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

Libraries

How to use unsloth/Jan-nano-128k-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unsloth/Jan-nano-128k-GGUF",
	filename="Jan-nano-128k-BF16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use unsloth/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 unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL

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 unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL

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 unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

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

Use Docker

docker model run hf.co/unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL

Ollama
How to use unsloth/Jan-nano-128k-GGUF with Ollama:
```
ollama run hf.co/unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use unsloth/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 unsloth/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 unsloth/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 unsloth/Jan-nano-128k-GGUF to start chatting

How to use unsloth/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 unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL

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": "unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use unsloth/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 unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL

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 unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use unsloth/Jan-nano-128k-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL
```

Lemonade

How to use unsloth/Jan-nano-128k-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/Jan-nano-128k-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.Jan-nano-128k-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Jan-nano-128k-GGUF / README.md

danielhanchen

Create README.md

898e7a7 verified 12 months ago

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	---
	tags:
	- unsloth
	license: apache-2.0
	language:
	- en
	base_model:
	- Menlo/Jan-nano-128k
	pipeline_tag: text-generation
	---
	<div>
	<p style="margin-top: 0;margin-bottom: 0;">
	<em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
	</p>
	<div style="display: flex; gap: 5px; align-items: center; ">
	<a href="https://github.com/unslothai/unsloth/">
	<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
	</a>
	<a href="https://discord.gg/unsloth">
	<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
	</a>
	<a href="https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune">
	<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
	</a>
	</div>
	</div>


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

	[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue?logo=github)](https://github.com/menloresearch/deep-research)
	[![Context Length](https://img.shields.io/badge/Context-128k-green)](https://huggingface.co/Menlo/Jan-nano-128k)
	[![License](https://img.shields.io/badge/License-Apache%202.0-yellow)](https://opensource.org/licenses/Apache-2.0)

	<div align="center">
	<img src="https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/NP7CvcjOtLX8mST0t7eAM.png" width="300" alt="Jan-Nano-128k">
	</div>

	Authors: [Alan Dao](https://scholar.google.com/citations?user=eGWws2UAAAAJ&hl=en), [Bach Vu Dinh](https://scholar.google.com/citations?user=7Lr6hdoAAAAJ&hl=vi), [Thinh Le](https://scholar.google.com/citations?user=8tcN7xMAAAAJ&hl=en)

	## Overview

	Jan-Nano-128k represents a significant advancement in compact language models for research applications. Building upon the success of [Jan-Nano](https://huggingface.co/Menlo/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:

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/Bc0ehij86l_NX52OfxeOj.png)

	## 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-Nano Demo](replay.gif)

	Jan-Nano-128k is fully supported by [Jan - beta build](https://www.jan.ai/docs/desktop/beta), providing a seamless local AI experience with complete privacy and control.

	For additional tutorials and community guidance, visit our [Discussion Forums](https://huggingface.co/Menlo/Jan-nano-128k/discussions).

	### VLLM Deployment

	```bash
	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
	```

	Note: The chat template is included in the tokenizer. For troubleshooting, download the [Non-think chat template](https://qwen.readthedocs.io/en/latest/_downloads/c101120b5bebcc2f12ec504fc93a965e/qwen3_nonthinking.jinja).

	### Recommended Sampling Parameters

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

	## 🤝 Community & Support

	- Discussions: [HuggingFace Community](https://huggingface.co/Menlo/Jan-nano-128k/discussions)
	- Issues: [GitHub Repository](https://github.com/menloresearch/deep-research/issues)
	- Documentation: [Official Docs](https://menloresearch.github.io/deep-research/)

	## 📄 Citation

	```bibtex
	@model{jan-nano-128k,
	title={Jan-Nano-128k: Deep Research with Extended Context},
	author={Dao, Alan and Dinh, Bach Vu and Le Thinh},
	year={2024},
	url={https://huggingface.co/Menlo/Jan-nano-128k}
	}
	```

	---

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