janhq/Jan-v3-4B-base-instruct
Jan-v3-4B-base-instruct is a 4 billion parameter instruction-tuned model developed by janhq, derived from Qwen/Qwen3-4B-Instruct-2507 via post-training distillation. It features a native context length of 262,144 tokens and is designed as a compact, ownable base for fine-tuning. This model offers improved instruction following and serves as an effective lightweight coding assistant, minimizing capacity-capability trade-offs.
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Overview
Jan-v3-4B-base-instruct is a 4 billion parameter instruction-tuned model developed by janhq, built upon the core architecture of Qwen/Qwen3-4B-Instruct-2507. This model was created through post-training distillation from a larger teacher model, allowing it to transfer advanced capabilities while maintaining strong general-purpose performance on standard benchmarks. It is designed to be a compact and easily fine-tunable base model, aiming to reduce the typical compromises between model size and capability.
Key Capabilities
- Compact and Efficient: A 4B parameter model (3.6B non-embedding) with 36 layers, making it suitable for resource-constrained environments.
- Extended Context Length: Features a notable native context length of 262,144 tokens.
- Improved Instruction Following: Offers enhanced instruction adherence out-of-the-box.
- Strong Fine-tuning Base: Provides a robust starting point for downstream fine-tuning tasks.
- Lightweight Coding Assistance: Effective for basic coding support.
Intended Use Cases
- Serving as a superior small base model for various downstream applications.
- Applications requiring improved instruction following capabilities.
- Projects needing a strong foundation for further fine-tuning.
- Use cases benefiting from lightweight coding assistance.