Instructions to use wsntxxn/effb2-trm-audiocaps-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wsntxxn/effb2-trm-audiocaps-captioning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="wsntxxn/effb2-trm-audiocaps-captioning", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("wsntxxn/effb2-trm-audiocaps-captioning", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from wsntxxn/effb2-trm-audiocaps-captioning: direct link, hf CLI and curl.
- Browser
- Download file 55.3 MB
-
https://huggingface.co/wsntxxn/effb2-trm-audiocaps-captioning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://wsntxxn/effb2-trm-audiocaps-captioning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/wsntxxn/effb2-trm-audiocaps-captioning/resolve/main/pytorch_model.bin
55.3 MB
- Xet hash:
- 9014b82d59a536abd079c668471364689367b44d2511d516b533d2f64e668c40
- Size of remote file:
- 55.3 MB
- SHA256:
- 6d5d51984ac220288b130d04f53652a2aec21e7f2cd275c0a48ed1648f6ace16
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