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README.md

@@ -8,46 +8,122 @@ tags: [hobbylm, mixture-of-experts, moe, sparse-moe]
 
 # HobbyLM-Chat (500M MoE, instruction-tuned)
 
-Conversational/instruction-tuned variant of HobbyLM-Base (SmolTalk-style SFT).
+HobbyLM-Chat is the instruction-tuned conversational model — HobbyLM-Base taken through SmolTalk supervised fine-tuning and a SmolLM2-style UltraFeedback DPO pass. The jump from base is large: it holds a coherent persona, follows instructions, and (with a repetition penalty) produces varied, flowing prose.
 
-Part of the **HobbyLM** family — a from-scratch 500M sparse-MoE model trained on consumer-scale budgets.
+It's part of the **HobbyLM** family — a 500M sparse-MoE model (and its variants) built from scratch on a
+hobby budget: FineWeb, a handful of Modal H100 hours, a lot of ablations, and a from-scratch Rust engine
+([`hobby-rs`](https://github.com/harishsg993010/HobbyLM)) to run it on a laptop CPU.
+
+## Intended use
+
+General single- and multi-turn chat / instruction following. Prompt it with the trained `SYSTEM:` / `USER:` / `ASSISTANT:` turn format, and decode with a **repetition penalty ≈1.3** (this is what tames the small-model repetition tendency).
 
 ## Architecture
 
-HobbyLM is a **sparse Mixture-of-Experts (MoE)** transformer (DeepSeek-V3 / Ling-style):
+Every HobbyLM variant shares one core: a **sparse Mixture-of-Experts (MoE)** decoder in the modern
+small-MoE style (DeepSeek-V3 / OLMoE lineage), where each design choice was picked by ablation rather
+than by guesswork.
 
 | Component | Value |
 |---|---|
-| Total parameters | ~500M (≈ a fraction active per token) |
-| Hidden size / layers | 768 / 16 (1 dense FFN layer, 15 MoE) |
+| Total parameters | ~500M (only a fraction is active per token) |
+| Hidden size / layers | 768 / 16 (first FFN dense, the rest MoE) |
 | Routed experts / active | 36 / top-6 (+ 1 always-on shared expert) |
-| Attention | GQA, 12 query / 3 KV heads, head-dim 128, per-head QK-norm |
-| Router | sigmoid gating, aux-loss-free balancing bias, no top-k renorm |
-| Positional | RoPE |
+| Attention | GQA, 12 query / 3 KV heads, decoupled head-dim 128, per-head QK-norm |
+| Router | sigmoid gating, DeepSeek-V3 aux-loss-free load balancing, no top-k renorm |
+| Positional | RoPE (θ up to 1e6 for the 8k-context checkpoints) |
 | Tokenizer | GPT-2 byte-level BPE (50,304 vocab, sentinel-padded) |
+| Optimizer | Muon on the 2-D + per-expert matrices, AdamW on everything else |
+
+The full ablation log (QK-norm is the single biggest lever; aux-loss-free beats classic aux-loss;
+≥32 experts and top-6 help; embedding-scaling hurt) lives in the project's architecture notes.
+
+## Benchmarks
+
+0-shot multiple-choice, our harness. Note that MC benchmarks measure *knowledge*, not *chat quality* — the
+goal of this checkpoint is conversational fluency, which these tasks don't capture. The small dip vs the base
+model is the usual **alignment tax**.
 
+| Task | HobbyLM-Chat | HobbyLM-Base |
+|---|---|---|
+| ARC-challenge | 23.8 | 22.4 |
+| ARC-easy | 42.2 | 42.8 |
+| HellaSwag | 39.5 | 41.6 |
+| PIQA | 67.1 | 69.5 |
+| WinoGrande | 53.6 | 51.3 |
+| OpenBookQA | 27.2 | 29.8 |
+| BoolQ | 44.4 | 51.0 |
+| **Average** | **42.5** | **44.0** |
 
-## Files
+Reasoning tasks (ARC, WinoGrande) held or improved; BoolQ dropped the most — chat phrasing fits the
+log-likelihood format worse, not a capability loss. This is healthy for a ~500M chat model (SmolLM-360M range).
 
