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Model: janhq/Jan-v1-edge Source: Original Platform
2026-06-15 18:34:12 +08:00
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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-1.7B
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Jan-v1-edge: Distilled for Edge, Built for Web Search
+
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue?logo=github)](https://github.com/menloresearch/deep-research)
+[![License](https://img.shields.io/badge/License-Apache%202.0-yellow)](https://opensource.org/licenses/Apache-2.0)
+[![Jan App](https://img.shields.io/badge/Powered%20by-Jan%20App-purple?style=flat&logo=android)](https://jan.ai/)
+
+## Overview
+
+**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.
+
+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.
+
+## Performance
+
+### Question Answering(SimpleQA)
+
+Despite having only 1.7B parameters, **Jan-v1-edge** achieves 83% accuracy—nearly matching the larger Jan-nano-128k—demonstrating its efficiency and robustness.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/655e3b59d5c0d3db5359ca3c/gV6Ph1m3rW6KeYkpj_b4s.png)
+
+### Chat & Instruction Following
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/655e3b59d5c0d3db5359ca3c/xNWL41L__oULHJkuAaGGt.png)
+
+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.
+
+## Quick Start
+
+### Integration with Jan App
+
+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.
+
+### Local Deployment
+
+**Using vLLM:**
+```bash
+vllm serve janhq/Jan-v1-edge \
+    --host 0.0.0.0 \
+    --port 1234 \
+    --enable-auto-tool-choice \
+    --tool-call-parser hermes
+    
+```
+
+**Using llama.cpp:**
+```bash
+llama-server --model Jan-v1-edge-Q8_0.gguf \
+    --host 0.0.0.0 \
+    --port 1234 \
+    --jinja \
+    --no-context-shift
+```
+
+### Recommended Inference Parameters
+```yaml
+temperature: 0.6
+top_p: 0.95
+top_k: 20
+min_p: 0.0
+max_tokens: 2048
+```
+
+## 🤝 Community & Support
+
+-   **Discussions**: [HuggingFace Community](https://huggingface.co/janhq/Jan-v1-edge/discussions)
+-   **Jan App**: Discover more about the Jan App at [jan.ai](https://jan.ai/)
+
+## 📄 Citation
+```bibtex
+Updated Soon
+```