Instructions to use aleegis/ac6dec11-27c1-432c-93b0-ef7cdc9f88e5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aleegis/ac6dec11-27c1-432c-93b0-ef7cdc9f88e5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("scb10x/llama-3-typhoon-v1.5-8b-instruct") model = PeftModel.from_pretrained(base_model, "aleegis/ac6dec11-27c1-432c-93b0-ef7cdc9f88e5") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from aleegis/ac6dec11-27c1-432c-93b0-ef7cdc9f88e5: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/aleegis/ac6dec11-27c1-432c-93b0-ef7cdc9f88e5/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://aleegis/ac6dec11-27c1-432c-93b0-ef7cdc9f88e5/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aleegis/ac6dec11-27c1-432c-93b0-ef7cdc9f88e5/resolve/main/last-checkpoint/training_args.bin
6.78 kB
- Xet hash:
- d612d4a6ab4de54943ce83d0a7e4c59e70eb6815df5c0880dcd7b93f729f1ce9
- Size of remote file:
- 6.78 kB
- SHA256:
- 556b52df742ee1284910c50b2cd41f1eb7adef539c88a3c04aeec6e7cf686bd5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.