Instructions to use cimol/3a48d94d-6406-4b11-a8fb-f42d50273305 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/3a48d94d-6406-4b11-a8fb-f42d50273305 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B") model = PeftModel.from_pretrained(base_model, "cimol/3a48d94d-6406-4b11-a8fb-f42d50273305") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cimol/3a48d94d-6406-4b11-a8fb-f42d50273305: direct link, hf CLI and curl.
- Browser
- Download file 7.03 kB
-
https://huggingface.co/cimol/3a48d94d-6406-4b11-a8fb-f42d50273305/resolve/main/training_args.bin
- Command line
-
hf download hf://cimol/3a48d94d-6406-4b11-a8fb-f42d50273305/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cimol/3a48d94d-6406-4b11-a8fb-f42d50273305/resolve/main/training_args.bin
7.03 kB
- Xet hash:
- e6eb3da8a3587b4949243c998cc8e85974b5f1797746ae15d7f4b82b86a65f1c
- Size of remote file:
- 7.03 kB
- SHA256:
- b1fdec8aff630bba200672586e9292b7988673140f4c784d0c8f42f4b173d53b
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