Instructions to use unsloth/Jan-nano-128k-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
- Pi
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
File size: 5,218 Bytes
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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.
[](https://github.com/menloresearch/deep-research)
[](https://huggingface.co/Menlo/Jan-nano-128k)
[](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:**
- **๐ Research Deeper**: Extended context allows for processing entire research papers, lengthy documents, and complex multi-turn conversations
- **โก Native 128k Window**: Built from the ground up to handle long contexts efficiently, maintaining performance across the full context range
- **๐ 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.
## ๐ฅ๏ธ How to Run Locally

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
```
## ๐ค 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/)
## ๐ 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.* |