Instructions to use luonghuuthanhnam5/Llama-2-7b-chat-hf-fine-tuned-adapters with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use luonghuuthanhnam5/Llama-2-7b-chat-hf-fine-tuned-adapters with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf") model = PeftModel.from_pretrained(base_model, "luonghuuthanhnam5/Llama-2-7b-chat-hf-fine-tuned-adapters") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from luonghuuthanhnam5/Llama-2-7b-chat-hf-fine-tuned-adapters: direct link, hf CLI and curl.
- Browser
- Download file 84 MB
-
https://huggingface.co/luonghuuthanhnam5/Llama-2-7b-chat-hf-fine-tuned-adapters/resolve/main/adapter_model.bin
- Command line
-
hf download hf://luonghuuthanhnam5/Llama-2-7b-chat-hf-fine-tuned-adapters/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/luonghuuthanhnam5/Llama-2-7b-chat-hf-fine-tuned-adapters/resolve/main/adapter_model.bin
84 MB
- Xet hash:
- f743487cc5bd1a153d8d06461a3ef38294ba3cf3e1f51ab7ccf4d6aeaf7eebfe
- Size of remote file:
- 84 MB
- SHA256:
- 604fc6f766b8280d462d018813ddd2ca4ab43b2ed0e99d0ad246527cfd29ddc0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.