Jan-v1-AIO-GGUF

Jan-v1-4B is a 4-billion-parameter language model built on the Qwen3-4B-thinking architecture, meticulously fine-tuned for agentic reasoning, problem-solving, and tool utilization with support for web search tasks and large context lengths up to 256,000 tokens. Achieving 91.1% accuracy on the SimpleQA benchmark, Jan-v1-4B excels at factual question answering and conversation while running efficiently on local hardware for enhanced privacy and offline use, making it a strong choice for advanced Q&A, reasoning, and integration with the Jan desktop application or compatible inference engines. 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 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.

Jan-v1 GGUF Models

Model Files

Jan-v1-2509

Jan-v1-edge

Jan-v1-4B

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):