Instructions to use AGENTDARS/Reviewer-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AGENTDARS/Reviewer-7B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/home/dars/.cache/huggingface/hub/models--deepseek-ai--DeepSeek-R1-Distill-Qwen-7B/snapshots/393119fcd6a873e5776c79b0db01c96911f5f0fc/") model = PeftModel.from_pretrained(base_model, "AGENTDARS/Reviewer-7B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b15aea0c578ca8c8cc13a472dff42bc9a45634f2d2983ef091fb9f8d1c7cfa5f
- Size of remote file:
- 7.22 kB
- SHA256:
- 9a90c1a81811ec64045f3834893bb9172637c9bc9f2daf050bb5fd2ff30f3029
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.