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/tokenizer.json from cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20/resolve/main/last-checkpoint/tokenizer.json
- Command line
-
hf download hf://cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20/last-checkpoint/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/cimol/0ea943d2-149b-4d41-904a-afbf75ce1a20/resolve/main/last-checkpoint/tokenizer.json
11.4 MB
- Xet hash:
- 18f5ee8c52e9d225044dd7ef664f390226accb3a4e27ac5d3e5a71e8a4eebf2a
- Size of remote file:
- 11.4 MB
- SHA256:
- fab42efe8d17406525a9154b728cf9e957629a8ed7ce997770efdd71128c6a1a
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