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

Libraries

How to use Menlo/Jan-nano-128k-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Menlo/Jan-nano-128k-gguf",
	filename="jan-nano-128k-Q3_K_L.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Menlo/Jan-nano-128k-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Menlo/Jan-nano-128k-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Menlo/Jan-nano-128k-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Menlo/Jan-nano-128k-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Menlo/Jan-nano-128k-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Menlo/Jan-nano-128k-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Menlo/Jan-nano-128k-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Menlo/Jan-nano-128k-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Menlo/Jan-nano-128k-gguf:Q4_K_M

Use Docker

docker model run hf.co/Menlo/Jan-nano-128k-gguf:Q4_K_M

LM Studio
Jan

vLLM

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

Use Docker

docker model run hf.co/Menlo/Jan-nano-128k-gguf:Q4_K_M

Ollama
How to use Menlo/Jan-nano-128k-gguf with Ollama:
```
ollama run hf.co/Menlo/Jan-nano-128k-gguf:Q4_K_M
```

Unsloth Studio new

How to use Menlo/Jan-nano-128k-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Menlo/Jan-nano-128k-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Menlo/Jan-nano-128k-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Menlo/Jan-nano-128k-gguf to start chatting

Pi new

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Menlo/Jan-nano-128k-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Menlo/Jan-nano-128k-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Menlo/Jan-nano-128k-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Menlo/Jan-nano-128k-gguf:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Menlo/Jan-nano-128k-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Menlo/Jan-nano-128k-gguf with Docker Model Runner:
```
docker model run hf.co/Menlo/Jan-nano-128k-gguf:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Menlo/Jan-nano-128k-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Jan-nano-128k-gguf-Q4_K_M

List all available models

lemonade list

Jan-Nano-128k: Empowering deeper research through extended context understanding.

_{Note: Jan-Nano is a non-thinking model.}

Authors: Alan Dao, Bach Vu Dinh

Overview

Jan-Nano-128k represents a significant advancement in compact language models for research applications. Building upon the success of Jan-Nano, this enhanced version features a native 128k context window that enables deeper, more comprehensive research capabilities without the performance degradation typically associated with context extension methods.

Key Improvements:

🔍 Research Deeper: Extended context allows for processing entire research papers, lengthy documents, and complex multi-turn conversations
⚡ Native 128k Window: Built from the ground up to handle long contexts efficiently, maintaining performance across the full context range
📈 Enhanced Performance: Unlike traditional context extension methods, Jan-Nano-128k shows improved performance with longer contexts

This model maintains full compatibility with Model Context Protocol (MCP) servers while dramatically expanding the scope of research tasks it can handle in a single session.

Evaluation

Jan-Nano-128k has been rigorously evaluated on the SimpleQA benchmark using our MCP-based methodology, demonstrating superior performance compared to its predecessor:

Why Jan-Nano-128k?

Traditional approaches to extending context length, such as YaRN (Yet another RoPE extensioN), often result in performance degradation as context length increases. Jan-Nano-128k breaks this paradigm:

This fundamental difference makes Jan-Nano-128k ideal for research applications requiring deep document analysis, multi-document synthesis, and complex reasoning over large information sets.

🖥️ How to Run Locally

Jan desktop will eventually support this model (WIP). Otherwise you can check the deployment options below that we have tested.

For additional tutorials and community guidance, visit our Discussion Forums.

Deployment

Deploy using VLLM:

vllm serve Menlo/Jan-nano-128k \
    --host 0.0.0.0 \
    --port 1234 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes \
    --rope-scaling '{"rope_type":"yarn","factor":3.2,"original_max_position_embeddings":40960}' --max-model-len 131072

Or llama-server from llama.cpp:

llama-server ... --rope-scaling yarn --rope-scale 3.2 --yarn-orig-ctx 40960

Note: The chat template is included in the tokenizer. For troubleshooting, download the Non-think chat template.

Recommended Sampling Parameters

Temperature: 0.7
Top-p: 0.8
Top-k: 20
Min-p: 0.0

FAQ:

I have Jinja template issue with LMStudio, how can i fix? Here

🤝 Community & Support

Discussions: HuggingFace Community
Issues: GitHub Repository
Documentation: Official Docs

📄 Citation

@misc{dao2025jannanotechnicalreport,
      title={Jan-nano Technical Report}, 
      author={Alan Dao and Dinh Bach Vu},
      year={2025},
      eprint={2506.22760},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.22760}, 
}

Jan-Nano-128k: Empowering deeper research through extended context understanding.