bluetooth.cpp
+++ b/src/modules/bluetooth.cpp
@@ -111,13 +111,16 @@ waybar::modules::Bluetooth::Bluetooth(const std::string& id, const Json::Value&
findConnectedDevices(cur_controller_->path, conn... | [
"src/modules/bluetooth.cpp"
] | [] | true | ||
Alexays/Waybar | 4,846 | issue_to_patch | MPRIS: needs some improvements when more than one player is active
I'm seeing some problems with the module, regarding having more than one track/video playing at the same time.
Situation 1:
1. I put a song for example in mopidy, the MPRIS module indicates mopidy playing;
2. Then, I open a video on youtube thro... | fix(mpris): fall back to next non-ignored player and prefer playing players | When using `playerctld`, the MPRIS module had several issues with player selection:
- **Ignored players caused widget to disappear** — If the most recently active player was in
`ignored-players`, `getPlayerInfo()` returned `std::nullopt` with no fallback, hiding the widget
entirely even when other valid players we... | d527ccd4c1f53f4bb161677b451aabb89556f2d5 | a871d90161fadab34b494d0ee5d018eb90496b8d | diff --git a/include/modules/mpris/mpris.hpp b/include/modules/mpris/mpris.hpp
index ad4dac1e1..a33db4d2b 100644
--- a/include/modules/mpris/mpris.hpp
+++ b/include/modules/mpris/mpris.hpp
@@ -78,6 +78,7 @@ class Mpris : public ALabel {
PlayerctlPlayerManager* manager;
PlayerctlPlayer* player;
+ PlayerctlPlaye... | [
"include/modules/mpris/mpris.hpp",
"src/modules/mpris/mpris.cpp"
] | [] | true | |
Alexays/Waybar | 4,821 | issue_to_patch | fix: owerwrite handleScroll to remove any scrolling handling from group | Hi!
This PR completely removes execution of any command on scroll (even set specifically with "on-scroll-up" or "on-scroll-down") by overwriting handleScroll method for group module. | a8bbc90efecce06d0979d7b93456ab9a5808ca66 | 40200afb6890f4488b9c850b6d3a98526828d212 | diff --git a/include/group.hpp b/include/group.hpp
index 41cb5ee0b..d5cbb289d 100644
--- a/include/group.hpp
+++ b/include/group.hpp
@@ -30,6 +30,7 @@ class Group : public AModule {
bool handleMouseEnter(GdkEventCrossing* const& ev) override;
bool handleMouseLeave(GdkEventCrossing* const& ev) override;
bool ha... | [
"include/group.hpp",
"src/group.cpp"
] | [] | true | ||
Alexays/Waybar | 4,836 | issue_to_patch | tooltips do not display provided values
I have a custom module
```
"custom/xmr": {
"exec": "~/.local/bin/xmr",
"interval": 600,
"return-type": "json",
"tooltip": true,
"format": "XMR £{}",
"tooltip-format": "XMR £{}"
},
```
Which listens to the output of a script, one such output is provided:
```
{"te... | feat: add `{tooltip}` in format replacements in custom module | ### Description
This PR fixes a key issue in custom module. `tooltip` cannot be passed in `"tooltip": "{tooltip} some random text"` when return-type is `json`, as `{tooltip}` is not added in format replacements.
**Before**:
`~/.config/waybar/scripts/pacman-updates.py`
```py
#!/usr/bin/env python3
import subprocess
im... | 306f9706844d2e25c89b1338d12983600f416447 | f96fb53eebba39130cd7ec20b06c77aaff066472 | diff --git a/src/modules/custom.cpp b/src/modules/custom.cpp
index 0a8d9cf6d..e2b705da0 100644
--- a/src/modules/custom.cpp
+++ b/src/modules/custom.cpp
@@ -182,9 +182,10 @@ auto waybar::modules::Custom::update() -> void {
if (tooltipEnabled()) {
if (tooltip_format_enabled_) {
auto tool... | [
"src/modules/custom.cpp"
] | [] | true | |
AlexsJones/llmfit | 606 | issue_to_patch | This app can't run on your PC issue
When I launched it, it shows the message as title says.
Device Name Desktop-i5-12400F
Processor 12th Gen Intel(R) Core(TM) i5-12400F 2.50 GHz
Installed RAM 64.0 GB (63.9 GB usable)
Storage 3.64 TB SSD SanDisk SDSSDH3 4T00
Graphics Card NVIDIA GeForce RTX 3060 (12 GB)
System Type... | fix: bundle WebView2 bootstrapper for Windows desktop app | ## Summary
- Adds `webviewInstallMode: "downloadBootstrapper"` to `tauri.conf.json` so the Windows installer automatically downloads and installs WebView2 if it's missing
## Context
Users on Windows 10 without WebView2 pre-installed hit "This app can't run on your PC" (#195). The root cause is that `tauri.conf.json`... | e317fb8502b485c1721c03d88a3f8bc1e63659b3 | 7fa52aadcc92d565cc19e2eb6489fdd1c3abd54d | diff --git a/llmfit-desktop/tauri.conf.json b/llmfit-desktop/tauri.conf.json
index e9a2c00b..f9fd7243 100644
--- a/llmfit-desktop/tauri.conf.json
+++ b/llmfit-desktop/tauri.conf.json
@@ -5,6 +5,11 @@
"build": {
"frontendDist": "./ui"
},
+ "bundle": {
+ "windows": {
+ "webviewInstallMode": "downloadB... | [
"llmfit-desktop/tauri.conf.json"
] | [] | true | |
AlexsJones/llmfit | 628 | issue_to_patch | docs: update Chinese translation (README.zh.md) | ## Motivation
Updating the Chinese translation (`README.zh.md`) to maintain strict parity with the latest `README.md` updates. As the community grows and new features like Community Leaderboard, Inference Bench, and LM Studio integrations are introduced, keeping localized documentation up-to-date is crucial for Chines... | 2d2fa5867c8ecb3f46c8517dcaf5327837bf8806 | cd935f4a97399d86f3fa444dac78a92b4d8c5577 | diff --git a/README.zh.md b/README.zh.md
index c462b083..4336780f 100644
--- a/README.zh.md
+++ b/README.zh.md
@@ -1,7 +1,7 @@
# llmfit
<p align="center">
- <img src="assets/icon.svg" alt="llmfit 图标" width="128" height="128">
+ <img src="assets/icon.svg" alt="llmfit icon" width="128" height="128">
</p>
<p ali... | [
"README.zh.md"
] | [] | true | ||
AlexsJones/llmfit | 597 | issue_to_patch | chore(deps): bump rmcp from 1.6.0 to 1.7.0 | Bumps [rmcp](https://github.com/modelcontextprotocol/rust-sdk) from 1.6.0 to 1.7.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/modelcontextprotocol/rust-sdk/releases">rmcp's releases</a>.</em></p>
<blockquote>
<h2>rmcp-macros-v1.7.0</h2>
<h3>Added</h3>
<ul>
<li>add task-b... | a376bb83eac9fd4a458d01c34aa6180c1e32884e | e8ffcc5974a4a91600fced0c431fda11aabb36e4 | diff --git a/Cargo.lock b/Cargo.lock
index 82bc7067..4915b5b6 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -576,10 +576,8 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "c673075a2e0e5f4a1dde27ce9dee1ea4558c7ffe648f576438a20ca1d2acc4b0"
dependencies = [
"iana-time-zone",
- "js-sys",
... | [
"Cargo.lock",
"llmfit-tui/Cargo.toml"
] | [] | true | ||
AlexsJones/llmfit | 616 | issue_to_patch | chore(deps): bump http from 1.4.0 to 1.4.1 | Bumps [http](https://github.com/hyperium/http) from 1.4.0 to 1.4.1.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/hyperium/http/releases">http's releases</a>.</em></p>
<blockquote>
<h2>v1.4.1</h2>
<h2>tl;dr</h2>
<ul>
<li>Fix <code>PathAndQuery::from_static()</code> and <code... | ec99edeb8c4f21b7461841209717bcb6702fe2de | 05e8e7736fe1ead5598369515bc4339716604cc9 | diff --git a/Cargo.lock b/Cargo.lock
index d716e7b1..fdfbae7a 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -1903,9 +1903,9 @@ dependencies = [
[[package]]
name = "http"
-version = "1.4.0"
+version = "1.4.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e3ba2a386d7f85a81f119ad7498ebe4... | [
"Cargo.lock"
] | [] | true | ||
AlexsJones/llmfit | 603 | issue_to_patch | feat(data): add Whisper/ASR model entries + audio support roadmap | ## What
Adds 4 `automatic-speech-recognition` model entries to `data/hf_models.json`:
| Model | Params | RTF (GPU) | RTF (CPU) | VRAM |
|---|---|---|---|---|
| openai/whisper-large-v3 | 1.5 B | 0.04 | 0.6 | 2.5 GB |
| openai/whisper-large-v3-turbo | 809 M | 0.007 | 0.12 | 1.5 GB |
| distil-whisper/distil-larg... | abab7f63f0a4b6b6684684356c02a3d19150c6b9 | 4815bf8941d8c4eb33c3837be6381a86592b51b3 | diff --git a/AUDIO_SUPPORT.md b/AUDIO_SUPPORT.md
new file mode 100644
index 00000000..41de9612
--- /dev/null
+++ b/AUDIO_SUPPORT.md
@@ -0,0 +1,129 @@
+# Audio Model Support — Implementation Plan
+
+This document describes the Rust changes needed to fully support
+`pipeline_tag: automatic-speech-recognition` models (Whi... | [
"AUDIO_SUPPORT.md",
"data/hf_models.json",
"llmfit-core/data/hf_models.json",
"llmfit-core/src/models.rs"
] | [] | true | ||
AlexsJones/llmfit | 611 | issue_to_patch | fix(tui): make provider filter input cursor-aware and ASCII-only | ## Summary
Fixes the Provider popup filter input so it behaves like a proper cursor-aware text field.
