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* Update run_infer.py of gorilla to download my-languages.so * add exist check, change file path, lint code --------- Co-authored-by: Graham Neubig <neubig@gmail.com> Co-authored-by: yufansong <yufan@risingwave-labs.com>
Evaluation
This folder contains code and resources to run experiments and evaluations.
Logistics
To better organize the evaluation folder, we should follow the rules below:
- Each subfolder contains a specific benchmark or experiment. For example,
evaluation/swe_benchshould contain all the preprocessing/evaluation/analysis scripts. - Raw data and experimental records should not be stored within this repo.
- For model outputs, they should be stored at this huggingface space for visualization.
- Important data files of manageable size and analysis scripts (e.g., jupyter notebooks) can be directly uploaded to this repo.
Supported Benchmarks
To learn more about how to integrate your benchmark into OpenDevin, check out tutorial here.
Software Engineering
- SWE-Bench:
evaluation/swe_bench - HumanEvalFix:
evaluation/humanevalfix - BIRD:
evaluation/bird - BioCoder:
evaluation/ml_bench - ML-Bench:
evaluation/ml_bench - APIBench:
evaluation/gorilla - ToolQA:
evaluation/toolqa
Web Browsing
- WebArena:
evaluation/webarena - MiniWob++:
evaluation/miniwob
Misc. Assistance
- GAIA:
evaluation/gaia - GPQA:
evaluation/gpqa - AgentBench:
evaluation/agent_bench - MINT:
evaluation/mint - Entity deduction Arena (EDA):
evaluation/EDA - ProofWriter:
evaluation/logic_reasoning
Before everything begins: Setup Environment and LLM Configuration
Please follow instruction here to setup your local development environment and LLM.
OpenDevin in development mode uses config.toml to keep track of most configurations.
Here's an example configuration file you can use to define and use multiple LLMs:
[llm]
# IMPORTANT: add your API key here, and set the model to the one you want to evaluate
model = "gpt-4o-2024-05-13"
api_key = "sk-XXX"
[llm.eval_gpt4_1106_preview_llm]
model = "gpt-4-1106-preview"
api_key = "XXX"
temperature = 0.0
[llm.eval_some_openai_compatible_model_llm]
model = "openai/MODEL_NAME"
base_url = "https://OPENAI_COMPATIBLE_URL/v1"
api_key = "XXX"
temperature = 0.0
Result Visualization
Check this huggingface space for visualization of existing experimental results.
Upload your results
You can start your own fork of our huggingface evaluation outputs and submit a PR of your evaluation results to our hosted huggingface repo via PR following the guide here.