Instructions to use immanuelpeter/MiniMax-M3.1-Flash-Vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use immanuelpeter/MiniMax-M3.1-Flash-Vision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="immanuelpeter/MiniMax-M3.1-Flash-Vision")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("immanuelpeter/MiniMax-M3.1-Flash-Vision") model = AutoModel.from_pretrained("immanuelpeter/MiniMax-M3.1-Flash-Vision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 529 Bytes
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Extracted from MiniMaxAI/MiniMax-M3.1-Flash at 54e4e69dfc452b1e94a426c24a4fdeeaa8e3ad5e.
Copyright MiniMax. Distributed under the accompanying MiniMax Community License.
Tower and Projector tensors are preserved exactly. State-dictionary keys are adapted for native Transformers classes.
This is a standalone vision extraction, not the complete language model.
projector.py and examples/inference.py are original vision-tower-bench helper code.
Copyright (c) 2026 Immanuel Peter. MIT; see LICENSE-CODE.
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