Instructions to use alirezamsh/quip-512-mocha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alirezamsh/quip-512-mocha with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alirezamsh/quip-512-mocha")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alirezamsh/quip-512-mocha") model = AutoModelForSequenceClassification.from_pretrained("alirezamsh/quip-512-mocha", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from alirezamsh/quip-512-mocha: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/alirezamsh/quip-512-mocha/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://alirezamsh/quip-512-mocha/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/alirezamsh/quip-512-mocha/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- 4c6d3e24519c412fbd41fb793a220bcc973d87e9696b8ca097d74493aece9150
- Size of remote file:
- 1.42 GB
- SHA256:
- e914a879e20ff11fd9cb4eba2f2a12b44555b792bcf21f49292eeecd66bc45d0
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