## Changes
- Add cursor-aware editing for the Provider popup filter input.
- Support left/right cursor movement, Home/End, Backspace, and Delete.
- Keep long filter text within the popup input row using hori... | 6c0b69701e8ec9be28a7a98bd0e94812f64a037c | e1b81211da4d8d0273798de50a6c2278ebd16ca4 | diff --git a/llmfit-tui/src/tui_app.rs b/llmfit-tui/src/tui_app.rs
index a110d52a..adf08440 100644
--- a/llmfit-tui/src/tui_app.rs
+++ b/llmfit-tui/src/tui_app.rs
@@ -46,6 +46,32 @@ fn next_grapheme_boundary(value: &str, index: usize) -> usize {
.unwrap_or_else(|| value.len())
}
+fn insert_ascii_graphic_inp... | [
"llmfit-tui/src/tui_app.rs",
"llmfit-tui/src/tui_events.rs",
"llmfit-tui/src/tui_ui.rs"
] | [] | true | ||
AlexsJones/llmfit | 625 | issue_to_patch | refactor: extract duplicated provider detection and fit pipeline into shared code
## Problem
The provider-installation detection and model-fit analysis pipeline is duplicated across three locations:
| Location | Occurrences |
|---|---|
| `main.rs:run_fit()` | ~20 lines |
| `main.rs:run_recommend()` | ~20 lines |
| `... | refactor: extract duplicated provider detection and fit pipeline into InstalledIndex | ## Problem
Provider-installation detection and the model-fit analysis pipeline are duplicated across three locations:
| Location | Occurrences |
|---|---|
| `main.rs:run_fit()` | ~20 lines |
| `main.rs:run_recommend()` | ~20 lines |
| `tui_app.rs` init + 5 call sites | ~50 lines |
**Behavioral drift**: the TUI check... | ec99edeb8c4f21b7461841209717bcb6702fe2de | 3598e96a1ca695ef2759ebb13440d6d0c894349e | diff --git a/llmfit-core/src/analysis.rs b/llmfit-core/src/analysis.rs
new file mode 100644
index 00000000..ce1e31c9
--- /dev/null
+++ b/llmfit-core/src/analysis.rs
@@ -0,0 +1,160 @@
+use crate::fit::{InferenceRuntime, ModelFit};
+use crate::hardware::SystemSpecs;
+use crate::models::ModelDatabase;
+use crate::provider... | [
"llmfit-core/src/analysis.rs",
"llmfit-core/src/lib.rs",
"llmfit-tui/src/main.rs",
"llmfit-tui/src/tui_app.rs",
"llmfit-tui/src/tui_ui.rs"
] | [] | true | |
AlexsJones/llmfit | 626 | issue_to_patch | docs: add Japanese README | I created Japanese translated README. | ec99edeb8c4f21b7461841209717bcb6702fe2de | da51e052601ea0100ee27ea3dbc9039f7b469b03 | diff --git a/README.ja.md b/README.ja.md
new file mode 100644
index 00000000..f2dffcb8
--- /dev/null
+++ b/README.ja.md
@@ -0,0 +1,978 @@
+# llmfit
+
+<p align="center">
+ <img src="assets/icon.svg" alt="llmfit アイコン" width="128" height="128">
+</p>
+
+<p align="center">
+ <a href="README.md">English</a> ·
+ <a href=... | [
"README.ja.md",
"README.md",
"README.zh.md"
] | [] | true | ||
AlexsJones/llmfit | 604 | issue_to_patch | Gemma 4 QAT GGUF models from Unsloth are missing from search/index
## Summary
`llmfit` does not find the new Unsloth **Gemma 4 QAT** models from Hugging Face, including the GGUF variants, even when searching by exact model id.
This looks like either:
1. the model database/index has not picked up these repos yet, or... | chore(main): release 0.9.31 | :robot: I have created a release *beep* *boop*
---
## [0.9.31](https://github.com/AlexsJones/llmfit/compare/v0.9.30...v0.9.31) (2026-06-09)
### Features
* **lmstudio:** Add LMSTUDIO_API_KEY support for authenticated instances ([0dc2712](https://github.com/AlexsJones/llmfit/commit/0dc2712df4b4d947e0db371c98bf376d4d... | 3f9b22bb032a3b4444750ddfca12acfdf96e27df | 328be3926946305c0e28e3d80141aa7314355984 | diff --git a/.release-please-manifest.json b/.release-please-manifest.json
index 03d18a20..3c6c5d6e 100644
--- a/.release-please-manifest.json
+++ b/.release-please-manifest.json
@@ -1,3 +1,3 @@
{
- ".": "0.9.30"
+ ".": "0.9.31"
}
diff --git a/CHANGELOG.md b/CHANGELOG.md
index b9cc7a74..b3c910ce 100644
--- a/CHANGE... | [
".release-please-manifest.json",
"CHANGELOG.md",
"Cargo.lock",
"Cargo.toml",
"version.txt"
] | [] | true | |
AlexsJones/llmfit | 623 | issue_to_patch | GGUF-only repos report wrong parameter count and memory estimates (e.g. unsloth/DeepSeek-R1-0528-GGUF shows 29.6B instead of 671B)
### Summary
When viewing `unsloth/DeepSeek-R1-0528-GGUF` in llmfit, the tool reports **~29.6B parameters** and memory requirements on the order of **~15 GB**, while Hugging Face correctly... | fix: use GGUF metadata for param counts in GGUF-only repos | ## Summary
Fixes #622
GGUF-only repos (like `unsloth/DeepSeek-R1-0528-GGUF`) have no `safetensors` metadata, so the updater fell back to name-based parameter parsing — producing **29.6B instead of 671B** and wildly wrong memory estimates (15 GB instead of 405 GB at Q4_K_M).
