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chore(model gallery): 🤖 add new models via gallery agent
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gallery/index.yaml

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- filename: ReForm-32B.i1-Q4_K_M.gguf
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sha256: a7f69d6e2efe002368bc896fc5682d34a1ac63669a4db0f42faf44a29012dc3f
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uri: huggingface://mradermacher/ReForm-32B-i1-GGUF/ReForm-32B.i1-Q4_K_M.gguf
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- !!merge <<: *qwen25coder
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name: "minimax-m2"
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urls:
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- https://huggingface.co/mradermacher/MiniMax-M2-GGUF
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description: |
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**MiniMax-M2**
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*Open-source, lightweight MoE model for elite coding and agent workflows*
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MiniMax-M2 is a highly efficient, open-source **Mixture-of-Experts (MoE)** model optimized for **coding and agentic tasks**. With **230 billion total parameters** and only **10 billion active parameters at inference**, it delivers elite performance in code generation, multi-step tool use, and complex task planning—while maintaining low latency, high throughput, and cost-effective deployment.
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🔥 **Key Highlights**:
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- ✅ **#1 in open-source intelligence** (per Artificial Analysis benchmarks)
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- ✅ **Top-tier performance** on SWE-bench, Terminal-Bench, BrowseComp, and GAIA
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- ✅ Built for **end-to-end agent workflows**: planning, tool use, and execution
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- ✅ **Interleaved thinking** architecture: preserves reasoning traces for better accuracy
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- ✅ Ideal for local inference via **vLLM, SGLang, or MLX**
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🎯 **Best for**: Developers, AI agents, and researchers building responsive, intelligent tool-using systems—without the cost of large models.
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📄 **Model Repo**: [MiniMaxAI/MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2)
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🔗 **Free API & Agent**: [agent.minimax.io](https://agent.minimax.io/)
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💡 **Use Case**: IDE assistants, automated testing, browser agents, CI/CD automation, and more.
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*“Small footprint, big brain—perfect for real-world agentic AI.”*
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overrides:
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parameters:
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model: MiniMax-M2.Q4_K_S.gguf.part1of3
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files:
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- filename: MiniMax-M2.Q4_K_S.gguf.part1of3
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sha256: 7fb66d3be2d10d13960d16984ee2d8e930e90fd6a77bb1f69089a486a80d6e23
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uri: huggingface://mradermacher/MiniMax-M2-GGUF/MiniMax-M2.Q4_K_S.gguf.part1of3

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