File size: 3,002 Bytes
1ed505d 83e745e 1ed505d f4ed4d6 1ed505d f22857d 1ed505d e2db6d1 1ed505d e2db6d1 1ed505d e2db6d1 1ed505d e2db6d1 1ed505d e2db6d1 1ed505d e2db6d1 1ed505d 0e3a593 1ed505d fb299bf 1ed505d e2db6d1 1ed505d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
---
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
[](https://github.com/menloresearch/deep-research)
[](https://opensource.org/licenses/Apache-2.0)
[](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.
### Chat & Instruction Following
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
``` |