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")# 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
- Xet hash:
- 36a44b4dd8d4226b631c75c8869f0f3bf2ab1c06ab5fe9b0948416ca31a4dbfb
- Size of remote file:
- 2.67 kB
- SHA256:
- 80bcc069cabc6b6d181e812bf74e344248153f08f20ea9b6b1c0b390acf1b24b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.