- Add `gguf` field to `HfApiModel` and req... | 0dc2712df4b4d947e0db371c98bf376d4da16c85 | 33047d85fab2913d09905922df9a3daef6b5a314 | diff --git a/llmfit-core/data/hf_models.json b/llmfit-core/data/hf_models.json
index e9238e07..c1f2ffb0 100644
--- a/llmfit-core/data/hf_models.json
+++ b/llmfit-core/data/hf_models.json
@@ -124948,18 +124948,18 @@
{
"name": "unsloth/DeepSeek-R1-0528-GGUF",
"provider": "unsloth",
- "parameter_count": "2... | [
"llmfit-core/data/hf_models.json",
"llmfit-core/src/providers.rs",
"llmfit-core/src/update.rs"
] | [] | true | |
AlexsJones/llmfit | 607 | issue_to_patch | feat(lmstudio): Add LMSTUDIO_API_KEY support for authenticated instances | - Read API key from LMSTUDIO_API_KEY environment variable
- Attach Authorization: Bearer header to all HTTP requests to LM Studio
* detect_with_installed() - model listing probe
* is_available() - availability check
* start_pull() POST - model download initiation
* start_pull() polling GET - download progr... | e317fb8502b485c1721c03d88a3f8bc1e63659b3 | 46f70b790e3bd177e001469d04456ce1e20193c5 | diff --git a/README.md b/README.md
index 172b6756..598933cf 100644
--- a/README.md
+++ b/README.md
@@ -860,6 +860,15 @@ To connect to LM Studio on a different host or port, set the `LMSTUDIO_HOST` env
LMSTUDIO_HOST="http://192.168.1.100:1234" llmfit
```
+### API authentication
+
+If your LM Studio instance has **Re... | [
"README.md",
"llmfit-core/src/providers.rs"
] | [] | true | ||
AlexsJones/llmfit | 620 | issue_to_patch | Gemma 4 QAT GGUF models from Unsloth are missing from search/index
## Summary
`llmfit` does not find the new Unsloth **Gemma 4 QAT** models from Hugging Face, including the GGUF variants, even when searching by exact model id.
This looks like either:
1. the model database/index has not picked up these repos yet, or... | fix: add Gemma 4 QAT models and fallback HF search | ## Summary
Fixes #613
- **Backfill embedded database** with 7 Unsloth Gemma 4 QAT GGUF models (12B, 26B-A4B, 31B, E4B, E2B, E4B-mobile, E2B-mobile) so they are immediately searchable
- **Fallback live HF search** — when `llmfit search` finds no local matches, it now queries HuggingFace directly for GGUF repos, making... | 6c0b69701e8ec9be28a7a98bd0e94812f64a037c | 2b62c6db70c6ee702ecda5cc269b24604240db07 | diff --git a/llmfit-core/data/hf_models.json b/llmfit-core/data/hf_models.json
index 68b47c08..e9238e07 100644
--- a/llmfit-core/data/hf_models.json
+++ b/llmfit-core/data/hf_models.json
@@ -160739,5 +160739,212 @@
"num_experts": 384,
"active_experts": 6,
"active_parameters": 49000000000
+ },
+ {
+ ... | [
"llmfit-core/data/hf_models.json",
"llmfit-core/src/update.rs",
"llmfit-tui/src/main.rs"
] | [] | true | |
AlexsJones/llmfit | 612 | issue_to_patch | fix(tui): ignore modified key input in search | ## Summary
Fixes the main TUI Search input so modified key combinations are not inserted as stray text.
## Changes
- Ignore Search character events that include Control, Alt/Option, Super/Command, Hyper, or Meta modifiers.
- Prevent shortcut/navigation key combinations from appearing as text when the terminal... | 66f4c887b37c617944a8bc14a1d0a20aedac9767 | 8de0a1b5a0627327f82360f9c9ced2ca71cfc4a7 | diff --git a/llmfit-tui/src/tui_events.rs b/llmfit-tui/src/tui_events.rs
index 565da778..9c7ce915 100644
--- a/llmfit-tui/src/tui_events.rs
+++ b/llmfit-tui/src/tui_events.rs
@@ -291,7 +291,7 @@ fn handle_search_mode(app: &mut App, key: KeyEvent) {
KeyCode::Left => app.search_cursor_left(),
KeyCode::R... | [
"llmfit-tui/src/tui_events.rs"
] | [] | true | ||
AlexsJones/llmfit | 610 | issue_to_patch | fix(tui): handle unicode download directory input | ## Summary
Fixes the Download Manager models-dir editor so non-ASCII paths can be edited safely in the TUI.
## Changes
- Handle models-dir cursor movement, insertion, Backspace, and Delete on valid grapheme boundaries.
- Prevent panics when editing non-ASCII filesystem paths (for example, Chinese or Japanese ... | 66f4c887b37c617944a8bc14a1d0a20aedac9767 | da120f8d6993dd2e6cfed899478b6796b1992221 | diff --git a/llmfit-tui/src/tui_app.rs b/llmfit-tui/src/tui_app.rs
index 6b99d161..a110d52a 100644
--- a/llmfit-tui/src/tui_app.rs
+++ b/llmfit-tui/src/tui_app.rs
@@ -1789,6 +1789,47 @@ impl App {
self.dm_editing_dir = true;
}
+ pub fn insert_dm_dir_char(&mut self, c: char) {
+ self.dm_dir_cur... | [
"llmfit-tui/src/tui_app.rs",
"llmfit-tui/src/tui_events.rs",
"llmfit-tui/src/tui_ui.rs"
] | [] | true | ||
AlexsJones/llmfit | 608 | issue_to_patch | fix(tui): handle search input cursor and overflow | ## Summary
- Prevent long TUI search queries from overflowing into adjacent filter columns.
- Allow left/right cursor movement while editing the main TUI search input.
- Keep non-ASCII search input editing on grapheme cluster boundaries and render by terminal cell width.
## Testing
- `cargo fmt --check`
- `... | e317fb8502b485c1721c03d88a3f8bc1e63659b3 | 85960054bbb88c961ffa4797006dd3475a8b4699 | diff --git a/Cargo.lock b/Cargo.lock
index a5a6214b..fcba7fdc 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -2503,6 +2503,8 @@ dependencies = [
"tabled",
"tokio",
"tower",
+ "unicode-segmentation",
+ "unicode-width",
]
[[package]]
diff --git a/llmfit-tui/Cargo.toml b/llmfit-tui/Cargo.toml
index e8cfcd2c..97a767... | [
"Cargo.lock",
"llmfit-tui/Cargo.toml",
"llmfit-tui/src/tui_app.rs",
"llmfit-tui/src/tui_events.rs",
"llmfit-tui/src/tui_ui.rs"
] | [] | true | ||
AlexsJones/llmfit | 598 | issue_to_patch | chore(deps): bump docker/build-push-action from 7.1.0 to 7.2.0 | Bumps [docker/build-push-action](https://github.com/docker/build-push-action) from 7.1.0 to 7.2.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/docker/build-push-action/releases">docker/build-push-action's releases</a>.</em></p>
<blockquote>
<h2>v7.2.0</h2>
<ul>
<li>Bump <c... | a376bb83eac9fd4a458d01c34aa6180c1e32884e | 9371510a0e012dd7a9c2aadc6843617780bc3399 | diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 4db257e1..69db2bbf 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -48,7 +48,7 @@ jobs:
type=raw,value=latest,enable={{is_default_branch}}
- name: Build and push Docker image
- use... | [
".github/workflows/docker.yml"
] | [] | true | ||
AlexsJones/llmfit | 605 | issue_to_patch | feat: upgrade MiniMax default model to M3 | ## Summary
Upgrade the curated MiniMax model list to use **MiniMax-M3** as the latest flagship entry, while keeping MiniMax-M2.7 as a previous-generation option. Removes the older MiniMax-M2.5 from the curated target list.
## Changes
- `data/hf_models.json` / `llmfit-core/data/hf_models.json`:
- Insert `MiniMaxAI/... | f0d4783dd99f4c12a0fe15d564d8bc787ef7c755 | 7407d1035ef7dd7b14ed50a1dc1f3f6e21a08a44 | diff --git a/data/hf_models.json b/data/hf_models.json
index b3a6937e..68b47c08 100644
--- a/data/hf_models.json
+++ b/data/hf_models.json
@@ -154276,7 +154276,7 @@
"active_parameters": 18223715635
},
{
- "name": "MiniMaxAI/MiniMax-M2.7",
+ "name": "MiniMaxAI/MiniMax-M3",
"provider": "minimaxai",
... | [
"data/hf_models.json",
"llmfit-core/data/hf_models.json",
"scripts/scrape_hf_models.py"
] | [] | true | ||
AlexsJones/llmfit | 586 | issue_to_patch | feat: speed up provider navigation when holding shift (gh-571) | PR implements feature request from #571.
