Abiray/MiniMax-M2.7-Q3_K_M-GGUF

---

Note: This is a Q3_K_M GGUF quantized version of the original MiniMaxAI/MiniMax-M2.7 model, packaged as a single massive file for seamless local deployment.

Join Our 💬 WeChat | 🧩 Discord community.

MiniMax Agent | ⚡️ API | MCP | MiniMax Website

🤗 Hugging Face | 🐙 GitHub | 🤖️ ModelScope | 📄 LICENSE

MiniMax-M2.7 is our first model deeply participating in its own evolution. M2.7 is capable of building complex agent harnesses and completing highly elaborate productivity tasks, leveraging Agent Teams, complex Skills, and dynamic tool search. For more details, see our blog post.

Model Self-Evolution

M2.7 initiates a cycle of model self-evolution: during development, we let the model update its own memory, build dozens of complex skills for RL experiments, and improve its own learning process based on experiment results. An internal version of M2.7 autonomously optimized a programming scaffold over 100+ rounds — analyzing failure trajectories, modifying code, running evaluations, and deciding to keep or revert — achieving a 30% performance improvement. On MLE Bench Lite (22 ML competitions), M2.7 achieved a 66.6% medal rate, second only to Opus-4.6 and GPT-5.4.

Professional Software Engineering

M2.7 delivers outstanding real-world programming capabilities spanning log analysis, bug troubleshooting, refactoring, code security, and machine learning. Beyond code generation, M2.7 demonstrates strong system-level reasoning — correlating monitoring metrics, conducting trace analysis, verifying root causes in databases, and making SRE-level decisions. Using M2.7, we have reduced live production incident recovery time to under three minutes on multiple occasions.

Professional Work

M2.7 achieved an ELO score of 1495 on GDPval-AA (highest among open-source models), surpassing GPT5.3. It handles Word, Excel, and PPT with high-fidelity multi-round editing, producing editable deliverables. On Toolathon, M2.7 reached 46.3% accuracy (global top tier), and maintains 97% skill compliance across 40+ complex skills on MM Claw.

Entertainment

M2.7 features strengthened character consistency and emotional intelligence. We open-sourced OpenRoom, an interactive demo that places AI interaction within a Web GUI space with real-time visual feedback and scene interactions. Try it at openroom.ai.

---

GGUF Local Deployment Guide (llama.cpp)

This repository contains the MiniMax-M2.7-Q3_K_M.gguf model packaged as a single, large file.

Hardware Note: Due to the extreme parameter count of this model, even the quantized versions require significant system resources. Ensure you have adequate system RAM or VRAM to offload the model layers.

Run with llama.cpp

You can run this model natively using llama.cpp by pointing it to the .gguf file.

# Example CLI inference
./llama-cli -m /path/to/MiniMax-M2.7-Q3_K_M.gguf \
  -p "You are a helpful assistant. Your name is MiniMax-M2.7 and is built by MiniMax." \
  -n 512 -c 4096