Instructions to use M-CLIP/M-BERT-Base-69 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M-CLIP/M-BERT-Base-69 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="M-CLIP/M-BERT-Base-69")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("M-CLIP/M-BERT-Base-69") model = AutoModel.from_pretrained("M-CLIP/M-BERT-Base-69", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from M-CLIP/M-BERT-Base-69: direct link, hf CLI and curl.
- Browser
- Download file 711 MB
-
https://huggingface.co/M-CLIP/M-BERT-Base-69/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://M-CLIP/M-BERT-Base-69/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/M-CLIP/M-BERT-Base-69/resolve/main/flax_model.msgpack
711 MB
- Xet hash:
- 8722057e44e903a2dda262a466e0fb518aa16aee01c436bcd01bc3427ef9298f
- Size of remote file:
- 711 MB
- SHA256:
- 165a9459d8d1773a8c626053a0f889a02350f25fca7f89384e6060f3c3f2f18f
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