Add vLLM backend for open weight model evaluation #34
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lewtun
commented
Sep 4, 2024
| class VllmChatModel(BaseLLM): | ||
| def __init__(self, llm_config: LLMConfig): | ||
| super().__init__(llm_config) | ||
| self.client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY") |
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This is the default endpoint in vLLM, but I could add it as an env variable if preferred
lewtun
commented
Sep 4, 2024
| @@ -88,7 +88,7 @@ def get_runned_ids(file_path): | |||
| args = parser.parse_args() | |||
| rewards = [] | |||
| all_task_ids = list(range(0, 251)) | |||
| REWARD_LOG_FILE = f"{args.llm}_{args.agent_arch}_results_webshop.csv" | |||
| REWARD_LOG_FILE = f"{args.llm.replace('/', '_')}_{args.agent_arch}_results_webshop.csv" | |||
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This is needed because Hugging Face model repos are in the form {org}/{repo_name} which causes problems when trying to write the file to disk.
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This PR adds support for a vLLM backend so that Hugging Face models like
Salesforce/xLAM-v0.1-rcan be evaluated. I've shown how this works for WebShop and am happy to extend it to the other benchmarks if the API looks good to you.