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
Download export_manifest.json from immanuelpeter/MiniMax-M3.1-Flash-Vision: direct link, hf CLI and curl.
- Browser
- Download file 1.51 kB
-
https://huggingface.co/immanuelpeter/MiniMax-M3.1-Flash-Vision/resolve/main/export_manifest.json
- Command line
-
hf download hf://immanuelpeter/MiniMax-M3.1-Flash-Vision/export_manifest.json
-
curl -L -o export_manifest.json https://huggingface.co/immanuelpeter/MiniMax-M3.1-Flash-Vision/resolve/main/export_manifest.json
1.51 kB
| { | |
| "source_repo": "MiniMaxAI/MiniMax-M3.1-Flash", | |
| "source_revision": "54e4e69dfc452b1e94a426c24a4fdeeaa8e3ad5e", | |
| "source_shards": [ | |
| "model-00013-of-00048.safetensors", | |
| "model-00048-of-00048.safetensors" | |
| ], | |
| "dtype": "mixed", | |
| "source_dtypes_preserved": true, | |
| "tower_tensors": 515, | |
| "projector_tensors": 8, | |
| "tensor_dtypes": { | |
| "F32": 1, | |
| "BF16": 522 | |
| }, | |
| "files_sha256": { | |
| "LICENSE": "8c4978e3e0fe0349ff929cc8450463cba3039c4c9fc144d8bd5382ab00df247a", | |
| "LICENSE-CODE": "149f05ce186198084c9abd6af8e76e108ba7e507446a19ee5a2ffd694f69f525", | |
| "NOTICE": "19d19379d439b6dc2614476bb50b170fbe382a58f3a84ec67ff9bcd4e212d626", | |
| "README.md": "4c85532cd87301b8ae8ec2686c6b3680aedda6d705089456ad7d65c701f3c6cc", | |
| "config.json": "9c3a6dac0654055594fdde1f148400331f3e55cb72ffe1b7a6806db07ab93a79", | |
| "examples/inference.py": "fcde5470ba4e9fce0730f544f4f3b7669bf4f9bdcaf556223cebc4ed67ef1c5a", | |
| "model.safetensors": "95e3731e26887d066a0b2291a1574da40a36aba5ba43e476746b9c16a64bcf08", | |
| "preprocessor_config.json": "c95c459d449a2dacd974bcfef8986b2734afd55e73eb05a7aaad30542cf78b6e", | |
| "projector.py": "bab293839308485914d9618601282dbaaefc03e495841988a90174b07f4c92e3", | |
| "projector.safetensors": "81fe7c8be0a2e91b26040ab23523713d1b775eadcdee5ac0021593762d3e6332", | |
| "projector_config.json": "672b312ca5fb10324a41936294814367fb371a3891cc6d8ee8df3d22fc0c559c" | |
| }, | |
| "hash_excludes": [ | |
| "export_manifest.json" | |
| ], | |
| "hub_only_metadata": [ | |
| ".gitattributes" | |
| ] | |
| } | |