Small Benchmarks for LM Agents
Project description
SmallBench
Small, simple agent task environments for training and evaluation.
Designed to challenge a broad spectrum of lm-agent abilities.
Spinning Up - Use
uv add smallbench
or
pip install smallbench
Spinning Up - Dev
uv venv smallbench-dev
source smallbench-dev/bin/activate
uv sync
uv run ruff format .
Easy Benchmarks
BigCodeBench - Agent Harness
This benchmark provides a stateful environment for lm-based agents to solve coding problems from the BigCodeBench dataset. Agents are given a scratchpad (soon), a way to prepare and use unit tests, editing handlers, and a way to submit their solution.
Please see the BigCodeBench page for more information about the underlying dataset.
Get Started
Local
add GROQ_API_KEY and any other API keys supported by the apropos-ai library to the .env file.
- Note: Groq, Google, and possibly other providers offer free tiers.
If you use a Docker backend, ensure you have the Docker app running. If you use Modal, please add all necessary credentials.
Then, run the test script:
uv run python -m src.smallbench.benchmarks.bcb_a.test
Colab
Check out the Colab if you prefer to run the benchmark in the cloud.
Medium Benchmarks
TBD
Hard Benchmarks
TBD
Difficult Benchmarks
TBD
Scores
BigCodeBench - Agent Harness (ReAct)
LM | Number Correct | Success Rate | Sample Size | Avg. Cost Per Run |
---|---|---|---|---|
gpt-4o-2024-08-06 | 17 | 17% | 100 | $0.057 |
deepseek-v2.5 | 12 | 12% | 100 | $0.0029 |
gpt-4o-mini-2024-07-18 | 12 | 12% | 100 | $0.003 |
gemini-1.5-flash-latest | 6 | 06% | 100 | $0.0018 |
Animation credits: ZZ
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