Instructions to use cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-7B") model = PeftModel.from_pretrained(base_model, "cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20: direct link, hf CLI and curl.
- Browser
- Download file 328 MB
-
https://huggingface.co/cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20/resolve/main/last-checkpoint/optimizer.pt
328 MB
- Xet hash:
- 2a8df26841cde59a0397a49b79c8490dbf67330b64d659c33a364c3b601d9501
- Size of remote file:
- 328 MB
- SHA256:
- 70573ecd96cdd3bc0a7001abafebafca9053e2e7ef3e801e51dea106c267c1e4
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