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README.md
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README.md
@@ -3,7 +3,7 @@ license: apache-2.0
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language:
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- en
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base_model:
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- Qwen/Qwen3-4B-Thinking-2507
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- janhq/Jan-v1-4B
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pipeline_tag: text-generation
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---
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# Jan-v1: Advanced Agentic Language Model
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@@ -17,24 +17,24 @@ pipeline_tag: text-generation
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## Overview
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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.
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**Jan-v1** is the first release in the **Jan Family**, designed for agentic reasoning and problem-solving within the [Jan App](https://jan.ai/). Based on our [**Lucy**](https://huggingface.co/Menlo/Lucy) model, Jan-v1 achieves improved performance through model scaling.
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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.
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Jan-v1 uses the [Qwen3-4B-thinking](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) model to provide enhanced reasoning capabilities and tool utilization. This architecture delivers better performance on complex agentic tasks.
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## Performance
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### Question Answering (SimpleQA)
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For question-answering, Jan-v1 shows a significant performance gain from model scaling, achieving 91.2% accuracy.
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For question-answering, Jan-v1 shows a significant performance gain from model scaling, achieving 91.1% accuracy.
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*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.*
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*The 91.1% 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.*
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### Chat Benchmarks
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These benchmarks evaluate the model's conversational and instructional capabilities.
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## Quick Start
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@@ -48,7 +48,7 @@ Jan-v1 is optimized for direct integration with the [Jan App](https://jan.ai/).
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**Using vLLM:**
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```bash
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vllm serve Menlo/Jan-v1 \
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vllm serve janhq/Jan-v1-4B \
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--host 0.0.0.0 \
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--port 1234 \
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--enable-auto-tool-choice \
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@@ -59,7 +59,9 @@ vllm serve Menlo/Jan-v1 \
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```bash
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llama-server --model jan-v1.gguf \
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--host 0.0.0.0 \
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--port 1234
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--port 1234 \
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--jinja \
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--no-context-shift
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```
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### Recommended Parameters
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@@ -75,9 +77,13 @@ max_tokens: 2048
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## 🤝 Community & Support
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- **Discussions**: [HuggingFace Community](https://huggingface.co/Menlo/Jan-v1/discussions) <!-- Update with your HF model ID -->
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- **Discussions**: [HuggingFace Community](https://huggingface.co/janhq/Jan-v1-4B/discussions) <!-- Update with your HF model ID -->
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- **Jan App**: Learn more about the Jan App at [jan.ai](https://jan.ai/)
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## (*) Note
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By default we have system prompt in chat template, this is to make sure the model having the same performance with the benchmark result. You can also use the vanilla chat template without system prompt in the file [chat_template_raw.jinja](https://huggingface.co/janhq/Jan-v1-4B/blob/main/chat_template_raw.jinja).
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## 📄 Citation
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```bibtex
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Updated Soon
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