-- `model.safetensors` — the model weights (fp32).
-- `config.json` — architecture / hyperparameters.
-- GGUF builds (arch `hobbylm`) live in [`rootxhacker/HobbyLM-gguf`](https://huggingface.co/rootxhacker/HobbyLM-gguf).
+> **How these were measured.** All language-model scores are **0-shot** through our own port of
+> EleutherAI's `lm-evaluation-harness` (a custom `MoELMWrapper` that runs log-likelihood scoring over the
+> HobbyLM MoE + GPT-2 tokenizer). Reference models in the comparison table were run through the **identical
+> harness and task set**, so the numbers are apples-to-apples with ours — they are *not* copied from other
+> model cards. We validated the harness against published cards (e.g. TinyLlama 52.75 vs card 52.99). These
+> are small research models: read the numbers in context, not as leaderboard claims.
 
-## Loading (safetensors)
+## Usage
+
+### Python (PyTorch reference implementation)
+
+HobbyLM is a custom sparse-MoE architecture — there's no `transformers` `AutoModel` for it, so load it with
+the small reference implementation from the [GitHub repo](https://github.com/harishsg993010/HobbyLM):
 
 ```python
-import json, torch
+# HobbyLM is a CUSTOM sparse-MoE architecture, so load it with the reference implementation —
+# NOT transformers.AutoModelForCausalLM (there is no AutoModel mapping for this arch).
+# pip install torch safetensors tiktoken huggingface_hub
+# git clone https://github.com/harishsg993010/HobbyLM && cd HobbyLM
+
+import json, torch, tiktoken
+from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file
-sd  = load_file("model.safetensors")
-cfg = json.load(open("config.json"))
-# rebuild the HobbyLM nn.Module from `cfg` and `load_state_dict(sd)`.
+from hobbylm.config import ModelConfig
+from hobbylm.model import MoETransformer
+from hobbylm.generate import generate
+
+repo = "rootxhacker/HobbyLM-Chat"
+cfg = ModelConfig(**{k: v for k, v in json.load(open(hf_hub_download(repo, "config.json"))).items() if k != "preset"})
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+cfg.expert_backend = "grouped" if device.type == "cuda" else "bmm"
+
+model = MoETransformer(cfg).to(device).eval()
+model.load_state_dict(load_file(hf_hub_download(repo, "model.safetensors")))
+
+enc = tiktoken.get_encoding("gpt2")
+prompt = "USER: Give me three tips for better sleep.\nASSISTANT:"
+ids = torch.tensor([enc.encode_ordinary(prompt)], device=device)
+out = generate(model, ids, max_new_tokens=64, temperature=0.7, top_k=0, device=device,
+               repetition_penalty=1.3)               # temperature=0.0 for greedy
+print(enc.decode(out[0].tolist()))
 ```
 
-## Notes & limitations
+> Prompt it with the trained `USER:` / `ASSISTANT:` turn format (a leading `SYSTEM:` turn is optional). A repetition penalty around **1.3** is recommended.
+
+### GGUF + hobby-rs (CPU)
+
+GGUF builds (architecture `hobbylm`) live in [`rootxhacker/HobbyLM-gguf`](https://huggingface.co/rootxhacker/HobbyLM-gguf). They load
+directly in the from-scratch `hobby-rs` CPU engine — **stock llama.cpp won't load them** without registering
+the `hobbylm` architecture first.
+
+```bash
+hobby-rs --model HobbyLM-Chat.gguf --prompt "..." --n 64
+```
+
+## Training
+
+SFT on ~1.5M chat trajectories (smol-smoltalk + the conversational smoltalk2 subsets), loss on assistant turns only; then UltraFeedback DPO (β=0.1) — the SmolLM2 recipe. SFT loss → ~1.50, DPO preference accuracy 0.50 → 0.64.
+
+## Limitations
 
-- Research model at the ~500M scale: fluent but with the capability ceiling of a small model.
-- The GGUF uses a custom `hobbylm` architecture (see the GGUF repo) and needs `moe-rs` or a patched llama.cpp.
+- Carries the 500M ceiling: factual hallucination, and weak adherence to strict output formats (e.g. exact syllable counts).
+- Use a repetition penalty at decode time; greedy decoding can loop.
+- Not safety-aligned — no RLHF safety tuning.
 
 ## License
 
-Apache-2.0.
+Apache-2.0. Weights aren't a substitute for judgement — this is a research / hobby model at the 500M scale,
+not a production system.