# llama-server-simulator Implementation Summary

## Overview
Successfully implemented a standalone Python script that simulates a llama-server HTTP endpoint for testing the eval script.

## Features Implemented

### 1. HTTP Server
- Flask-based `/v1/chat/completions` endpoint
- OpenAI-compatible response format
- Configurable port and host

### 2. AIME Dataset Integration
- Loads AIME dataset from HuggingFace
- In-memory storage for fast lookup
- 90 questions loaded from train split

### 3. Intelligent Question Matching
- **Exact matching**: Direct string comparison
- **LaTeX removal**: Removes `$...$` formatting for flexible matching
- **Levenshtein distance**: Calculates similarity between strings
- **Partial matching**: Finds best match even with small differences

### 4. Response Generation
- Configurable success rate (0-1)
- Returns correct answers when success rate allows
- Returns wrong answers when success rate doesn't allow
- Wrong answers are generated by incrementing the expected answer

### 5. Debug Logging
- Debug messages written to stderr
- Logs request content, matching results, and distances
- Helps troubleshoot matching issues

## Configuration Options

```bash
python3 llama-server-simulator.py \
  --port 8034 \
  --host localhost \
  --success-rate 0.8 \
  --dataset-split train
```

## Testing Results

### Test 1: Correct Answer
- **Success rate**: 0.8
- **Expected answer**: 116
- **Result**: ✓ Correct (116)

### Test 2: Wrong Answer
- **Success rate**: 0.0
- **Expected answer**: 116
- **Result**: ✓ Wrong (117)

### Test 3: No Matching Question
- **Request**: "What is the capital of France?"
- **Result**: ✓ Returns error "No matching question found"

### Test 4: Success Rate Verification
- **Success rate**: 0.8
- **Requests**: 10
- **Correct answers**: 8/10 (80%)
- **Result**: ✓ Success rate working as expected

## Technical Details

### Matching Algorithm
1. Try exact match (case-insensitive)
2. Try match after removing LaTeX formatting
3. Calculate Levenshtein distance for partial matches
4. Return best match if distance < 0.3 (30% difference)

### Response Format
```json
{
  "id": "chatcmpl-1769864875",
  "object": "chat.completion",
  "created": 1769864875,
  "model": "llama",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "116"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 100,
    "completion_tokens": 50,
    "total_tokens": 150
  }
}
```

## Files Created

1. `llama-server-simulator.py` - Main simulator script
2. `test-simulator.sh` - Basic test script
3. `test-simulator-comprehensive.sh` - Comprehensive test script
4. `llama-server-simulator-plan.md` - Implementation plan
5. `llama-eval-discussion.md` - Discussion notes

## Next Steps

1. ✓ Basic simulator structure
2. ✓ AIME dataset integration
3. ✓ Question matching with Levenshtein distance
4. ✓ Response generation with configurable success rate
5. ✓ Testing with curl requests
6. ⏭️ Integrate with eval script
7. ⏭️ Implement eval state object
8. ⏭️ Implement processor object
9. ⏭️ Add real-time progress reporting

## Known Limitations

1. Only supports AIME dataset (train split)
2. Matching is case-insensitive
3. Wrong answers are simple increments (not realistic)
4. No support for multiple endpoints
5. No distributed evaluation

## Future Enhancements

1. Support multiple datasets
2. More sophisticated wrong answer generation
3. Multiple endpoint support
4. Distributed evaluation
5. Real-time progress reporting
6. Eval state serialization