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
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Download README.md from immanuelpeter/MiniMax-M3.1-Flash-Vision: direct link, hf CLI and curl.
- Browser
- Download file 2.52 kB
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https://huggingface.co/immanuelpeter/MiniMax-M3.1-Flash-Vision/resolve/main/README.md
- Command line
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hf download hf://immanuelpeter/MiniMax-M3.1-Flash-Vision/README.md
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curl -L -o README.md https://huggingface.co/immanuelpeter/MiniMax-M3.1-Flash-Vision/resolve/main/README.md
2.52 kB
| license: other | |
| license_name: minimax-community | |
| license_link: LICENSE | |
| pipeline_tag: image-feature-extraction | |
| library_name: transformers | |
| tags: | |
| - vision | |
| base_model: | |
| - MiniMaxAI/MiniMax-M3.1-Flash | |
| # MiniMax-M3.1-Flash Vision | |
| This repository packages the Tower and Projector from | |
| [MiniMax M3.1 Flash](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash). | |
| ## Contents | |
| | File | Tensors | What it holds | | |
| |---|---|---| | |
| | `model.safetensors` | 515 | MiniMax-M3.1-Flash vision Tower | | |
| | `projector.safetensors` | 8 | `multi_modal_projector` and remapped `patch_merge_mlp` | | |
| | `projector_config.json`, `projector.py` | | Projector shapes and loader | | |
| | `config.json`, `preprocessor_config.json` | | Vision-only model and image-processing configuration | | |
| | `export_manifest.json` | | Source pin, tensor counts and file hashes | | |
| | `LICENSE`, `LICENSE-CODE`, `NOTICE` | | Weight license, code license and attribution | | |
| ## Architecture | |
| | Component | Details | | |
| |---|---| | |
| | Tower | 32 layers, 1280 hidden, 16 heads, 5120 intermediate, patch size 14, 3D RoPE, image size 672 | | |
| | Token compression | 2x2 spatial merge, temporal patch size 2 | | |
| | Projector | `Linear(1280, 6144)`, GELU, `Linear(6144, 6144)`, flatten four 6144-wide patches, `Linear(24576, 6144)`, GELU, `Linear(6144, 6144)` | | |
| The class is `MiniMaxM3VLVisionModel`. Still images are repeated as two frames for the | |
| temporal patch embedding. At 448 pixels the example reports Tower `(1, 1024, 1280)`, | |
| merged `(1, 256, 5120)` and projected `(1, 256, 6144)`. | |
| ## Usage | |
| See [`examples/inference.py`](examples/inference.py) for image feature extraction. | |
| Forward one image per call: the native HF attention couples images in a single call. | |
| ## Validation | |
| The release tests compare all 515 Tower tensors and eight Projector tensors with the | |
| pinned parent checkpoint using `torch.equal`. Fixed-image Tower, merged and projected | |
| outputs also match the native parent implementation at 56 and 448 pixels. | |
| ## Reproduction | |
| The [export script](https://github.com/immanuel-peter/vision-tower-bench/blob/main/scripts/export_minimax_m3_vision.py) | |
| reads the Tower and Projector tensors from the pinned parent and writes the original | |
| tensors, preserving their dtypes. The script pins the parent revision. | |
| ## Credits | |
| MiniMax released the MiniMax-M3.1-Flash weights and the native Transformers implementation. | |
| ## License | |
| [MiniMax Community License](LICENSE), the same license as the source model. Helper code | |
| is MIT; see [LICENSE-CODE](LICENSE-CODE) and [NOTICE](NOTICE). | |