license, language, base_model, pipeline_tag, library_name, tags
| license | language | base_model | pipeline_tag | library_name | tags | |||
|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
|
text-generation | transformers |
|
Jan-v3-4B-base-instruct: a 4B baseline model for fine-tuning
Overview
Jan-v3-4B-base-instruct is a 4B-parameter model obtained via post-training distillation from a larger teacher, transferring capabilities while preserving general-purpose performance on standard benchmarks. The result is a compact, ownable base that is straightforward to fine-tune, broadly applicable and minimizing the usual capacity–capability trade-offs.
Building on this base, Jan-Code, a code-tuned variant, will be released soon.
Model Overview
Note: Jan-v3-4B-base-instruct inherits its core architecture from Qwen/Qwen3-4B-Instruct-2507.
- Number of Parameters: 4.0B
- Number of Parameters (Non-Embedding): 3.6B
- Number of Layers: 36
- Number of Attention Heads (GQA): 32 for Q and 8 for KV
- Context Length: 262,144 natively.
Intended Use
- A better small base for downstream work: improved instruction following out of the box, strong starting point for fine-tuning, and effective lightweight coding assistance.
Performance
Quick Start
Integration with Jan Apps
Jan-v3 demo is hosted on Jan Browser at chat.jan.ai. It is also optimized for direct integration with Jan Desktop, select the model in the app to start using it.
Local Deployment
Using vLLM:
vllm serve janhq/Jan-v3-4B-base-instruct \
--host 0.0.0.0 \
--port 1234 \
--enable-auto-tool-choice \
--tool-call-parser hermes
Using llama.cpp:
llama-server --model Jan-v3-4B-base-instruct-Q8_0.gguf \
--host 0.0.0.0 \
--port 1234 \
--jinja \
--no-context-shift
Recommended Parameters
For optimal performance in agentic and general tasks, we recommend the following inference parameters:
temperature: 0.7
top_p: 0.8
top_k: 20
🤝 Community & Support
- Discussions: Hugging Face Community
- Jan App: Learn more about the Jan App at jan.ai
📄 Citation
Updated Soon