Instructions to use HuggingFaceTB/nanowhale-100m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceTB/nanowhale-100m with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="HuggingFaceTB/nanowhale-100m", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/nanowhale-100m", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/nanowhale-100m", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceTB/nanowhale-100m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceTB/nanowhale-100m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceTB/nanowhale-100m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HuggingFaceTB/nanowhale-100m

SGLang

How to use HuggingFaceTB/nanowhale-100m with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "HuggingFaceTB/nanowhale-100m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceTB/nanowhale-100m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "HuggingFaceTB/nanowhale-100m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceTB/nanowhale-100m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HuggingFaceTB/nanowhale-100m with Docker Model Runner:
```
docker model run hf.co/HuggingFaceTB/nanowhale-100m
```

nanowhale-100m / README.md

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	---
	license: apache-2.0
	language:
	- en
	library_name: transformers
	tags:
	- deepseek
	- moe
	- causal-lm
	- sft
	- chat
	datasets:
	- HuggingFaceFW/fineweb-edu
	- HuggingFaceTB/smol-smoltalk
	base_model: HuggingFaceTB/nanowhale-100m-base
	pipeline_tag: text-generation
	model-index:
	- name: nanowhale-100m
	results: []
	---

	# 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: [HuggingFaceTB/nanowhale-100m-base](https://huggingface.co/HuggingFaceTB/nanowhale-100m-base)
	- This model: SFT on [HuggingFaceTB/smol-smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk)
	- Training code: [github.com/huggingface/nanowhale](https://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](https://huggingface.co/datasets/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](https://huggingface.co/datasets/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

	```python
	import torch
	from safetensors.torch import load_file
	from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
	from huggingface_hub import hf_hub_download

	# Load model (recommended: manual load for reliability)
	config = AutoConfig.from_pretrained("HuggingFaceTB/nanowhale-100m", trust_remote_code=True)
	model = AutoModelForCausalLM.from_config(config, trust_remote_code=True).float()

	# Download and load weights
	weights_path = hf_hub_download("HuggingFaceTB/nanowhale-100m", "model.safetensors")
	state_dict = load_file(weights_path)
	model.load_state_dict(state_dict, strict=True)
	model = model.cuda().eval()

	tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/nanowhale-100m")

	# Chat
	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.
	- bf16 NaN: Use fp32 — the Hyper-Connections architecture produces values that overflow bf16 range at this scale.
	- Custom code: Requires `trust_remote_code=True`.

	## Hardware

	Trained on 1× NVIDIA H100 80GB.

	## License

	Apache-2.0