Jan-Nano-128k: Empowering deeper research through extended context understanding.
Authors: Alan Dao, Bach Vu Dinh, Thinh Le
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-Nano-128k is fully supported by Jan - beta build, providing a seamless local AI experience with complete privacy and control.
For additional tutorials and community guidance, visit our Discussion Forums.
VLLM Deployment
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.
Recommended Sampling Parameters
Temperature: 0.7
Top-p: 0.8
Top-k: 20
Min-p: 0.0
🤝 Community & Support
- Discussions: HuggingFace Community
- Issues: GitHub Repository
- Documentation: Official Docs
📄 Citation
@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.