Instructions to use ZeroUniqueness/qlora-llama-2-13b-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ZeroUniqueness/qlora-llama-2-13b-code with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/workspace/webui/models/TheBloke_Llama-2-13B-fp16") model = PeftModel.from_pretrained(base_model, "ZeroUniqueness/qlora-llama-2-13b-code") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-7600/optimizer.pt from ZeroUniqueness/qlora-llama-2-13b-code: direct link, hf CLI and curl.
- Browser
- Download file 1 GB
-
https://huggingface.co/ZeroUniqueness/qlora-llama-2-13b-code/resolve/main/checkpoint-7600/optimizer.pt
- Command line
-
hf download hf://ZeroUniqueness/qlora-llama-2-13b-code/checkpoint-7600/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/ZeroUniqueness/qlora-llama-2-13b-code/resolve/main/checkpoint-7600/optimizer.pt
1 GB
- Xet hash:
- 014d90b29c602028dc2bbe2cbb8c836082d3fd0379dc8eadd2c983c0fbcb2215
- Size of remote file:
- 1 GB
- SHA256:
- 9fb53c8a12296e035ec70bf89911c9255494b238ffbb8f9c7a06d814a5410c87
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.