81 lines
2.9 KiB
Markdown
81 lines
2.9 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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base_model:
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- Qwen/Qwen3-1.7B
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Jan-v1-edge: Distilled for Edge, Built for Web Search
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[](https://github.com/menloresearch/deep-research)
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[](https://opensource.org/licenses/Apache-2.0)
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[](https://jan.ai/)
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## Overview
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**Jan-v1-edge** is a lightweight agentic model built for fast, reliable on-device execution. As the second release in the **Jan Family**, it is distilled from the larger [`Jan-v1`](https://huggingface.co/janhq/Jan-v1-4B) model, preserving strong reasoning and problem-solving ability in a smaller footprint suitable for resource-constrained environments.
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Jan-v1-edge was developed through a two-phase post-training process. The first phase, **Supervised Fine-Tuning (SFT)**, transferred core capabilities from the `Jan-v1` teacher model to the smaller student. The second phase, **Reinforcement Learning with Verifiable Rewards (RLVR)** —the same method used in `Jan-v1` and `Lucy`—further optimized reasoning efficiency, tool use, and correctness. This staged approach delivers reliable results on complex, interactive workloads.
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## Performance
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### Question Answering(SimpleQA)
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Despite having only 1.7B parameters, **Jan-v1-edge** achieves 83% accuracy—nearly matching the larger Jan-nano-128k—demonstrating its efficiency and robustness.
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### Chat & Instruction Following
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Versus Qwen 3 1.7B Thinking, Jan-v1-edge shows a slight degradation on instruction-following and CreativeWriting, while remaining comparable or better on EQBench and recency QA.
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## Quick Start
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### Integration with Jan App
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Jan-v1-edge is optimized for direct integration with the [Jan App](https://jan.ai/). Simply select the model from the Jan App interface for immediate access to its full capabilities.
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### Local Deployment
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**Using vLLM:**
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```bash
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vllm serve janhq/Jan-v1-edge \
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--host 0.0.0.0 \
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--port 1234 \
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--enable-auto-tool-choice \
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--tool-call-parser hermes
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```
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**Using llama.cpp:**
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```bash
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llama-server --model Jan-v1-edge-Q8_0.gguf \
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--host 0.0.0.0 \
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--port 1234 \
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--jinja \
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--no-context-shift
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```
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### Recommended Inference Parameters
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```yaml
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temperature: 0.6
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top_p: 0.95
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top_k: 20
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min_p: 0.0
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max_tokens: 2048
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```
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## 🤝 Community & Support
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- **Discussions**: [HuggingFace Community](https://huggingface.co/janhq/Jan-v1-edge/discussions)
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- **Jan App**: Discover more about the Jan App at [jan.ai](https://jan.ai/)
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## 📄 Citation
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```bibtex
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Updated Soon
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``` |