Instructions to use Manojb/llama-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Manojb/llama-finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Manojb/llama-finetune")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Manojb/llama-finetune") model = AutoModel.from_pretrained("Manojb/llama-finetune", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from Manojb/llama-finetune: direct link, hf CLI and curl.
- Browser
- Download file 493 kB
-
https://huggingface.co/Manojb/llama-finetune/resolve/main/tokenizer.model
- Command line
-
hf download hf://Manojb/llama-finetune/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/Manojb/llama-finetune/resolve/main/tokenizer.model
493 kB
- Xet hash:
- 1e090c2d2774ea7875da72d682c12600bd69085e9c28674b917a49fe82ccffe2
- Size of remote file:
- 493 kB
- SHA256:
- dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.