When holding shift the up/down navigation in TUI providers select advances by 25 providers instead of 1.
I tested it manually.
Not sure if it's idiomatic rust code. | abab7f63f0a4b6b6684684356c02a3d19150c6b9 | 7d74d7d4a93cb8c229868b00d8bc3385005e3551 | diff --git a/llmfit-tui/src/tui_app.rs b/llmfit-tui/src/tui_app.rs
index 752ddb24..4b789d2e 100644
--- a/llmfit-tui/src/tui_app.rs
+++ b/llmfit-tui/src/tui_app.rs
@@ -9,8 +9,9 @@ use llmfit_core::providers::{
use llmfit_core::quality;
use std::collections::{HashMap, HashSet};
+use std::ops::Add;
use std::sync::mps... | [
"llmfit-tui/src/tui_app.rs",
"llmfit-tui/src/tui_events.rs",
"llmfit-tui/src/tui_ui.rs"
] | [] | true | ||
AlexsJones/llmfit | 582 | issue_to_patch | Provide for efficient narrowing
Coming in for the first time, there are thousands of models, which is overwhelming to say the least.
User needs intuitive and quick ways to find the diamonds in the rough with as few keystrokes as possible.
Possible parameters by which to do this:
- **Hardware**: Does the model fit u... | feat: open TUI with best-fit model preselected | ## Summary
Pre-select the best-fit model when the TUI opens, instead of starting on row 0 of the table. Maintainer suggested exactly this in issue #346: "open the TUI on the best fit selected for you".
## Why this matters
The current behavior makes users scroll to find the model llmfit just told them is the right on... | 9e3477e2165f0b379a003c64a9651fcfd54de6db | c1ae43359c70d3bab8b5e6cd18c5e2707f29f0fe | diff --git a/llmfit-tui/src/tui_app.rs b/llmfit-tui/src/tui_app.rs
index 9d088278..4f24c975 100644
--- a/llmfit-tui/src/tui_app.rs
+++ b/llmfit-tui/src/tui_app.rs
@@ -1166,10 +1166,37 @@ impl App {
app.apply_filters();
app.re_sort();
+ app.preselect_initial_best_fit();
app.enqueue_ca... | [
"llmfit-tui/src/tui_app.rs"
] | [] | true | |
AlexsJones/llmfit | 601 | issue_to_patch | chore(main): release 0.9.30 | :robot: I have created a release *beep* *boop*
---
## [0.9.30](https://github.com/AlexsJones/llmfit/compare/v0.9.29...v0.9.30) (2026-06-01)
### Features
* **tui:** fuzzy search in provider filter popup ([#600](https://github.com/AlexsJones/llmfit/issues/600)) ([b055a04](https://github.com/AlexsJones/llmfit/commit/... | b055a04beb50008c3d868aa7f43598ad6b614d33 | 88f4450a95c61f44a65488e93888bc9a18bf72b4 | diff --git a/.release-please-manifest.json b/.release-please-manifest.json
index eb8621d5..03d18a20 100644
--- a/.release-please-manifest.json
+++ b/.release-please-manifest.json
@@ -1,3 +1,3 @@
{
- ".": "0.9.29"
+ ".": "0.9.30"
}
diff --git a/CHANGELOG.md b/CHANGELOG.md
index ccaba403..b9cc7a74 100644
--- a/CHANGE... | [
".release-please-manifest.json",
"CHANGELOG.md",
"Cargo.lock",
"Cargo.toml",
"version.txt"
] | [] | true | ||
AlexsJones/llmfit | 600 | issue_to_patch | [Feature] Fuzzy Search in Provider Menu
When pressing "P" to filter models by provider, I'd love the ability to type and get fuzzy filtering of the providers for easier selection/deselection.
<img width="434" height="980" alt="Image" src="https://github.com/user-attachments/assets/3b955d34-b690-4436-a8f1-88fa5d963c4d... | feat(tui): fuzzy search in provider filter popup | Closes #589
## What
Adds type-to-fuzzy-filter to the provider filter popup (opened with `P`), as requested in #589. Typing now narrows the provider list with case-insensitive subsequence matching (fzf-style — characters must appear in order but not contiguously).
## Behavior
- **Type any character** → filters the p... | a376bb83eac9fd4a458d01c34aa6180c1e32884e | 49e3539352a02d78b04dba10637646c15d18b6ce | diff --git a/README.md b/README.md
index 3d3a3f50..172b6756 100644
--- a/README.md
+++ b/README.md
@@ -132,7 +132,7 @@ Launches the interactive terminal UI. Your system specs (CPU, RAM, GPU name, VRA
| `V` | Enter Select mode (column-based filtering) |
| `t` ... | [
"README.md",
"llmfit-tui/src/tui_app.rs",
"llmfit-tui/src/tui_events.rs",
"llmfit-tui/src/tui_ui.rs"
] | [] | true | |
AlexsJones/llmfit | 574 | issue_to_patch | brew install compiles LLVM from source, but pre-built binaries exist
Hi! I wanted to highlight something that seems like a quick win.
Pre-built binaries are available in releases (e.g. `llmfit-v0.9.28-x86_64-apple-darwin.tar.gz`) and the curl install script works great:
```curl -fsSL https://llmfit.axjns.dev/install... | chore(main): release 0.9.29 | :robot: I have created a release *beep* *boop*
---
## [0.9.29](https://github.com/AlexsJones/llmfit/compare/v0.9.28...v0.9.29) (2026-05-27)
### Features
* open TUI with best-fit model preselected ([#582](https://github.com/AlexsJones/llmfit/issues/582)) ([834ee20](https://github.com/AlexsJones/llmfit/commit/834ee2... | 665a4e15438b061e33974dcbad639f318ef6b5d2 | 6e97c1504ef0109b2b525df6befd0725c8be5171 | diff --git a/.release-please-manifest.json b/.release-please-manifest.json
index 89c6ba59..eb8621d5 100644
--- a/.release-please-manifest.json
+++ b/.release-please-manifest.json
@@ -1,3 +1,3 @@
{
- ".": "0.9.28"
+ ".": "0.9.29"
}
diff --git a/CHANGELOG.md b/CHANGELOG.md
index dd631d8d..ccaba403 100644
--- a/CHANGE... | [
".release-please-manifest.json",
"CHANGELOG.md",
"Cargo.lock",
"Cargo.toml",
"version.txt"
] | [] | true | |
AlexsJones/llmfit | 577 | issue_to_patch | chore(deps): bump tauri from 2.11.0 to 2.11.2 | Bumps [tauri](https://github.com/tauri-apps/tauri) from 2.11.0 to 2.11.2.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/tauri-apps/tauri/releases">tauri's releases</a>.</em></p>
<blockquote>
<h2>tauri-cli v2.11.2</h2>
<!-- raw HTML omitted -->
<pre><code>Fetching advisory da... | 7c5e2413e6c819fb07b10c0bbf0b583ae05d2ab0 | 8ac3ce3a214b777a85bdf012b523873734491c3e | diff --git a/Cargo.lock b/Cargo.lock
index 7a986b3f..75159272 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -4674,9 +4674,9 @@ checksum = "61c41af27dd6d1e27b1b16b489db798443478cef1f06a660c96db617ba5de3b1"
[[package]]
name = "tauri"
-version = "2.11.0"
+version = "2.11.2"
