# Model Evaluation Framework

A simple asynchronous framework for evaluating language models on reasoning tasks using the OpenRouter API.

## Evaluation Results Repository

In order to keep the main repo clean and not clutter it with evaluation traces from different models, we store all evaluation results in a separate repository: [reasoning-gym-eval](https://github.com/open-thought/reasoning-gym-eval).

If you run evaluations and want to contribute your results, please create a pull request in the [reasoning-gym-eval](https://github.com/open-thought/reasoning-gym-eval) repository, not in the main reasoning-gym repo.

## Overview

This framework provides tools to evaluate language models on the reasoning_gym datasets. It supports:
- Concurrent evaluation of multiple questions and datasets
- Customizable dataset configurations
- Automatic result aggregation and summary generation
- Rate limiting for API calls

## Setup

1. Install reasoning-gym in development mode:
```bash
pip install -e ..
```

2. Install the additional dependencies required for evaluation:
```bash
pip install -r requirements-eval.txt
```

3. Set your API key (if required by the API):

   For OpenRouter, you can set it as an environment variable:
   ```bash
   export OPENROUTER_API_KEY=your-api-key
   ```

   Or provide it directly when running the script:
   ```bash
   python eval.py --config your_config.yaml --api-key your-api-key
   ```

   Note: API key is optional for some APIs (e.g., local deployments).


4. Prepare your evaluation configuration in YAML or JSON format (see example in `example_config.yaml`):

```yaml
# Example configuration
model: "meta-llama/llama-3.3-70b-instruct"
provider: "Hyperbolic"  # Optional, can be omitted
output_dir: "results"
max_concurrent: 10
default_size: 20  # Default size for all datasets
default_seed: 42  # Default seed for all datasets
max_tokens: 32768  # Maximum generation length (optional)
temperature: 0.6   # Generation temperature (optional)
top_p: 0.95        # Top-p sampling parameter (optional)
system_prompt_id: "default"  # Use a predefined system prompt by ID (optional)
# system_prompt: "Your custom system prompt here"  # Or specify a custom system prompt directly

categories:
  - category: "algebra"
    datasets:
      - dataset: "complex_arithmetic"
        params:
          min_real: -10
          max_real: 10
          min_imag: -10
          max_imag: 10

  - category: "arithmetic"
    datasets:
      - dataset: "chain_sum"
        size: 12
        seed: 43
        params:
          min_digits: 2
          allow_negation: true

      - dataset: "products"
        size: 10
        seed: 43
        params:
          min_digits: 2
          allow_negation: true
```

For example, to evaluate Claude 3.5 Sonnet on algorithmic datasets:

```yaml
model: "anthropic/claude-3.5-sonnet"
provider: "Anthropic"
output_dir: "results"
max_concurrent: 5
default_size: 50
default_seed: 45

categories:
  - category: "algorithmic"
    datasets:
      - dataset: "count_primes"
      - dataset: "game_of_life"
      - dataset: "graph_color"
      - dataset: "isomorphic_strings"
      - dataset: "letter_jumble"
      - dataset: "rotate_matrix"
      - dataset: "sentence_reordering"
      - dataset: "string_manipulation"
      - dataset: "word_ladder"
      - dataset: "word_sorting"
```

### Generating Configurations

You can generate a configuration file with all registered datasets using the `generate_config.py` script:

```bash
python generate_config.py --output my_config.yaml --model "anthropic/claude-3.5-sonnet" --provider "Anthropic" --size 50 --seed 42
```

Options:
- `--output`: Output YAML file path (default: all_datasets.yaml)
- `--model`: Model name (default: openai/gpt-4)
- `--provider`: Provider name (default: None)
- `--size`: Default dataset size (default: 100)
- `--seed`: Default dataset seed (default: 42)
- `--include-params`: Include all configuration parameters (default: False)
- `--category`: Only include datasets from this category (default: None)

#### Generating Config for a Specific Category

To generate a configuration file containing only datasets from a specific category:

```bash
python generate_config.py --category algorithmic --output algorithmic_datasets.yaml --model "anthropic/claude-3.5-sonnet"
```

This will create a configuration file that includes only datasets in the "algorithmic" category. This is useful when you want to focus your evaluation on a specific type of reasoning tasks.

Example categories include: math, arithmetic, reasoning, algorithmic, etc. The category is automatically extracted from the dataset's module name (e.g., from `reasoning_gym.math.dataset_name`, it extracts "math").

You can see all available categories by running the script without the `--category` option, as it will print all categories at the end of execution.

### Running Evaluations

```bash
python eval.py --config configs/your_config.yaml
```

For example:

```bash
python eval.py --config example_config.yaml --full-results
```

You can specify a different API base URL if needed:

```bash
python eval.py --config example_config.yaml --base-url "https://api.together.xyz/v1" --api-key "your-together-api-key"
```


The results will be stored in a directory named after the model and timestamp, containing:
- `summary.json` - Summary of all results
- `results.json` - Full results (if `--full-results` is specified)
- Individual dataset results in category subdirectories

For example:
```
results/
└── meta-llama_llama-3.3-70b-instruct_20250227_162030/
    ├── summary.json
    ├── results.json
    ├── algebra/
    │   └── complex_arithmetic.json
    └── arithmetic/
        ├── chain_sum.json
        └── products.json
```

Please upload your results to [reasoning-gym-eval](https://github.com/open-thought/reasoning-gym-eval).