Here’s an enhanced blog-post-style overview of the Jan‑v1‑4B model from the Hugging Face Hub:
Key points
- Jan-v1-4B is the inaugural model in the Jan family, built for agentic reasoning and problem-solving within the Jan App, an AI assistant platform from Menlo Research.
- It is fine-tuned from the Lucy model architecture and powered by Qwen3-4B-thinking for advanced reasoning and tool integration.
- On the SimpleQA factual question answering benchmark, Jan-v1 achieves 91.1% accuracy.
- Local deployment is supported through vLLM and llama.cpp, and a GGUF version offers multiple quantization options in 4-bit, 5-bit, 6-bit, and 8-bit formats.
- The recommended inference settings are temperature 0.6, top_p 0.95, top_k 20, min_p 0.0, and max_tokens 2048.
Overview
Jan-v1-4B is the inaugural model in the Jan family—crafted for agentic reasoning and problem-solving within the Jan App, an AI assistant platform from Menlo Research. It is fine-tuned from the Lucy model architecture, leveraging superior model scaling for enhanced performance
(Hugging Face).
Powered by Qwen3‑4B‑thinking, this model is designed for advanced reasoning and robust tool integration capabilities, making it well-suited for complex, multi-step tasks
(Hugging Face).
Performance Highlights
- On the SimpleQA benchmark—a factual question answering measure—Jan‑v1 achieves an impressive 91.1% accuracy, marking a notable milestone for models of this size
(Hugging Face). - It also performs strongly on chat and instructional benchmarks, showcasing balanced conversational abilities
(Hugging Face).
Getting Started
Integration with Jan App
Users can seamlessly access Jan‑v1 by selecting it from the Jan App interface—no additional setup required
(Hugging Face).
Local Deployment
To run Jan‑v1 locally, two popular frameworks are supported:
- vLLM
vllm serve janhq/Jan-v1-4B \ --host 0.0.0.0 \ --port 1234 \ --enable-auto-tool-choice \ --tool-call-parser hermes - llama.cpp
llama-server --model Jan-v1-4B-Q4_K_M.gguf \ --host 0.0.0.0 \ --port 1234 \ --jinja \ --no-context-shift
Recommended Inference Settings
Users are advised to apply the following parameters for optimal performance:
temperature: 0.6
top_p: 0.95
top_k: 20
min_p: 0.0
max_tokens: 2048
Quantization Variant
There’s also a GGUF-formatted version—denoted Jan‑v1‑4B‑GGUF—which offers multiple quantization options (4-bit, 5-bit, 6-bit, 8-bit), useful for efficient local deployment
(Hugging Face).
Summary
| Attribute | Description |
|---|---|
| Model Type | Open-source, 4-billion parameter agentic LLM |
| Architecture | Lucy-based, leveraging Qwen3-4B-thinking |
| Benchmarks | 91.1% SimpleQA accuracy; strong chat/instructional performance |
| Deployment | Integrated in Jan App; local support via vLLM and llama.cpp |
| Settings | Recommended inference parameters (temp, top_p/k, etc.) |
| Quant Variant | GGUF version with efficient quantization support |
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