139 lines
3.7 KiB
Markdown
139 lines
3.7 KiB
Markdown
# 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
|
|
10. ✓ Add enhanced grading system with LLM judge
|
|
|
|
## 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
|
|
7. Enhanced grading with LLM judge
|
|
8. Response truncation for better answer extraction
|