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 value_head.pt from zkjzou99/code-prm-critic-lora-32k: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/zkjzou99/code-prm-critic-lora-32k/resolve/main/value_head.pt
- Command line
-
hf download hf://zkjzou99/code-prm-critic-lora-32k/value_head.pt
-
curl -L -o value_head.pt https://huggingface.co/zkjzou99/code-prm-critic-lora-32k/resolve/main/value_head.pt
6.78 kB
- Xet hash:
- 308a02912531bd526545e129014a372f339d34117c98c643a7cc1491af779510
- Size of remote file:
- 6.78 kB
- SHA256:
- edcc22854467e3c53e668cdfc10f01a72445d6b51fb27c144898e01142d9fecb
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