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
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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 PyPIHow 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
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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.