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hudsongouge
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hudsongouge
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| File | Size | Notes |
|---|---|---|
model.safetensors | 76MB | HF-format weights (Llama-compatible layout) |
tokenizer.json | – | 16k ChatML BPE |
nanochat-40m-bf16.gguf | 77MB | full-precision GGUF |
nanochat-40m-q8_0.gguf | 41MB | Q8_0 |
nanochat-40m-q4_k_m.gguf | 25MB | Q4_K_M |
sft_smoltalk_40m_laptop.yaml | – | full training config |
HuggingFaceTB/smol-smoltalk, subset by source, 1 epoch, ≤1,024 tokens/sample:
| source | conversations | samples | tokens |
|---|---|---|---|
| everyday-conversations | 2,252 | 8,595 | 933,426 |
| explore-instruct-rewrite | 3,020 | 5,986 | 271,456 |
| smol-summarize-5k | 4,658 | 18,514 | 6,666,822 |
| smol-summarize-20k | 18,839 | 33,893 | 5,079,395 |
Train: 206,644 samples / val: 512 (deterministic shuffle, seed 0).
Trained on an Apple M3 Max (30-core GPU) in ~3.1 hours (~8.3k tok/s) on 80.0M tokens from HuggingFaceTB/smol-smoltalk.
Could have gone a lot faster, but I was using my GPU for a lot of other things all at once.
AdamW (lr 2e-3 cosine, warmup 200, wd 0.1, clip 1.0), effective batch 32, 6,458 steps. Val loss 2.66.
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch tok = AutoTokenizer.from_pretrained("hudsongouge/nanochat-40M")m = AutoModelForCausalLM.from_pretrained("hudsongouge/nanochat-40M", dtype=torch.bfloat16)prompt = "<|im_start|>user\nWhat is the capital of France?<|im_end|>\n<|im_start|>assistant\n"ids = tok(prompt, return_tensors="pt").input_idsprint(tok.decode(m.generate(ids, max_new_tokens=40)[0][ids.shape[1]:]))Or point llama.cpp / LM Studio / Ollama at the GGUFs.
text
Q: What is the capital of France?A: The capital of France is Paris.Q: Explain photosynthesis in one sentence.A: Photosynthesis is a process used by plants to convert light energy into chemical energy.Q: Write a haiku about the ocean.A: In the vast ocean's shade,the waves dance under moonlight,silent echoes fade.Q: What is 17 + 25?A: To find the sum of 17 and 25, we can add them together:17 + 25 = 17 + 25 = 30.Dumb, but decent.
| smollm-rewrite-30k | 27,343 | 39,810 | 9,410,604 |
| smol-contraints | 34,107 | 57,443 | 7,280,378 |
| openhermes-50k | 45,130 | 67,336 | 16,686,328 |
| smol-magpie-ultra-short | 25,934 | 25,934 | 33,671,827 |
| total | 161,283 | 257,511 | 80,000,236 |