# GSM8K Accuracy Evaluation

This directory contains a replacement for the lm-eval-harness GSM8K evaluation, using an isolated GSM8K script and vLLM server for better performance and control.

## Usage

### Run tests with pytest (like buildkite)

```bash
pytest -s -v tests/evals/gsm8k/test_gsm8k_correctness.py \
    --config-list-file=configs/models-small.txt
```

### Run standalone evaluation script

```bash
# Start vLLM server first
vllm serve Qwen/Qwen2.5-1.5B-Instruct --port 8000

# Run evaluation
python tests/evals/gsm8k/gsm8k_eval.py --port 8000
```

## Configuration Format

Model configs in `configs/` directory use this YAML format:

```yaml
model_name: "Qwen/Qwen2.5-1.5B-Instruct"
accuracy_threshold: 0.54  # Minimum expected accuracy
num_questions: 1319       # Number of questions (default: full test set)
num_fewshot: 5            # Few-shot examples from train set
server_args: "--max-model-len 4096 --tensor-parallel-size 2"  # Server arguments
env:                      # Environment variables (optional)
  VLLM_USE_FLASHINFER_MOE_FP4: "1"
```

The `server_args` field accepts any arguments that can be passed to `vllm serve`.

The `env` field accepts a dictionary of environment variables to set for the server process.
