Instructions to use Menlo/Jan-nano-128k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Menlo/Jan-nano-128k with Transformers:

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

pipe = pipeline("text-generation", model="Menlo/Jan-nano-128k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Menlo/Jan-nano-128k")
model = AutoModelForCausalLM.from_pretrained("Menlo/Jan-nano-128k")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Menlo/Jan-nano-128k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Menlo/Jan-nano-128k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Menlo/Jan-nano-128k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Menlo/Jan-nano-128k

SGLang

How to use Menlo/Jan-nano-128k 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 "Menlo/Jan-nano-128k" \
    --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": "Menlo/Jan-nano-128k",
		"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 "Menlo/Jan-nano-128k" \
        --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": "Menlo/Jan-nano-128k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Menlo/Jan-nano-128k with Docker Model Runner:
```
docker model run hf.co/Menlo/Jan-nano-128k
```

Can we deploy this model with Model Context Protocol (MCP)?

by 6NguyenTran6 - opened Jun 30, 2025

Discussion

6NguyenTran6

Jun 30, 2025

•

edited Jun 30, 2025

Hello all,
As the title mentioned, can we deploy this model with Model Context Protocol (MCP)? And if we can, could you please give us the tuts.
Thank you so much.

6NguyenTran6 changed discussion title from Can we deloy this model with Model Context Protocol (MCP)? to Can we deploy this model with Model Context Protocol (MCP)? Jun 30, 2025

alandao

Menlo Research org Jun 30, 2025

there are many ways to use LLM with MCP, you can check it online it's readily available in most sources.

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