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/optimizer.pt from cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f: direct link, hf CLI and curl.
- Browser
- Download file 328 MB
-
https://huggingface.co/cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/cimol/a42dd542-bf68-4fe7-a1e2-05d672b8be5f/resolve/main/last-checkpoint/optimizer.pt
328 MB
- Xet hash:
- 623b951c12f183d0785f1a0580ca07032d36f2bbff657817d404140e89ed5266
- Size of remote file:
- 328 MB
- SHA256:
- d4503af9edba2b80dee88ab91386ee4c4efb0bf1482b5c1fbbcae699526a3f4d
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