OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Models

Minimax 01

Minimax 01 is the public Python repository for MiniMax-Text-01, a long-context language model, and MiniMax-VL-01, a vision-language model. It provides architecture information and a Transformers quickstart for text generation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Minimax 01 — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use the repository when you need model architecture details or a code-based path to load MiniMax-Text-01 for inference. For image-related work, consult its MiniMax-VL-01 documentation.

Who it is for

  • Developers preparing to run or adapt the documented models.
  • Researchers examining the models’ architectures and training approaches.
  • Teams exploring long-context text processing or vision-language applications.

Documented uses

  • Review the architectures of MiniMax-Text-01 and MiniMax-VL-01.
  • Configure and load MiniMax-Text-01 for text generation using the Python quickstart.
  • Evaluate MiniMax-VL-01 as the repository’s vision-language model for image-related tasks.
Before you choose

Requirements & limits

  • The provided text-generation example assumes eight GPUs and uses int8 quantization with bfloat16 model loading.
  • The code is listed under MIT, while the repository links a separate model agreement; check the model terms independently.
  • The supplied quickstart excerpt covers MiniMax-Text-01, not a runnable MiniMax-VL-01 inference procedure.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Load MiniMax-Text-01 for text generation

Follow the repository’s Python quickstart to configure and load MiniMax-Text-01 and generate a short response.

Before you start

  • Python environment with the Transformers, PyTorch and Quanto components used by the example.
  • Access to the MiniMaxAI/MiniMax-Text-01 model files.
  • Eight GPUs, as assumed by the example.

Steps

  1. Load the model configuration and use it to calculate a layer-to-device map for eight GPUs.

  2. Configure int8 quantization, leaving the listed embedding, output-head, coefficient and MoE gate modules unconverted.

  3. Load the tokenizer and format a user prompt with the chat template.

  4. Load the model in bfloat16 with the device map and quantization settings, then call generation with the configured token limit.

Expected outcome

A generated text response from MiniMax-Text-01 using the example’s quantized, multi-GPU setup.

Guide limitations

  • The example assumes eight GPUs and does not provide a smaller-device setup.
  • The repository identifies a separate model agreement in addition to the MIT code license.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
MiniMax-AI/MiniMax-01 · Official homepage title · retained in its source language
How we review sourcesSuggest a correction