Instructions to use ktm379/code-llama-7b-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ktm379/code-llama-7b-train 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, "ktm379/code-llama-7b-train") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 67c0698b7f2aebf1b610b8969ff072801cba75f0139956b84d3d977b8e4da9f1
- Size of remote file:
- 4.79 kB
- SHA256:
- 2451c3a64ccd71fd6f6626af30b6ad10dc1649d956e3c9058830bdb9268d7db5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.