Instructions to use cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct") model = PeftModel.from_pretrained(base_model, "cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f/resolve/main/last-checkpoint/training_args.bin
6.84 kB
- Xet hash:
- 58f5345a61c5658477dd3b80a9d87b07518adaaeaa9098a6b85a6f0ecd851c7e
- Size of remote file:
- 6.84 kB
- SHA256:
- e6b59b3c3f378a41bd4f46c10ddcdf22ab25775d06aedd987fc7e82b37519eea
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