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/adapter_model.safetensors from cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20: direct link, hf CLI and curl.
- Browser
- Download file 646 MB
-
https://huggingface.co/cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20/resolve/main/last-checkpoint/adapter_model.safetensors
- Command line
-
hf download hf://cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20/last-checkpoint/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20/resolve/main/last-checkpoint/adapter_model.safetensors
646 MB
- Xet hash:
- d9ac9302fdaa71456ff93ee6f3d2edfca177a7954ce91c989cfbf6c62ff28a0f
- Size of remote file:
- 646 MB
- SHA256:
- cf027fd464e1f58f9d3f82d207235fd41e6bd08be4096e6a7a204bd7b5639520
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