Files
shishirpatil--gorilla/gorilla/eval/README.md
T
wehub-resource-sync bbfc60cd69
Publish BFCL to PyPI / build_and_publish (push) Waiting to run
Update API Zoo Data / send-updates (push) Waiting to run
chore: import upstream snapshot with attribution
2026-07-13 13:37:27 +08:00

1.2 KiB

Gorilla

Get Started

Getting GPT-3.5-turbo, GPT-4 and Claude Responses (0-Shot)

To get LLM responses for the API calls, use the following command:

python get_llm_responses.py --model gpt-3.5-turbo --api_key $API_KEY --output_file gpt-3.5-turbo_torchhub_0_shot.jsonl --question_data eval-data/questions/torchhub/questions_torchhub_0_shot.jsonl --api_name torchhub

Getting Responses with Retrievers (bm25 or gpt)

python get_llm_responses_retriever.py --retriever bm25 --model gpt-3.5-turbo --api_key $API_KEY --output_file gpt-3.5-turbo_torchhub_0_shot.jsonl --question_data eval-data/questions/torchhub/questions_torchhub_0_shot.jsonl --api_name torchhub --api_dataset ../../data/api/torchhub_api.jsonl

Evaluate the Response with AST tree matching

After the responses of the LLM is generated, we can start to evaluate the generated responses with respect to our dataset:

cd eval-scripts
python ast_eval_th.py --api_dataset ../../../data/api/torchhub_api.jsonl --apibench ../../../data/apibench/torchhub_eval.json --llm_responses ../eval-data/responses/torchhub/response_torchhub_Gorilla_FT_0_shot.jsonl