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Model: janhq/Jan-v3-4B-base-instruct Source: Original Platform
2026-08-17 13:50:13 +08:00
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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-4B-Instruct-2507
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- code
+---
+# Jan-v3-4B-base-instruct: a 4B baseline model for fine-tuning
+
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue?logo=github)](https://github.com/janhq/jan) 
+[![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/) 
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/655e3b59d5c0d3db5359ca3c/A65FII_r3rAi9wZtK5P_v.png)
+
+## 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
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/655e3b59d5c0d3db5359ca3c/IGuQdKZ0_IGIwL0Wkcasi.png)
+
+## Quick Start
+
+### Integration with Jan Apps
+
+Jan-v3 demo is hosted on **Jan Browser** at **[chat.jan.ai](https://chat.jan.ai/)**. It is also optimized for direct integration with [Jan Desktop](https://jan.ai/), select the model in the app to start using it.
+
+
+### Local Deployment
+
+**Using vLLM:**
+```bash
+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:**
+```bash
+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:
+```yaml
+temperature: 0.7
+top_p: 0.8
+top_k: 20
+```
+
+## 🤝 Community & Support
+
+- **Discussions**: [Hugging Face Community](https://huggingface.co/janhq/Jan-v2-VL-8B/discussions) 
+- **Jan App**: Learn more about the Jan App at [jan.ai](https://jan.ai/)
+
+## 📄 Citation
+```bibtex
+Updated Soon
+```