136 lines
3.5 KiB
Markdown
136 lines
3.5 KiB
Markdown
# llama-server-simulator Implementation Summary
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## Overview
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Successfully implemented a standalone Python script that simulates a llama-server HTTP endpoint for testing the eval script.
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## Features Implemented
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### 1. HTTP Server
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- Flask-based `/v1/chat/completions` endpoint
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- OpenAI-compatible response format
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- Configurable port and host
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### 2. AIME Dataset Integration
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- Loads AIME dataset from HuggingFace
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- In-memory storage for fast lookup
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- 90 questions loaded from train split
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### 3. Intelligent Question Matching
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- **Exact matching**: Direct string comparison
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- **LaTeX removal**: Removes `$...$` formatting for flexible matching
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- **Levenshtein distance**: Calculates similarity between strings
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- **Partial matching**: Finds best match even with small differences
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### 4. Response Generation
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- Configurable success rate (0-1)
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- Returns correct answers when success rate allows
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- Returns wrong answers when success rate doesn't allow
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- Wrong answers are generated by incrementing the expected answer
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### 5. Debug Logging
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- Debug messages written to stderr
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- Logs request content, matching results, and distances
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- Helps troubleshoot matching issues
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## Configuration Options
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```bash
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python3 llama-server-simulator.py \
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--port 8034 \
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--host localhost \
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--success-rate 0.8 \
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--dataset-split train
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```
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## Testing Results
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### Test 1: Correct Answer
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- **Success rate**: 0.8
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- **Expected answer**: 116
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- **Result**: ✓ Correct (116)
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### Test 2: Wrong Answer
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- **Success rate**: 0.0
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- **Expected answer**: 116
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- **Result**: ✓ Wrong (117)
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### Test 3: No Matching Question
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- **Request**: "What is the capital of France?"
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- **Result**: ✓ Returns error "No matching question found"
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### Test 4: Success Rate Verification
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- **Success rate**: 0.8
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- **Requests**: 10
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- **Correct answers**: 8/10 (80%)
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- **Result**: ✓ Success rate working as expected
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## Technical Details
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### Matching Algorithm
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1. Try exact match (case-insensitive)
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2. Try match after removing LaTeX formatting
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3. Calculate Levenshtein distance for partial matches
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4. Return best match if distance < 0.3 (30% difference)
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### Response Format
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```json
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{
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"id": "chatcmpl-1769864875",
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"object": "chat.completion",
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"created": 1769864875,
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"model": "llama",
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"choices": [
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": "116"
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},
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"finish_reason": "stop"
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}
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],
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"usage": {
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"prompt_tokens": 100,
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"completion_tokens": 50,
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"total_tokens": 150
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}
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}
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```
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## Files Created
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1. `llama-server-simulator.py` - Main simulator script
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2. `test-simulator.sh` - Basic test script
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3. `test-simulator-comprehensive.sh` - Comprehensive test script
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4. `llama-server-simulator-plan.md` - Implementation plan
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5. `llama-eval-discussion.md` - Discussion notes
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## Next Steps
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1. ✓ Basic simulator structure
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2. ✓ AIME dataset integration
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3. ✓ Question matching with Levenshtein distance
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4. ✓ Response generation with configurable success rate
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5. ✓ Testing with curl requests
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6. ⏭️ Integrate with eval script
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7. ⏭️ Implement eval state object
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8. ⏭️ Implement processor object
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9. ⏭️ Add real-time progress reporting
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## Known Limitations
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1. Only supports AIME dataset (train split)
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2. Matching is case-insensitive
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3. Wrong answers are simple increments (not realistic)
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4. No support for multiple endpoints
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5. No distributed evaluation
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## Future Enhancements
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1. Support multiple datasets
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2. More sophisticated wrong answer generation
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3. Multiple endpoint support
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4. Distributed evaluation
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5. Real-time progress reporting
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6. Eval state serialization
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