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MiniMax-01

MiniMax-AI/MiniMax-01

Repository for MiniMax-Text-01 and MiniMax-VL-01 models based on Linear Attention

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

44views this month

3.5k stars332 forksLast push Jul 7, 2025 Python MIT

Decision brief

MiniMax-01 optimizes Linear Attention for large-language and vision-language models.

Good fit when

  • When high throughput performance is required for model serving
  • For efficient memory management and batch request handling

Avoid when

  • If deep customization of attention mechanisms aside from Linear Attention is needed
  • In favor of frameworks lacking vLLM's optimization features, when efficiency or memory use are secondary to flexibility

Observed Jul 14, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report
Maintenance
Dormant (406d since push)
As of Aug 18, 2026
Provenance
Not a fork · Organization account
As of Aug 18, 2026
Security (OSV)
No lockfile
As of Jul 11, 2026

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Backing

Company context for MiniMax. Display-only - separate from trust and ranking.

Company
MiniMax·GitHub org profile·Jul 11, 2026
Commercial model
Pure OSS·GitHub org profile (public repos)·Jul 11, 2026

Install

pip install MiniMax-01
PyPI

How it fits your stack(7)

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Evidence and technical details

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Overview

Offers deployment guides for production use via vLLM or Transformers for miniaturized large language and vision-language models.

Capability facts

Languages
python

Source: github.language · Aug 18, 2026

Categories

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README

5. Deployment Guide For production deployment, we recommend using vLLM to serve MiniMax Text 01 and MiniMax VL 01. vLLM provides excellent performance for serving large language models with the following features: 🔥 Outstanding service throughput performance ⚡ Efficient and inte...

For agents

This page has a .md twin and JSON over the API.

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