Instructions to use zkjzou99/code-prm-critic-lora-32k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use zkjzou99/code-prm-critic-lora-32k with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("Qwen/Qwen3.5-4B") model = PeftModel.from_pretrained(base_model, "zkjzou99/code-prm-critic-lora-32k") - Transformers
How to use zkjzou99/code-prm-critic-lora-32k with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zkjzou99/code-prm-critic-lora-32k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from zkjzou99/code-prm-critic-lora-32k: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/zkjzou99/code-prm-critic-lora-32k/resolve/main/tokenizer.json
- Command line
-
hf download hf://zkjzou99/code-prm-critic-lora-32k/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/zkjzou99/code-prm-critic-lora-32k/resolve/main/tokenizer.json
20 MB
- Xet hash:
- 8f2a7f18b1106b9adfa0c4b534797c676080a87eb6f552760f3bfdaef0c5dbd0
- Size of remote file:
- 20 MB
- SHA256:
- 5320c859bb31a7d4b8b88fdb095b1f7242c666109ca880d232e88567ea72218c
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