source = "registry+https://github.com/rust-la... | [
"Cargo.lock"
] | [] | true | ||
AlexsJones/llmfit | 578 | issue_to_patch | chore(deps): bump tokio from 1.52.2 to 1.52.3 | Bumps [tokio](https://github.com/tokio-rs/tokio) from 1.52.2 to 1.52.3.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/tokio-rs/tokio/releases">tokio's releases</a>.</em></p>
<blockquote>
<h2>Tokio v1.52.3</h2>
<h1>1.52.3 (May 8th, 2026)</h1>
<h3>Fixed</h3>
<ul>
<li>sync: fix... | 9e3477e2165f0b379a003c64a9651fcfd54de6db | 74b54c6a96c3fbde2a62ad8ca1e47d79d1099532 | diff --git a/Cargo.lock b/Cargo.lock
index 25a36e83..585651c2 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -5072,9 +5072,9 @@ dependencies = [
[[package]]
name = "tokio"
-version = "1.52.2"
+version = "1.52.3"
source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "110a78583f19d5cdb2c5ccf321d1... | [
"Cargo.lock"
] | [] | true | ||
AlexsJones/llmfit | 580 | issue_to_patch | chore(deps): bump tauri-build from 2.6.0 to 2.6.1 | Bumps [tauri-build](https://github.com/tauri-apps/tauri) from 2.6.0 to 2.6.1.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/tauri-apps/tauri/releases">tauri-build's releases</a>.</em></p>
<blockquote>
<h2>tauri-build v2.6.1</h2>
<!-- raw HTML omitted -->
<pre><code>Fetching ... | 9e3477e2165f0b379a003c64a9651fcfd54de6db | c7d5b665f037ea823a20636c4b2b403043d0c5a0 | diff --git a/Cargo.lock b/Cargo.lock
index 25a36e83..3385346b 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -4725,9 +4725,9 @@ dependencies = [
[[package]]
name = "tauri-build"
-version = "2.6.0"
+version = "2.6.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "be9aa8c59a894f76c29a0025... | [
"Cargo.lock"
] | [] | true | ||
AlexsJones/llmfit | 576 | issue_to_patch | fix: align SortColumn::next with display order of columns in TUI | ## Problem
In TUI when you cycle sort column by pressing 's' the params column is out of the display order:
<img width="600" alt="llmfit-sort" src="https://github.com/user-attachments/assets/4777476e-9605-461a-9c33-80b63fe5c97a" />
llmfit 0.9.28
## Solution
This PR aligns `SortColumn::next` with display ... | 9ec7dd35f7ab603e16caee163ed396f02a30a3ee | 07efb6d5e107c095249b557d99bf877068a5d8b0 | diff --git a/llmfit-core/src/fit.rs b/llmfit-core/src/fit.rs
index 591e8dc4..4deeb8e7 100644
--- a/llmfit-core/src/fit.rs
+++ b/llmfit-core/src/fit.rs
@@ -152,14 +152,14 @@ impl SortColumn {
pub fn next(&self) -> Self {
match self {
+ SortColumn::Params => SortColumn::Score,
Sort... | [
"llmfit-core/src/fit.rs"
] | [] | true | ||
AlexsJones/llmfit | 575 | issue_to_patch | GGUF models installed by hf cli shown as mlx in TUI
I have 6 GGUF models downloaded by official huggingface cli:
```
hf cache ls
ID SIZE LAST_ACCESSED LAST_MODIFIED REFS
----------------------------------- ----- ------------- ------------- ----
model/unsloth/Qwen3.6-27B-GGUF 17.... | fix(core): scan XDG cache path for HF models on macOS | ## Summary
- On macOS, `dirs::cache_dir()` returns `~/Library/Caches` but the HuggingFace CLI stores models in `~/.cache/huggingface/hub/` (XDG-style)
- Changed `dirs_hf_cache()` to `dirs_hf_cache_all()` which checks both paths
- Fixes GGUF and MLX model detection on macOS
Fixes #568
## Test plan
- [x] `cargo test ... | c3e3f0d03fb9a83ced7a4c4ff0ba2ad6926db153 | e2363a2439b61cf2eae1e720a7d5a76e0773c374 | diff --git a/llmfit-core/src/providers.rs b/llmfit-core/src/providers.rs
index 1df7231a..dc148c30 100644
--- a/llmfit-core/src/providers.rs
+++ b/llmfit-core/src/providers.rs
@@ -472,82 +472,106 @@ fn is_likely_gguf_repo(repo_lower: &str) -> bool {
repo_lower.contains("-gguf") || repo_lower.ends_with("gguf")
}
... | [
"llmfit-core/src/providers.rs"
] | [] | true | |
AlexsJones/llmfit | 573 | issue_to_patch | feat: rescrape model catalog and expand Ollama mappings | ## Summary
Supersedes #316. Full rescrape with all models from #316 incorporated.
- Full rescrape of `hf_models.json` (1852 to 5341 models, 663 with GGUF sources)
- Added 9 new curated models to `TARGET_MODELS`: Devstral, Cohere Command A/R+, Falcon3-3B, Nemotron-Super-49B, EXAONE-Deep-2.4B/32B
- Added 21 Ollama mapp... | a736d60626fd4b6c0b92a8b56dedefa6a236d8be | 14ac25f087bf19e7ee3b48da48b93c9771aa5cb2 | diff --git a/data/hf_models.json b/data/hf_models.json
index 1135b58a..b3a6937e 100644
--- a/data/hf_models.json
+++ b/data/hf_models.json
@@ -126,7 +126,7 @@
"capabilities": [],
"pipeline_tag": "unknown",
"architecture": "phi",
- "hf_downloads": 33939,
+ "hf_downloads": 37655,
"hf_likes": 0,
... | [
"data/hf_models.json",
"llmfit-core/data/hf_models.json",
"llmfit-core/src/providers.rs",
"llmfit-tui/src/tui_app.rs",
"scripts/scrape_hf_models.py"
] | [] | true | ||
AlexsJones/llmfit | 572 | issue_to_patch | chore(main): release 0.9.28 | :robot: I have created a release *beep* *boop*
---
## [0.9.28](https://github.com/AlexsJones/llmfit/compare/v0.9.27...v0.9.28) (2026-05-22)
### Features
* **tui:** add ollama_name field to JSON output ([#566](https://github.com/AlexsJones/llmfit/issues/566)) ([36b3181](https://github.com/AlexsJones/llmfit/commit/3... | 6093bcc952eee0b5a64141809665c8bc823e21d8 | 1baa852b5044c11b3eb6b890adacaeba024405f3 | diff --git a/.release-please-manifest.json b/.release-please-manifest.json
index 459f7afb..89c6ba59 100644
--- a/.release-please-manifest.json
+++ b/.release-please-manifest.json
@@ -1,3 +1,3 @@
{
- ".": "0.9.27"
+ ".": "0.9.28"
}
diff --git a/CHANGELOG.md b/CHANGELOG.md
index a3243f84..dd631d8d 100644
--- a/CHANGE... | [
".release-please-manifest.json",
"CHANGELOG.md",
"Cargo.lock",
"Cargo.toml",
"version.txt"
] | [] | true | ||
AlexsJones/llmfit | 566 | issue_to_patch | feat(tui): add ollama_name field to JSON output | ## Summary
Adds an `ollama_name` field to the JSON output produced by `llmfit recommend --json`
and the REST API (`/api/v1/models`, `/api/v1/models/top`).
The value is the string to pass directly to `ollama pull` (e.g. `"llama3.1:8b"`),
or `null` if the model has no known Ollama mapping.
