---
title: Jan-nano
canonical_url: "https://www.modelscope.cn/models/unsloth/Jan-nano"
md_url: "https://www.modelscope.cn/models/unsloth/Jan-nano.md"
repository: unsloth/Jan-nano
last_updated: 2025-06-16
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen3
architectures:
  - Qwen3ForCausalLM
base_model:
  - Menlo/Jan-nano
base_model_relation: finetune
parameters: 4.0B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 311
stars: 0
tags:
  - unsloth
---

# Jan-nano

> Jan-nano - unsloth 在 ModelScope 开源的模型。Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

unsloth/Jan-nano 是 ModelScope 魔搭社区上的 4.0B 参数text-generation模型，采用 apache-2.0 许可，基于 Menlo/Jan-nano 构建，可用 deploy_task text/emb、lmdeploy_turbomind 0.9.1、sglang 0.5.2 部署。

- **Repository**: unsloth/Jan-nano
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 4.0B
- **Base model**: Menlo/Jan-nano
- **Inference backends**: deploy_task text/emb, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: unsloth
- **Downloads**: 311
- **Stars**: 0
- **Last updated**: 2025-06-16

Source: https://www.modelscope.cn/models/unsloth/Jan-nano

---

<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: A 4B MCP-Optimized DeepResearch Model
[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue?logo=github)](https://github.com/menloresearch/deep-research) 

<div align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/wC7Xtolp7HOFIdKTOJhVt.png" width="300" alt="Jan-Nano">
</div>

## Overview

Jan-Nano is a compact 4-billion parameter language model specifically designed and trained for deep research tasks. This model has been optimized to work seamlessly with Model Context Protocol (MCP) servers, enabling efficient integration with various research tools and data sources.

## Evaluation
Jan-Nano has been evaluated on the SimpleQA benchmark using our MCP-based benchmark methodology, demonstrating strong performance for its model size:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/sdRfF9FX5ApPow9gZ31No.png)

The evaluation was conducted using our MCP-based benchmark approach, which assesses the model's performance on SimpleQA tasks while leveraging its native MCP server integration capabilities. This methodology better reflects Jan-Nano's real-world performance as a tool-augmented research model, validating both its factual accuracy and its effectiveness in MCP-enabled environments.

## How to Run Locally

![Jan-Nano Demo](replay.gif)

Jan-Nano is supported by [Jan]((https://github.com/menloresearch/jan)), an open-source ChatGPT alternative that runs entirely on your computer. Jan provides a user-friendly interface for running local AI models with full privacy and control.