Instructions to use Paladiso/8e6f4d68-0920-4b5c-8036-5bc83b2e08d4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Paladiso/8e6f4d68-0920-4b5c-8036-5bc83b2e08d4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/CodeLlama-7b-hf") model = PeftModel.from_pretrained(base_model, "Paladiso/8e6f4d68-0920-4b5c-8036-5bc83b2e08d4") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from Paladiso/8e6f4d68-0920-4b5c-8036-5bc83b2e08d4: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/Paladiso/8e6f4d68-0920-4b5c-8036-5bc83b2e08d4/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://Paladiso/8e6f4d68-0920-4b5c-8036-5bc83b2e08d4/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Paladiso/8e6f4d68-0920-4b5c-8036-5bc83b2e08d4/resolve/main/last-checkpoint/training_args.bin
6.78 kB
- Xet hash:
- 319c9b7c1cbf7409f08b844c0627dab2b468aa9a2116910d788d6d36da57481c
- Size of remote file:
- 6.78 kB
- SHA256:
- 40d4dbec45b86977836d9ba176982d54d3a800e9fc95f980536fb2e0ad437182
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