## Motivation
Con... | 051bbbc284fb30e80ca48249409f9fddef502556 | 9db66d6b3df24f414cc8b1b5e3e6dfd35c37c15c | diff --git a/llmfit-tui/src/display.rs b/llmfit-tui/src/display.rs
index c6424350..b377a836 100644
--- a/llmfit-tui/src/display.rs
+++ b/llmfit-tui/src/display.rs
@@ -6,6 +6,7 @@ use llmfit_core::fit::{FitLevel, ModelFit, RunMode, SortColumn};
use llmfit_core::hardware::SystemSpecs;
use llmfit_core::models::LlmModel;... | [
"llmfit-tui/src/display.rs"
] | [] | true | ||
AlexsJones/llmfit | 564 | issue_to_patch | chore(main): release 0.9.27 | :robot: I have created a release *beep* *boop*
---
## [0.9.27](https://github.com/AlexsJones/llmfit/compare/v0.9.26...v0.9.27) (2026-05-20)
### Bug Fixes
* **tui:** correct live-bench keybinding label from B to I ([7aae532](https://github.com/AlexsJones/llmfit/commit/7aae5326ef15018fccb9d1d9605252f75739a749))
---... | 7aae5326ef15018fccb9d1d9605252f75739a749 | 0ddbb93e13ca5bfcea487bbae3123de2bbbc7251 | diff --git a/.release-please-manifest.json b/.release-please-manifest.json
index 4410a978..459f7afb 100644
--- a/.release-please-manifest.json
+++ b/.release-please-manifest.json
@@ -1,3 +1,3 @@
{
- ".": "0.9.26"
+ ".": "0.9.27"
}
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 11dcaab4..a3243f84 100644
--- a/CHANGE... | [
".release-please-manifest.json",
"CHANGELOG.md",
"Cargo.lock",
"Cargo.toml",
"version.txt"
] | [] | true | ||
AlexsJones/llmfit | 560 | issue_to_patch | ROCm incorrect handling of multiple GPUs
I have a Linux system with two AMD GPUs: a Radeon AI Pro R9700 and an RX 7600. `llmfit` incorrectly detects this as two R9700s.
Output from `rocm-smi`, where the RX 7600 is in low-power state because it is not really being used:
```shell
$ rocm-smi --showmeminfo vram
======... | chore(main): release 0.9.26 | :robot: I have created a release *beep* *boop*
---
## [0.9.26](https://github.com/AlexsJones/llmfit/compare/v0.9.25...v0.9.26) (2026-05-19)
### Bug Fixes
* **hardware:** detect distinct AMD GPU models in multi-GPU ROCm systems ([#559](https://github.com/AlexsJones/llmfit/issues/559)) ([a100552](https://github.com/... | 1aa22bcba45bc4ec87edf434616c8a49f8c07ea9 | 9de434044680fc774f013b4943f2fb9fabddcc97 | diff --git a/.release-please-manifest.json b/.release-please-manifest.json
index 1979ed6d..4410a978 100644
--- a/.release-please-manifest.json
+++ b/.release-please-manifest.json
@@ -1,3 +1,3 @@
{
- ".": "0.9.25"
+ ".": "0.9.26"
}
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 78d11a50..11dcaab4 100644
--- a/CHANGE... | [
".release-please-manifest.json",
"CHANGELOG.md",
"Cargo.lock",
"Cargo.toml",
"version.txt"
] | [] | true | |
AlexsJones/llmfit | 562 | issue_to_patch | docs: add contributing guide and code of conduct | Addresses community request for contributor guidelines. Adds CONTRIBUTING.md with setup instructions, PR workflow, model addition guide, and code guidelines. Adds CODE_OF_CONDUCT.md referencing the Contributor Covenant v2.1.
Closes discussion #460 | 1aa22bcba45bc4ec87edf434616c8a49f8c07ea9 | a45d43aaea8fdf19e33326ec0ff7ebd1cca822eb | diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
new file mode 100644
index 00000000..e5073326
--- /dev/null
+++ b/CODE_OF_CONDUCT.md
@@ -0,0 +1,17 @@
+# Code of Conduct
+
+This project adopts the **Contributor Covenant v2.1** as its code of conduct.
+
+The full text is available at: https://www.contributor-covenan... | [
"CODE_OF_CONDUCT.md",
"CONTRIBUTING.md"
] | [] | true | ||
AlexsJones/llmfit | 551 | issue_to_patch | Add Apple M5/M5 Pro/M5 Max memory bandwidth entries | ## Summary
- Adds M5 (153.6 GB/s), M5 Pro (307 GB/s), and M5 Max (614 GB/s) to the Apple Silicon GPU memory bandwidth lookup table
- Includes corresponding test assertions
## Motivation
The M5 Max was released March 2026 and is already shipping in MacBook Pro 14"/16". Without these entries, `llmfit` falls back to a ge... | ce735c95c39ab10bd936238b7ab611d336a13c12 | 390ee687c2fb3b794c0525b62e8763da62ae1503 | diff --git a/llmfit-core/src/hardware.rs b/llmfit-core/src/hardware.rs
index 24fbbe25..bec25ea6 100644
--- a/llmfit-core/src/hardware.rs
+++ b/llmfit-core/src/hardware.rs
@@ -2038,6 +2038,15 @@ pub fn gpu_memory_bandwidth_gbps(name: &str) -> Option<f64> {
}
// ── Apple Silicon (unified memory bandwidth) ───... | [
"llmfit-core/src/hardware.rs"
] | [] | true | ||
AlexsJones/llmfit | 556 | issue_to_patch | chore(deps): bump tokio from 1.52.1 to 1.52.2 | Bumps [tokio](https://github.com/tokio-rs/tokio) from 1.52.1 to 1.52.2.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/tokio-rs/tokio/releases">tokio's releases</a>.</em></p>
<blockquote>
<h2>Tokio v1.52.2</h2>
<h1>1.52.2 (May 4th, 2026)</h1>
<p>This release reverts the LIFO ... | f617ce21c21057c03f77dabf8aa0db3daea5088f | a814b1e4d09f42d8420a6414d41eb3967d27ed78 | diff --git a/Cargo.lock b/Cargo.lock
index 4e6007f1..9e37f840 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -5306,9 +5306,9 @@ dependencies = [
[[package]]
name = "tokio"
-version = "1.52.1"
+version = "1.52.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b67dee974fe86fd92cc45b7a95fd... | [
"Cargo.lock",
"llmfit-tui/Cargo.toml"
] | [] | true | ||
AlexsJones/llmfit | 559 | issue_to_patch | ROCm incorrect handling of multiple GPUs
I have a Linux system with two AMD GPUs: a Radeon AI Pro R9700 and an RX 7600. `llmfit` incorrectly detects this as two R9700s.
