From 868d5588ac1a85858846a71fddfdb1f30a6ef179 Mon Sep 17 00:00:00 2001
From: "Jan (Homebrew Research)" <jan-hq@users.noreply.huggingface.co>
Date: Mon, 11 Aug 2025 14:54:17 +0000
Subject: [PATCH] Create README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Menlo/Jan-v1-4B
+pipeline_tag: text-generation
+---
+# Jan-v1: Advanced Agentic Language Model
+
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue?logo=github)](https://github.com/menloresearch/deep-research) 
+[![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/) 
+
+<!-- Optional: If you have a GIF for Jan-v1, include it here like Lucy's. -->
+<!-- ![image/gif](jan_v1_demo.gif) -->
+
+## Overview
+
+Introducing **Jan-v1**, the first release in the **Jan Family** – specifically designed for advanced agentic reasoning and complex problem-solving within the [Jan App](https://jan.ai/). Building on the innovative agentic capabilities of our earlier [**Lucy** ](https://huggingface.co/Menlo/Lucy) model, Jan-v1 represents a significant leap forward through strategic model scaling.
+
+Jan-v1 leverages the newly released [Qwen3-4B-thinking](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) model to deliver significantly enhanced reasoning capabilities and effective tool utilizatio. This architectural evolution is designed to deliver superior performance on complex agentic tasks, setting a new benchmark for accessible, high-performance AI.
+
+## Performance
+
+### Question Answering (SimpleQA) 
+For question-answering, Jan-v1 shows a significant performance gain from model scaling, achieving 91.2% accuracy.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/xuDDHjPnqzS_eziwShmBq.png)
+
+*The 91.2% SimpleQA accuracy represents a significant milestone in factual question answering for models of this scale, demonstrating the effectiveness of our scaling and fine-tuning approach.*
+
+### Report Generation & Factuality
+Evaluated on a benchmark testing factual report generation from web sources, using an LLM-as-judge. The benchmark includes our proprietary `Jan Exam - Longform` and the `DeepResearchBench`.
+
+| Model | Average Overall Score |
+| :--- | :--- |
+| o4-mini | 7.30 |
+| **Jan-v1-4B (Ours)** | **7.17** |
+| gpt-4.1 | 6.90 |
+| Qwen3-4B-Thinking-2507 | 6.84 |
+| 4o-mini | 6.60 |
+| Jan-nano-128k | 5.63 |
+## Quick Start
+
+### Integration with Jan App
+
+Jan-v1 is optimized for direct integration with the Jan App. Simply select the model from the Jan App interface for immediate access to its full capabilities.
+
+### Local Deployment
+
+**Using vLLM:**
+```bash
+vllm serve Menlo/Jan-v1 \
+    --host 0.0.0.0 \
+    --port 1234 \
+    --enable-auto-tool-choice \
+    --tool-call-parser hermes
+```
+
+**Using llama.cpp:**
+```bash
+llama-server --model jan-v1.gguf \
+    --host 0.0.0.0 \
+    --port 1234
+```
+
+### Recommended Parameters
+
+```yaml
+temperature: 0.6
+top_p: 0.95
+top_k: 20
+min_p: 0.0
+max_tokens: 2048
+```
+
+
+## 🤝 Community & Support
+
+- **Discussions**: [HuggingFace Community](https://huggingface.co/Menlo/Jan-v1/discussions) <!-- Update with your HF model ID -->
+- **Jan App**: Learn more about the Jan App at [jan.ai](https://jan.ai/)
+
+## 📄 Citation
+```bibtex
+Updated Soon
+```
+---
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