77 lines
2.3 KiB
Markdown
77 lines
2.3 KiB
Markdown
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---
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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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- janhq/Jan-v3-4B-base-instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- agent
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---
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# Jan-Code-4B: a small code-tuned model
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[](https://github.com/janhq/jan)
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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-Code-4B** is a **code-tuned** model built on top of [Jan-v3-4B-base-instruct](https://huggingface.co/janhq/Jan-v3-4B-base-instruct). It’s designed to be a practical coding model you can run locally and iterate on quickly—useful for everyday code tasks and as a lightweight “worker” model in agentic workflows.
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Compared to larger coding models, Jan-Code focuses on handling **well-scoped subtasks** reliably while keeping latency and compute requirements small.
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## Intended Use
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* **Lightweight coding assistant** for generation, editing, refactoring, and debugging
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* **A small, fast worker model** for agent setups (e.g., as a sub-agent that produces patches/tests while a larger model plans)
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* **Replace Haiku model in Claude Code setup**
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## Quick Start
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### Integration with Jan Apps
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Jan-code is optimized for direct integration with [Jan Desktop](https://jan.ai/), select the model in the app to start using it.
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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-code-4b \
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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-code-4b-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 Parameters
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For optimal performance in agentic and general tasks, we recommend the following inference parameters:
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```yaml
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temperature: 0.7
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top_p: 0.8
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top_k: 20
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```
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## 🤝 Community & Support
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- **Discussions**: [Hugging Face Community](https://huggingface.co/janhq/Jan-code/discussions)
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- **Jan App**: Learn 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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```
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