Instructions to use alhosseini/testingLlama2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use alhosseini/testingLlama2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "alhosseini/testingLlama2") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from alhosseini/testingLlama2: direct link, hf CLI and curl.
- Browser
- Download file 16.8 MB
-
https://huggingface.co/alhosseini/testingLlama2/resolve/main/adapter_model.bin
- Command line
-
hf download hf://alhosseini/testingLlama2/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/alhosseini/testingLlama2/resolve/main/adapter_model.bin
16.8 MB
- Xet hash:
- ca713af0504aa231faf6ac8802d24b7148ed5043cd406e1abd84ef4465cd57b7
- Size of remote file:
- 16.8 MB
- SHA256:
- 8b96ad17e92eb22e5b4b05b7ad9a68feec9739cc155b1be0ca1a7816a89dc45b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.