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c68478f470
Currently, OpenDevin uses a global singleton LLM config and a global singleton agent config. This PR allows customers to configure an LLM config for each agent. A hypothetically useful scenario is to use a cheaper LLM for repo exploration / code search, and a more powerful LLM to actually do the problem solving (CodeActAgent). Partially solves #2075 (web GUI improvement is not the goal of this PR)
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
- SWE-Bench:
evaluation/swe_bench - ML-Bench:
evaluation/ml_bench - HumanEvalFix:
evaluation/humanevalfix - GAIA:
evaluation/gaia - Entity deduction Arena (EDA):
evaluation/EDA - MINT:
evaluation/mint - AgentBench:
evaluation/agent_bench - BIRD:
evaluation/bird - LogicReasoning:
evaluation/logic_reasoning
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.