Instructions to use tomer-shimshi/llama2-Rav with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tomer-shimshi/llama2-Rav with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf") model = PeftModel.from_pretrained(base_model, "tomer-shimshi/llama2-Rav") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from tomer-shimshi/llama2-Rav: direct link, hf CLI and curl.
- Browser
- Download file 1.84 MB
-
https://huggingface.co/tomer-shimshi/llama2-Rav/resolve/main/tokenizer.json
- Command line
-
hf download hf://tomer-shimshi/llama2-Rav/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/tomer-shimshi/llama2-Rav/resolve/main/tokenizer.json
1.84 MB
File too large to display, you can check the raw version instead.