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

Libraries

How to use cmpatino/nanowhale-100m-base with Transformers:

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

pipe = pipeline("text-generation", model="cmpatino/nanowhale-100m-base", 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("cmpatino/nanowhale-100m-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("cmpatino/nanowhale-100m-base", 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 cmpatino/nanowhale-100m-base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/cmpatino/nanowhale-100m-base

SGLang

How to use cmpatino/nanowhale-100m-base 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 "cmpatino/nanowhale-100m-base" \
    --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": "cmpatino/nanowhale-100m-base",
		"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 "cmpatino/nanowhale-100m-base" \
        --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": "cmpatino/nanowhale-100m-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

cmpatino HF Staff commited on 6 days ago

Commit

b0df9a3

verified ·

1 Parent(s): d10cc5f

docs: simplify Usage to from_pretrained (now works after modeling fix)

Browse files

Files changed (1) hide show

README.md +5 -15

README.md CHANGED Viewed

@@ -74,23 +74,13 @@ This model implements key DeepSeek-V4 innovations at a miniature scale:
 ```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("cmpatino/nanowhale-100m-base", trust_remote_code=True)
-model = AutoModelForCausalLM.from_config(config, trust_remote_code=True).float()
-# Download and load weights
-weights_path = hf_hub_download("cmpatino/nanowhale-100m-base", "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/nanowhale-100m-base")
-# Generate
 input_ids = tokenizer.encode("The meaning of life is", return_tensors="pt").cuda()
 output = model.generate(input_ids, max_new_tokens=100, temperature=0.7, top_p=0.9,
                         pad_token_id=tokenizer.eos_token_id)
@@ -102,7 +92,7 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 - **Small model**: 110M params with 129K vocab means ~37% of parameters are in embeddings, limiting model capacity
 - **Limited training**: Only 5K steps / 2.6B tokens — significantly undertrained compared to production models
 - **Pretrained only**: This is a base model without instruction tuning. Outputs are language-model completions, not conversations.
-- **bf16 NaN**: Use fp32 — the Hyper-Connections architecture produces values that overflow bf16 range at this scale.
 - **Custom architecture**: Requires `trust_remote_code=True`
 ## License

 ```python
 import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "cmpatino/nanowhale-100m-base", trust_remote_code=True, dtype=torch.float32
+).cuda().eval()
 tokenizer = AutoTokenizer.from_pretrained("cmpatino/nanowhale-100m-base")
 input_ids = tokenizer.encode("The meaning of life is", return_tensors="pt").cuda()
 output = model.generate(input_ids, max_new_tokens=100, temperature=0.7, top_p=0.9,
                         pad_token_id=tokenizer.eos_token_id)
 - **Small model**: 110M params with 129K vocab means ~37% of parameters are in embeddings, limiting model capacity
 - **Limited training**: Only 5K steps / 2.6B tokens — significantly undertrained compared to production models
 - **Pretrained only**: This is a base model without instruction tuning. Outputs are language-model completions, not conversations.
+- **fp32 recommended**: The Hyper-Connections architecture can produce values that overflow bf16 range at this scale. Use `dtype=torch.float32`.
 - **Custom architecture**: Requires `trust_remote_code=True`
 ## License