Output from `rocm-smi`, where the RX 7600 is in low-power state because it is not really being used:
```shell
$ rocm-smi --showmeminfo vram
======... | fix(hardware): detect distinct AMD GPU models in multi-GPU ROCm systems | ## Summary
- Fixes `detect_amd_gpu_rocm_info()` which only captured the first GPU's name from `rocm-smi --showproductname`, causing all GPUs to report as the same model in mixed setups (e.g. RX 7600 + AI Pro R9700)
- Changed return type from `Option<GpuInfo>` to `Vec<GpuInfo>` with per-GPU name/VRAM parsing and groupin... | a57772e7ac2824decfc1d1fc9f117502ae3ba40d | e788a462d97f5f7840bedc1eb3dd605af5ea6747 | diff --git a/llmfit-core/src/hardware.rs b/llmfit-core/src/hardware.rs
index 24fbbe25..ddd89fec 100644
--- a/llmfit-core/src/hardware.rs
+++ b/llmfit-core/src/hardware.rs
@@ -138,10 +138,13 @@ impl SystemSpecs {
}
// AMD GPUs via rocm-smi or sysfs
- if let Some(amd) = Self::detect_amd_gpu_roc... | [
"llmfit-core/src/hardware.rs"
] | [] | true | |
AlexsJones/llmfit | 557 | issue_to_patch | chore(deps): bump tauri from 2.10.3 to 2.11.0 | Bumps [tauri](https://github.com/tauri-apps/tauri) from 2.10.3 to 2.11.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/tauri-apps/tauri/releases">tauri's releases</a>.</em></p>
<blockquote>
<h2>tauri-cli v2.11.0</h2>
<!-- raw HTML omitted -->
<pre><code>Fetching advisory da... | 712a270479b63d9ffcc8b982103abdcb60e528c7 | 21a515cd444f32e41b49631a8937c9431a79e85a | diff --git a/Cargo.lock b/Cargo.lock
index 7341ed75..7b7d93e7 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -677,12 +677,6 @@ version = "0.9.6"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "c2459377285ad874054d797f3ccebf984978aa39129f6eafde5cdc8315b612f8"
-[[package]]
-name = "convert_c... | [
"Cargo.lock"
] | [] | true | ||
AlexsJones/llmfit | 558 | issue_to_patch | chore(deps): bump tauri-build from 2.5.6 to 2.6.0 | Bumps [tauri-build](https://github.com/tauri-apps/tauri) from 2.5.6 to 2.6.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/tauri-apps/tauri/releases">tauri-build's releases</a>.</em></p>
<blockquote>
<h2>tauri-build v2.6.0</h2>
<!-- raw HTML omitted -->
<pre><code>Fetching ... | f617ce21c21057c03f77dabf8aa0db3daea5088f | 7c6e53c8955d45cd6eaef4e6f538f6dc4c4367b6 | diff --git a/Cargo.lock b/Cargo.lock
index 4e6007f1..7341ed75 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -919,14 +919,20 @@ dependencies = [
[[package]]
name = "ctor"
-version = "0.2.9"
+version = "0.8.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "32a2785755761f3ddc1492979ce1e48... | [
"Cargo.lock"
] | [] | true | ||
AlexsJones/llmfit | 549 | issue_to_patch | chore(main): release 0.9.25 | :robot: I have created a release *beep* *boop*
---
## [0.9.25](https://github.com/AlexsJones/llmfit/compare/v0.9.24...v0.9.25) (2026-05-17)
### Features
* **scoring:** add generation-aware quality scoring ([#553](https://github.com/AlexsJones/llmfit/issues/553)) ([a3e0eb2](https://github.com/AlexsJones/llmfit/comm... | 9e8308d3e6dccf83da35694233a53261902ebbf2 | fdef1b84b126a3266dcf66bcb902bab12f667d0f | diff --git a/.release-please-manifest.json b/.release-please-manifest.json
index 9f7b2b3e..1979ed6d 100644
--- a/.release-please-manifest.json
+++ b/.release-please-manifest.json
@@ -1,3 +1,3 @@
{
- ".": "0.9.24"
+ ".": "0.9.25"
}
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 01f06c74..78d11a50 100644
--- a/CHANGE... | [
".release-please-manifest.json",
"CHANGELOG.md",
"Cargo.lock",
"Cargo.toml",
"version.txt"
] | [] | true | ||
AlexsJones/llmfit | 554 | issue_to_patch | feat(tui): move provider detection to background threads for faster startup | ## Summary
- Moves all network-based provider detection (Ollama, MLX, Docker Model Runner, LM Studio, vLLM) to background threads instead of blocking TUI startup
- App renders immediately after hardware detection; provider results stream in via `mpsc` channel and update the model list as each completes
- Only llama.cpp... | a3e0eb27c57fe05298292f4c61699b9aed2824ea | 329c479702fd7646536cddd72b7f9ac9aa78adde | diff --git a/llmfit-tui/src/main.rs b/llmfit-tui/src/main.rs
index fed5b712..b1c704ec 100644
--- a/llmfit-tui/src/main.rs
+++ b/llmfit-tui/src/main.rs
@@ -1204,9 +1204,8 @@ fn run_tui_inner(
let mut terminal = ratatui::Terminal::new(backend)?;
draw_boot_screen(&mut terminal, "Detecting system hardware...")?;
... | [
"llmfit-tui/src/main.rs",
"llmfit-tui/src/tui_app.rs"
] | [] | true | ||
AlexsJones/llmfit | 553 | issue_to_patch | Quality scoring ignores model generation — newer/smaller models ranked below older/larger ones
## Problem: quality scoring ignores model generation, producing misleading rankings
### Current behavior
`quality_score()` in `fit.rs` determines model quality using **only parameter count** and a flat family name substrin... | feat(scoring): add generation-aware quality scoring | ## Summary
- Adds generation-aware quality scoring so newer model generations (Qwen3.6, Llama 4, DeepSeek V4, etc.) are no longer penalized for having fewer parameters than older models
- Parses model generation from the `architecture` field (already present in HuggingFace metadata) and applies a +3 bonus per generati... | ce735c95c39ab10bd936238b7ab611d336a13c12 | 33cc9e870b4d65f8af860459a62bd3c24ad95bdc | diff --git a/data/hf_models.json b/data/hf_models.json
index 6070caa4..1135b58a 100644
--- a/data/hf_models.json
+++ b/data/hf_models.json
@@ -27,6 +27,62 @@
"shared_expert_intermediate_size": null,
"_discovered": true
},
+ {
+ "name": "Harley-ml/PicoWord-5k",
+ "provider": "harley-ml",
+ "parame... | [
"data/hf_models.json",
"llmfit-core/src/fit.rs",
"llmfit-core/src/models.rs",
"llmfit-core/src/plan.rs",
"llmfit-core/src/update.rs",
"llmfit-tui/src/display.rs",
"llmfit-tui/src/main.rs",
"scripts/validate_generation_scoring.py"
] | [] | true | |
AlexsJones/llmfit | 548 | issue_to_patch | fix(docker): skip HTTP probe on Linux when Docker Desktop is not running | ## Problem
Docker Model Runner is a Docker Desktop feature. On Linux, Docker Desktop is uncommon, so the startup HTTP probe to `localhost:12434` times out after ~800ms on most Linux systems, slowing down every llmfit startup.
## Solution
Add `is_docker_desktop_running()` which checks for Docker Desktop's presence by... | 7677837fad8ce970e8f32b90315617722b535fef | c77f3089eccb4ca20817ac954014b02b76b53102 | diff --git a/llmfit-core/src/hardware.rs b/llmfit-core/src/hardware.rs
index d9937ff7..24fbbe25 100644
--- a/llmfit-core/src/hardware.rs
+++ b/llmfit-core/src/hardware.rs
@@ -579,7 +579,7 @@ impl SystemSpecs {
// Filter out integrated GPUs (iGPUs) that have very little VRAM.
// rocm-smi reports all GP... | [
"llmfit-core/src/hardware.rs",
"llmfit-core/src/providers.rs"
] | [] | true | ||
AlexsJones/llmfit | 546 | issue_to_patch | chore(main): release 0.9.24 | :robot: I have created a release *beep* *boop*
---
## [0.9.24](https://github.com/AlexsJones/llmfit/compare/v0.9.23...v0.9.24) (2026-05-12)
### Features
* add MCP server mode and NATS event publishing to llmfit serve ([#545](https://github.com/AlexsJones/llmfit/issues/545)) ([c4958f7](https://github.com/AlexsJones... | c655bc1b2e10b5b0a13a123344755e450832a06d | f20bfd61c16f6e76ea2b1f1295c5424ca7a4e4a5 | diff --git a/.release-please-manifest.json b/.release-please-manifest.json
index e0891d89..9f7b2b3e 100644
--- a/.release-please-manifest.json
+++ b/.release-please-manifest.json
@@ -1,3 +1,3 @@
{
- ".": "0.9.23"
+ ".": "0.9.24"
}
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 71cf341a..01f06c74 100644
--- a/CHANGE... | [
".release-please-manifest.json",
"CHANGELOG.md",
"Cargo.lock",
"Cargo.toml",
"version.txt"
] | [] | true | ||
AlexsJones/llmfit | 543 | issue_to_patch | fix(hardware): fix iGPU filtering on Ryzen 9 9950X with 2 GB iGPU | On systems with an AMD Ryzen 9 9950X (or similar APUs with a Granite Ridge iGPU), rocm-smi reports two GPU agents: the discrete GPU and the integrated GPU. The iGPU filter in detect_amd_gpu_rocm_info() uses a 2 GB VRAM threshold with >= to distinguish discrete GPUs from iGPUs.
The Ryzen 9 9950X's Granite Ridge iGPU ... | 05e884fcd68a5650afa010c2e93f790233017929 | aac98543ec025386d32304f199a30484a9191ae1 | diff --git a/llmfit-core/src/hardware.rs b/llmfit-core/src/hardware.rs
index f34125db..d9937ff7 100644
--- a/llmfit-core/src/hardware.rs
+++ b/llmfit-core/src/hardware.rs
@@ -579,12 +579,12 @@ impl SystemSpecs {
// Filter out integrated GPUs (iGPUs) that have very little VRAM.
