MiniMax-M2.7-GGUF

AaryanK/MiniMax-M2.7-GGUF

MiniMax-M2.7-GGUF is a text generation model from AaryanK. Use it when you need the model to write or continue text. It is set up for gguf. The card lists the license as other.

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Hugging Face

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.gguf1.2 TB · 100%

At a glance

Task: Text Generation
Library: gguf
License: other
Access: Public
Created: Apr 12, 2026
Updated: Apr 12, 2026
SHA: b3b8677a

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Base models

MiniMaxAI/MiniMax-M2.7

Model card

From the Hugging Face model README

MiniMax-M2.7-GGUF

Description

This repository contains GGUF format model files for MiniMaxAI's MiniMax-M2.7.

MiniMax-M2.7 is an advanced model designed for complex agent harnesses, model self-evolution, and professional software engineering. It is capable of executing elaborate productivity tasks, leveraging native Agent Teams for multi-agent collaboration, and performing system-level reasoning for real-world programming challenges.

Evaluation Results

Benchmark Overview

Agent Teams Demonstration

Files & Quantization

To see the available files, please verify the Files and versions tab.

How to Run (llama.cpp)

Recommended Parameters: The creators recommend the following parameters for the best performance:

Temperature: 1.0
Top-P: 0.95
Top-K: 40
Context: -c (Adjust based on your VRAM/RAM).

CLI Example

./llama-cli -m MiniMax-M2.7.Q4_K_M.gguf \
  -c 8192 \
  --temp 1.0 \
  --top-p 0.95 \
  --top-k 40 \
  -p "You are a helpful assistant. Your name is MiniMax-M2.7 and is built by MiniMax.\n\nUser: Write a Python script to automate a database backup.\nAssistant:" \
  -cnv

Server Example

./llama-server -m MiniMax-M2.7.Q4_K_M.gguf \
  --port 8080 \
  --host 0.0.0.0 \
  -c 16384 \
  -ngl 99

Original Model Card: MiniMaxAI/MiniMax-M2.7

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.

On SWE-Pro, M2.7 achieved 56.22%, matching GPT-5.3-Codex, with even stronger performance on real-world engineering benchmarks: SWE Multilingual (76.5) and Multi SWE Bench (52.7). On VIBE-Pro (55.6%), M2.7 is nearly on par with Opus 4.6. On Terminal Bench 2 (57.0%) and NL2Repo (39.8%), M2.7 demonstrates deep understanding of complex engineering systems. M2.7 also supports native Agent Teams for multi-agent collaboration with stable role identity and autonomous decision-making.

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. On the MM Claw end-to-end benchmark, M2.7 achieved 62.7%, close to Sonnet 4.6.

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.

How to Use

MiniMax Agent: https://agent.minimax.io/
MiniMax API: https://platform.minimax.io/
Token Plan: https://platform.minimax.io/subscribe/token-plan

Local Deployment Guide

Download the model from HuggingFace repository: https://huggingface.co/MiniMaxAI/MiniMax-M2.7

We recommend using the following inference frameworks (listed alphabetically) to serve the model:

SGLang

We recommend using SGLang to serve MiniMax-M2.7. Please refer to our SGLang Deployment Guide.

vLLM

We recommend using vLLM to serve MiniMax-M2.7. Please refer to our vLLM Deployment Guide.

Transformers

We recommend using Transformers to serve MiniMax-M2.7. Please refer to our Transformers Deployment Guide.

ModelScope

You also can get model weights from modelscope.

NVIDIA NIM

MiniMax M2.7 is also available on NVIDIA NIM Endpoint.

Inference Parameters

We recommend using the following parameters for best performance: temperature=1.0, top_p = 0.95, top_k = 40. Default system prompt:

You are a helpful assistant. Your name is MiniMax-M2.7 and is built by MiniMax.

Tool Calling Guide

Please refer to our Tool Calling Guide.

Contact Us

Task: Text Generation
Library: gguf
License: other
Languages: en, zh
Created: Apr 12, 2026
Updated: Apr 12, 2026