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README.md

@@ -8,46 +8,126 @@ tags: [hobbylm, mixture-of-experts, moe, sparse-moe]
 
 # HobbyLM-Base (500M sparse-MoE foundation LM)
 
-A 500M-parameter sparse Mixture-of-Experts base language model pretrained on FineWeb.
+HobbyLM-Base is the foundation the whole family is built on: a 500M-parameter sparse Mixture-of-Experts decoder trained **from scratch** on FineWeb — no distillation, no borrowed weights. It exists to answer a simple question: how far can you get at the ~500M scale if you sweat the architecture and the training recipe instead of throwing tokens at the problem?
 
-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
+
+A pretrained base model for text completion, and the checkpoint you fine-tune for downstream tasks. It is **not** instruction-tuned — for chat, use [HobbyLM-Chat](https://huggingface.co/rootxhacker/HobbyLM-Chat).
 
 ## 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, 7-task average through our harness (see note below). HobbyLM was trained on **40B tokens** — a tiny
+budget next to the comparison models — so the right way to read this table is *per training token*.
 
+| Model | Params | Pretrain tokens | Avg (7-task) |
+|---|---|---|---|
+| SmolLM2-360M | 360M | ~4T | 56.29 |
+| Qwen3-0.6B | 600M | ~36T | 54.78 |
+| gemma-3-270m | 270M | — | 48.09 |
+| pythia-410m | 410M | 300B | 45.34 |
+| **HobbyLM-Base (500M)** | **500M** | **40B** | **44.05** |
+| opt-350m | 350M | 180B | 43.61 |
+| HobbyLM-130M (sibling) | 130M | 10B | 42.97 |
+| MicroLlama-300M | 300M | 50B | 42.23 |
+| gpt2 | 124M | — | 40.62 |
+| pythia-160m | 160M | 300B | 38.60 |
 
-## Files
+Per-task (0-shot): HellaSwag 41.5 · LAMBADA 40.0 · SciQ 70.3 · PIQA 69.6 · ARC-easy 42.7
+(ARC-challenge / WinoGrande sit near chance, as expected at this scale). Validation loss: **3.03** at 1k
+context, **2.94** after the 8k context-extension.
 
-- `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).
+The ranking tracks **pretraining tokens**, not parameters: the top models see 50–900× more data than we do.
+In the classic ≤300B-token regime, HobbyLM leads per token — the 130M (10B tokens) beats MicroLlama-300M
+(50B), opt-350m (180B) and pythia-160m (300B). Token budget, not architecture, is the gap.
 
-## Loading (safetensors)
+> **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.
+
+## 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-Base"
+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 = "The capital of France is"
+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
+### 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-Base.gguf --prompt "..." --n 64
+```
+
+## Training
+
+Pretrained on ~40B unique FineWeb tokens (8×H100), then context-extended 1k→8k (RoPE θ 1e4→1e6). Muon on the hidden + per-expert matrices, AdamW on the router/embeddings/norms; fp32 router; chunked-checkpointed cross-entropy to fit a larger batch.
+
+## 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.
+- It's a ~500M base model on a 40B-token budget: fluent and factually-okay on easy questions, but it hallucinates and can repeat without a repetition penalty at decode time.
+- Trained on English FineWeb; other languages and code are out of distribution.
+- Not aligned or safety-tuned.
 
 ## 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.