// rocm-smi reports all ... | [
"llmfit-core/src/hardware.rs"
] | [] | true | ||
AlexsJones/llmfit | 544 | issue_to_patch | fix: MoE offload speed overestimate for Qwen3-Coder-Next | ## Summary
- Fix wrong `active_parameters` (3B → 5.46B) for `Qwen/Qwen3-Coder-Next` in model catalog
- Fix MoeOffload speed path skipping `run_mode_factor` (0.8) — early return predates RunModeFactors system
## Problem
LLMFit estimated **28.3 tok/s** for Qwen3-Coder-Next in MoEOffload mode. Actual measured speed on RX... | 05e884fcd68a5650afa010c2e93f790233017929 | 72e64c1472e3776de9e4ecd9009007ce18143adc | diff --git a/llmfit-core/data/hf_models.json b/llmfit-core/data/hf_models.json
index 6070caa4..e357ab2e 100644
--- a/llmfit-core/data/hf_models.json
+++ b/llmfit-core/data/hf_models.json
@@ -52497,7 +52497,7 @@
"is_moe": true,
"num_experts": 512,
"active_experts": 10,
- "active_parameters": 3000000000... | [
"llmfit-core/data/hf_models.json",
"llmfit-core/src/fit.rs"
] | [] | true | ||
AlexsJones/llmfit | 545 | issue_to_patch | feat: add MCP server mode and NATS event publishing | ## Summary
- Adds `llmfit serve --mcp` which runs an MCP server over stdio, exposing 6 tools for agent discovery (system specs, model recommendations, search, hardware planning, runtime checks, installed models)
- Adds `llmfit serve --send-events` (behind `nats` cargo feature) to publish hardware/model events to NATS ... | 05e884fcd68a5650afa010c2e93f790233017929 | e74e250c84b6ae8d823757d75404e7dfc91dad32 | diff --git a/API.md b/API.md
index 34e6febd..4889c910 100644
--- a/API.md
+++ b/API.md
@@ -224,6 +224,107 @@ curl "http://127.0.0.1:8787/api/v1/models/top?limit=5&min_fit=good&use_case=codi
curl "http://127.0.0.1:8787/api/v1/models/Mistral?runtime=any"
```
+---
+
+## MCP Server Mode
+
+llmfit can run as an MCP (Mod... | [
"API.md",
"Cargo.lock",
"llmfit-tui/Cargo.toml",
"llmfit-tui/src/events.rs",
"llmfit-tui/src/main.rs",
"llmfit-tui/src/mcp_server.rs",
"llmfit-tui/src/serve_api.rs",
"llmfit-tui/src/serve_shared.rs"
] | [] | true | ||
AlexsJones/llmfit | 540 | issue_to_patch | chore(main): release 0.9.23 | :robot: I have created a release *beep* *boop*
---
## [0.9.23](https://github.com/AlexsJones/llmfit/compare/v0.9.22...v0.9.23) (2026-05-10)
### Features
* populate architecture metadata for precise KV cache and MoE speed estimation ([#539](https://github.com/AlexsJones/llmfit/issues/539)) ([bab8c01](https://github... | 91fee7702a5728fd1b0b64459c8be024fc82051a | e43d241b39b31bb5483e61ff4c89efd256df82ee | diff --git a/.release-please-manifest.json b/.release-please-manifest.json
index 76d14cdd..e0891d89 100644
--- a/.release-please-manifest.json
+++ b/.release-please-manifest.json
@@ -1,3 +1,3 @@
{
- ".": "0.9.22"
+ ".": "0.9.23"
}
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4aabf8b7..71cf341a 100644
--- a/CHANGE... | [
".release-please-manifest.json",
"CHANGELOG.md",
"Cargo.lock",
"Cargo.toml",
"version.txt"
] | [] | true | ||
AlexsJones/llmfit | 523 | issue_to_patch | fix(bench): 5 follow-up improvements to #278 (keybinding I, labels, UTF-8, dedup HTTP types, README) | Follow-up to #278. Implements the 5 improvements @AlexsJones described in [this comment on #278](https://github.com/AlexsJones/llmfit/pull/278#issuecomment-4365354134) — the `de4522b` commit referenced there wasn't actually pushed (SHA isn't reachable from any branch), so this PR re-implements the same items from the d... | 8896fdb861fa6950081c144ddf23d9eb24aef7ed | e3405d08443a3dcf86370ae4afc5e73ae9fafc8a | diff --git a/README.md b/README.md
index d1a884e6..19d277ea 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@
<a href="https://about.signpath.io"><img src="https://img.shields.io/badge/SignPath-signed-brightgreen?logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNi... | [
"README.md",
"llmfit-core/src/bench.rs",
"llmfit-core/src/quality.rs",
"llmfit-tui/src/main.rs",
"llmfit-tui/src/tui_events.rs",
"llmfit-tui/src/tui_ui.rs"
] | [] | true | ||
AlexsJones/llmfit | 539 | issue_to_patch | feat: populate architecture metadata for precise KV cache and MoE speed estimation | ## Summary
- **Populate architecture metadata** (`hidden_size`, `vocab_size`, `moe_intermediate_size`, `shared_expert_intermediate_size`) from HuggingFace config.json for both curated and auto-discovered models, enabling the existing but previously starved Tier 1 MoE speed estimator and precise KV cache formula
- **Fi... | 4624ae88fa3ed7d59753a93bec09df47838e8679 | 55fa5f574d8e3596fa874817611b6d6ea8e18ebe | diff --git a/data/hf_models.json b/data/hf_models.json
index c7945062..6070caa4 100644
--- a/data/hf_models.json
+++ b/data/hf_models.json
@@ -1,4 +1,60 @@
[
+ {
+ "name": "brain-bzh/reve-positions",
+ "provider": "brain-bzh",
+ "parameter_count": "2K",
+ "parameters_raw": 1629,
+ "min_ram_gb": 1.0,
+ ... | [
"data/hf_models.json",
"llmfit-core/data/hf_models.json",
"llmfit-core/src/models.rs",
"scripts/scrape_hf_models.py"
] | [] | true | ||
AlexsJones/llmfit | 536 | issue_to_patch | chore(main): release 0.9.22 | :robot: I have created a release *beep* *boop*
---
## [0.9.22](https://github.com/AlexsJones/llmfit/compare/v0.9.21...v0.9.22) (2026-05-06)
### Features
* overhaul model discovery with cursor pagination, trending, and additive DB ([#537](https://github.com/AlexsJones/llmfit/issues/537)) ([93e9d67](https://github.c... | 93e9d673cf54bde62dc7b24a1400c1cd89f70c1c | ddbd10c8204b416bb2db2f64c39c5c529d0227ee | diff --git a/.release-please-manifest.json b/.release-please-manifest.json
index 507bde5f..76d14cdd 100644
--- a/.release-please-manifest.json
+++ b/.release-please-manifest.json
@@ -1,3 +1,3 @@
{
- ".": "0.9.21"
+ ".": "0.9.22"
}
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 87dad45d..4aabf8b7 100644
--- a/CHANGE... | [
".release-please-manifest.json",
"CHANGELOG.md",
"Cargo.lock",
"Cargo.toml",
"version.txt"
] | [] | true | ||
Anankke/SSPanel-UIM | 2,552 | issue_to_patch | for sing-box 1.14.0+ | c3ea6113c58d946fc3442cdadc13d30b681cf171 | bbc388544530e2aaaabf5ccea1d342ec19963b66 | diff --git a/config/appprofile.example.php b/config/appprofile.example.php
index f07527dc46..e57a5e24fa 100644
--- a/config/appprofile.example.php
+++ b/config/appprofile.example.php
@@ -61,6 +61,7 @@
'type' => 'quic',
'server' => '223.6.6.6',
'server_port' => 853,
+ ... | [
"config/appprofile.example.php",
"update.sh"
] | [] | true |
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