Instructions to use cimol/51248c15-c0e0-415d-a4fe-a581ba309508 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/51248c15-c0e0-415d-a4fe-a581ba309508 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-3b") model = PeftModel.from_pretrained(base_model, "cimol/51248c15-c0e0-415d-a4fe-a581ba309508") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cimol/51248c15-c0e0-415d-a4fe-a581ba309508: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/cimol/51248c15-c0e0-415d-a4fe-a581ba309508/resolve/main/training_args.bin
- Command line
-
hf download hf://cimol/51248c15-c0e0-415d-a4fe-a581ba309508/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cimol/51248c15-c0e0-415d-a4fe-a581ba309508/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 9093a3c03fec58b64a435915d2206ade80a2a3d9e6588dae5484b8f9deeef91b
- Size of remote file:
- 6.84 kB
- SHA256:
- 0e9b41abdf2c1ee1fee4307fa95d8026a900eed9bf9c15d0fe297ae70d8bd1be
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