93 lines
3.9 KiB
Plaintext
93 lines
3.9 KiB
Plaintext
---
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title: "Jan v0.6.7: OpenAI gpt-oss support and enhanced MCP tutorials"
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version: 0.6.7
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description: "Full support for OpenAI's open-weight gpt-oss models and new Jupyter MCP integration guide"
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date: 2025-08-07
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ogImage: "/assets/images/changelog/gpt-oss-serper.png"
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---
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import ChangelogHeader from "@/components/Changelog/ChangelogHeader"
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import { Callout } from 'nextra/components'
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<ChangelogHeader title="Jan v0.6.7: OpenAI gpt-oss support and enhanced MCP tutorials" date="2025-08-07" ogImage="/assets/images/changelog/gpt-oss-serper.png"/>
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## Highlights 🎉
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Jan v0.6.7 brings full support for OpenAI's groundbreaking open-weight models - gpt-oss-120b and gpt-oss-20b - along with enhanced MCP documentation and critical bug fixes for reasoning models.
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### 🚀 OpenAI gpt-oss Models Now Supported
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Jan now fully supports OpenAI's first open-weight language models since GPT-2:
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**gpt-oss-120b:**
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- 117B total parameters, 5.1B active per token
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- Runs efficiently on a single 80GB GPU
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- Near-parity with OpenAI o4-mini on reasoning benchmarks
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- Exceptional tool use and function calling capabilities
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**gpt-oss-20b:**
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- 21B total parameters, 3.6B active per token
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- Runs on edge devices with just 16GB memory
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- Similar performance to OpenAI o3-mini
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- Perfect for local inference and rapid iteration
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<Callout type="info">
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Both models use Mixture-of-Experts (MoE) architecture and support context lengths up to 128k tokens. They come natively quantized in MXFP4 format for efficient memory usage.
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</Callout>
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### 🎮 GPU Layer Configuration
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Due to the models' size, you may need to adjust GPU layers based on your hardware:
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Start with default settings and reduce layers if you encounter out-of-memory errors. Each system requires different configurations based on available VRAM.
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### 📚 New Jupyter MCP Tutorial
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We've added comprehensive documentation for the Jupyter MCP integration:
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- Real-time notebook interaction and code execution
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- Step-by-step setup with Python environment management
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- Example workflows for data analysis and visualization
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- Security best practices for code execution
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- Performance optimization tips
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The tutorial demonstrates how to turn Jan into a capable data science partner that can execute analysis, create visualizations, and iterate based on actual results.
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### 🔧 Bug Fixes
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Critical fixes for reasoning model support:
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- **Fixed reasoning text inclusion**: Reasoning text is no longer incorrectly included in chat completion requests
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- **Fixed thinking block display**: gpt-oss thinking blocks now render properly in the UI
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- **Fixed React state loop**: Resolved infinite re-render issue with useMediaQuery hook
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## Using gpt-oss Models
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### Download from Hub
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All gpt-oss GGUF variants are available in the Jan Hub. Simply search for "gpt-oss" and choose the quantization that fits your hardware:
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### Model Capabilities
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Both models excel at:
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- **Reasoning tasks**: Competition coding, mathematics, and problem solving
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- **Tool use**: Web search, code execution, and function calling
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- **CoT reasoning**: Full chain-of-thought visibility for monitoring
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- **Structured outputs**: JSON schema enforcement and grammar constraints
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### Performance Tips
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- **Memory requirements**: gpt-oss-120b needs ~80GB, gpt-oss-20b needs ~16GB
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- **GPU layers**: Adjust based on your VRAM (start high, reduce if needed)
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- **Context size**: Both models support up to 128k tokens
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- **Quantization**: Choose lower quantization for smaller memory footprint
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## Coming Next
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We're continuing to optimize performance for large models, expand MCP integrations, and improve the overall experience for running cutting-edge open models locally.
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Update your Jan or [download the latest](https://jan.ai/).
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For the complete list of changes, see the [GitHub release notes](https://github.com/janhq/jan/releases/tag/v0.6.7).
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