Instructions to use goodlucky55555/466b74ed-0ae4-47bc-bc35-53c2d521c8f7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use goodlucky55555/466b74ed-0ae4-47bc-bc35-53c2d521c8f7 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b") model = PeftModel.from_pretrained(base_model, "goodlucky55555/466b74ed-0ae4-47bc-bc35-53c2d521c8f7") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from goodlucky55555/466b74ed-0ae4-47bc-bc35-53c2d521c8f7: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/goodlucky55555/466b74ed-0ae4-47bc-bc35-53c2d521c8f7/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://goodlucky55555/466b74ed-0ae4-47bc-bc35-53c2d521c8f7/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/goodlucky55555/466b74ed-0ae4-47bc-bc35-53c2d521c8f7/resolve/main/last-checkpoint/training_args.bin
6.78 kB
- Xet hash:
- 686f56e8cf3a8c631ca737caaed141e484b4ddd0b208ae977a594b382e8634be
- Size of remote file:
- 6.78 kB
- SHA256:
- b3922705041ff53ec16739e9512d876a79ff2d382748997e7bf86ff203902669
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