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
```

cmpatino HF Staff commited on 2 days ago

Commit

f3e29ee

verified ·

1 Parent(s): fa4ce8a

Update README: fix model references to HuggingFaceTB/nanowhale-*

Browse files

Files changed (1) hide show

README.md +7 -5

README.md CHANGED Viewed

@@ -1,9 +1,10 @@
-# SmolDeepSeek-V4 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)
 ## Architecture
@@ -52,19 +53,19 @@ This model implements key DeepSeek-V4 innovations at a miniature scale:
 import torch
 from safetensors.torch import load_file
 from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
 # Load model (recommended: manual load for reliability)
-config = AutoConfig.from_pretrained("cmpatino/smol-deepseek-v4-100m", trust_remote_code=True)
 model = AutoModelForCausalLM.from_config(config, trust_remote_code=True).float()
 # Download and load weights
-from huggingface_hub import hf_hub_download
-weights_path = hf_hub_download("cmpatino/smol-deepseek-v4-100m", "model.safetensors")
 state_dict = load_file(weights_path)
 model.load_state_dict(state_dict, strict=True)
 model = model.cuda().eval()
-tokenizer = AutoTokenizer.from_pretrained("cmpatino/smol-deepseek-v4-100m")
 # Chat
 messages = [{"role": "user", "content": "What are 3 benefits of exercise?"}]
@@ -80,6 +81,7 @@ print(tokenizer.decode(output[0][input_ids.shape[1]:], skip_special_tokens=True)
 - **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.
 - **Custom code**: Requires `trust_remote_code=True`.
 ## Hardware

+# 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
 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?"}]
 - **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