Instructions to use sidd272/Llama_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sidd272/Llama_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sidd272/Llama_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sidd272/Llama_model") model = AutoModelForSequenceClassification.from_pretrained("sidd272/Llama_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sidd272/Llama_model: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/sidd272/Llama_model/resolve/main/training_args.bin
- Command line
-
hf download hf://sidd272/Llama_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sidd272/Llama_model/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 1a97f6c80afaf54b8888993803cce942c796be724418fa0ce5db77f3f1b98464
- Size of remote file:
- 4.6 kB
- SHA256:
- afe31e4d1bc1f193cc536f803e48bd71160a4b0497d4ef02b6069535eef0dd37
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