Instructions to use RayNene/adaption_multi_agent_fraud_bench with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use RayNene/adaption_multi_agent_fraud_bench with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("togethercomputer/Llama-4-Scout-17B-16E-Instruct_bnb_4bit") model = PeftModel.from_pretrained(base_model, "RayNene/adaption_multi_agent_fraud_bench") - Notebooks
- Google Colab
- Kaggle
Download win-rates.png from RayNene/adaption_multi_agent_fraud_bench: direct link, hf CLI and curl.
- Browser
- Download file 47.6 kB
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https://huggingface.co/RayNene/adaption_multi_agent_fraud_bench/resolve/main/win-rates.png
- Command line
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hf download hf://RayNene/adaption_multi_agent_fraud_bench/win-rates.png
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curl -L -o win-rates.png https://huggingface.co/RayNene/adaption_multi_agent_fraud_bench/resolve/main/win-rates.png
47.6 kB
