Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

# 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)

**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.*