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/trainer_state.json from cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20: direct link, hf CLI and curl.
- Browser
- Download file 18.8 kB
-
https://huggingface.co/cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20/resolve/38eae47d1d61eccd03a2e2bcbf90856ddeb7a139/last-checkpoint/trainer_state.json
- Command line
-
hf download hf://cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20@38eae47d1d61eccd03a2e2bcbf90856ddeb7a139/last-checkpoint/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20/resolve/38eae47d1d61eccd03a2e2bcbf90856ddeb7a139/last-checkpoint/trainer_state.json
18.8 kB
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