nanowhale-100m 🐳
A small ~110M parameter language model implementing the DeepSeek-V4 architecture, fine-tuned for chat/instruction following. Trained from scratch — no weights from DeepSeek-V4 were used.
- Pretrained base model: cmpatino/nanowhale-100m-base
- This model: SFT on HuggingFaceTB/smol-smoltalk
- Training code: github.com/huggingface/nanowhale
Architecture
This model implements key DeepSeek-V4 innovations at a miniature scale:
| Component | Details |
|---|---|
| Parameters | ~110M total (41M embeddings, 69M non-embedding) |
| Hidden size | 320 |
| Layers | 8 |
| Attention heads | 8 (1 KV head — MQA-style) |
| MLA | Multi-head Latent Attention with q_lora_rank=160 |
| MoE | 4 routed experts + 1 shared, top-2 routing |
| Hyper-Connections | hc_mult=4, Sinkhorn routing (replacing residual connections) |
| MTP | 1 next-token prediction layer |
| Vocab | 129,280 (DeepSeek-V4 tokenizer) |
| Context | 2,048 tokens |
Training
Stage 1: Pretraining
- Dataset: HuggingFaceFW/fineweb-edu
- Steps: 5,000 | Tokens: ~2.6B
- Batch: 32 effective (8 × 4 GA) | Seq length: 2,048
- LR: 6e-4, cosine, 3% warmup
- Precision: bf16 mixed
Stage 2: SFT (this model)
- Dataset: HuggingFaceTB/smol-smoltalk (460K conversations)
- Steps: 3,000 | Tokens: ~72.7M
- Batch: 32 effective (8 × 4 GA) | Seq length: 2,048
- LR: 2e-5, cosine, 5% warmup
- Precision: fp32
Metrics
| Metric | Pretrained | SFT |
|---|---|---|
| Eval loss | — | 2.607 |
| Perplexity (held-out) | 13.62 | 12.90 |
| Token accuracy | 33.8% | 48.5% |
Usage
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"cmpatino/nanowhale-100m", trust_remote_code=True, dtype=torch.float32
).cuda().eval()
tokenizer = AutoTokenizer.from_pretrained("cmpatino/nanowhale-100m")
messages = [{"role": "user", "content": "What are 3 benefits of exercise?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
input_ids = tokenizer.encode(prompt, return_tensors="pt").cuda()
output = model.generate(input_ids, max_new_tokens=200, temperature=0.7, top_p=0.9,
pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(output[0][input_ids.shape[1]:], skip_special_tokens=True))
Limitations
- Tiny model: 110M params with 129K vocabulary — most capacity goes to embeddings. Generations are often incoherent or factually wrong.
- Undertrained: Only 5K pretrain + 3K SFT steps. Production models train for 100K+ steps on trillions of tokens.
- Educational purpose: This model demonstrates the DeepSeek-V4 architecture at small scale. It is not suitable for any production use.
- fp32 recommended: The Hyper-Connections architecture can produce values that overflow bf16 range at this scale. Use
dtype=torch.float32. - Custom code: Requires
trust_remote_code=True.
Hardware
Trained on 1× NVIDIA H100 80GB.
License
Apache-2.0