Instructions to use quoctrungle/llama2-qlora-finetunined-openassistant-guanaco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use quoctrungle/llama2-qlora-finetunined-openassistant-guanaco with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyPixel/Llama-2-7B-bf16-sharded") model = PeftModel.from_pretrained(base_model, "quoctrungle/llama2-qlora-finetunined-openassistant-guanaco") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from quoctrungle/llama2-qlora-finetunined-openassistant-guanaco: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/quoctrungle/llama2-qlora-finetunined-openassistant-guanaco/resolve/main/adapter_model.bin
- Command line
-
hf download hf://quoctrungle/llama2-qlora-finetunined-openassistant-guanaco/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/quoctrungle/llama2-qlora-finetunined-openassistant-guanaco/resolve/main/adapter_model.bin
134 MB
- Xet hash:
- c1f386570dc2c8c1796684c5aac059c3602333fd27fb9477549765f8686e5cf0
- Size of remote file:
- 134 MB
- SHA256:
- 2a3d9403fcfa74f2302f1f015bbe860822348279aa4687c4b2694bb7174f773c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.