Instructions to use VMXVMX/llama2-project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use VMXVMX/llama2-project with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/tmp/huggingface/hub/hf-llama-2-7b") model = PeftModel.from_pretrained(base_model, "VMXVMX/llama2-project") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- abbbf206296c97134021ff5bf4ceb9ee49c0c44574abee381277a3ca621d39e4
- Size of remote file:
- 33.7 MB
- SHA256:
- e94bc0209fc8dcd1092e3025819db339065cfceeb247ee8e89ffdeeca1ac6d3e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.