Benchmark Results - Stack 2.9
Note: These benchmarks are currently in progress. Results will be published after training is complete.
Benchmark Overview
Stack 2.9 will be evaluated on a comprehensive suite of benchmarks to measure coding capabilities, tool use proficiency, and overall model performance. The evaluation framework includes both standard coding benchmarks and custom tool-use scenarios.
Planned Benchmarks
1. HumanEval
Description: A set of 164 Python programming problems from OpenAI's HumanEval benchmark. Metrics: Pass@k (k=1, 10, 100) Expected Range: 70-80% pass@1 (based on Qwen2.5-Coder-32B baseline of ~76.8%) Status: Scheduled for post-training evaluation
2. MBPP (Mostly Basic Python Programming)
Description: 500 Python function synthesis problems from Google's MBPP dataset. Metrics: Pass@1, execution accuracy Expected Range: 80-85% pass@1 (based on Qwen2.5-Coder-32B baseline of ~82.3%) Status: Scheduled for post-training evaluation
3. SWE-bench
Description: Real-world GitHub issues requiring code modifications and debugging. This is the most challenging software engineering benchmark. Metrics: Resolution rate, edit similarity, test pass rate Expected Range: 15-25% resolution rate (based on similar 32B parameter models) Status: Planned for comprehensive testing post-training
4. Tool Use Accuracy (Custom OpenClaw Suite)
Description: 500 tasks covering OpenClaw-specific tool patterns: file operations, search, API calls, system commands, data processing, and multi-step workflows. Metrics: Task completion rate, tool call accuracy, parameter correctness, workflow success Expected Range: 85-92% overall task completion (conservative estimate based on fine-tuning for tool patterns) Status: Evaluation framework in development
Additional Evaluations
Context Understanding
- Long-context benchmark: Testing 128K token window utilization
- Multi-file reasoning: Cross-file code comprehension and modification
Specialized Domains
- Voice Integration: Voice command processing and response generation
- Documentation Generation: Quality assessment of auto-generated API docs
- Code Review: Bug detection and suggestion quality
Results Template
Once evaluations are complete, results will be published in the following format:
| Benchmark | Pass@1 / Score | Sample Size | Evaluation Date | Notes |
|---|---|---|---|---|
| HumanEval | TBD | 164 problems | TBD | Standard Python coding |
| MBPP | TBD | 500 problems | TBD | Basic Python synthesis |
| SWE-bench | TBD | Varies | TBD | Real-world GitHub issues |
| Tool Use | TBD | 500 tasks | TBD | OpenClaw tool patterns |
| GSM8K | TBD | 1319 problems | TBD | Math reasoning (optional) |
Benchmark Methodology
Testing Conditions
- Temperature: 0.2 (for code generation tasks)
- Top_p: 0.95
- Batch size: 1 (unless otherwise noted)
- Hardware: NVIDIA A100 80GB (or equivalent)
- Quantization: AWQ 4-bit where applicable
- Inference engine: vLLM or similar for throughput testing
Evaluation Process
- Preprocessing: Standardized test set preparation with sanitization
- Inference: Automated generation of responses for each test case
- Verification: Automated test execution for coding problems
- Analysis: Statistical aggregation and result compilation
- Documentation: Detailed methodology and raw results publication
Timeline
- Training Completion: [Date to be announced]
- Benchmark Execution: 1-2 weeks post-training
- Results Analysis: 1 week
- Public Release: 1 week after analysis completion
Publication
Results will be published in multiple formats:
- This document (BENCHMARKS.md) - Summary tables and key findings
- Detailed report ( BENCHMARKS_DETAILED.md) - In-depth methodology and raw scores
- GitHub Release - Official results with reproducible evaluation scripts
- OpenRouter listing - Performance metrics for model comparison
Stack 2.9 Benchmark Status: In Progress | Results Coming Soon