sammm/codebench
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Leader Board picture and bug Fixs

Browse Source
This commit is contained in:
leduc 2025-03-15 01:35:25 +01:00
parent dedbeceb8e
commit f538ed1bd3
10 changed files with 664 additions and 991 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
.DS_Store vendored
View File

Binary file not shown.

122
README.md
View File

@ -1,6 +1,11 @@
# Codebench - Ollama Model Benchmark Tool
A Python-based benchmarking tool for testing and comparing different Ollama models on coding tasks.
A Python-based benchmarking tool for testing and comparing different Ollama models on coding tasks. This tool allows you to benchmark multiple Ollama models against common coding problems, measure their performance, and visualize the results.
## Components
- **Benchmarking Engine**: `main.py` - Core benchmarking functionality with integrated plotting
- **Visualization Tool**: `lboard.py` - Standalone visualization for benchmark results
## Features
@ -15,6 +20,7 @@ A Python-based benchmarking tool for testing and comparing different Ollama mode
- Python 3.8+
- Ollama server (local or remote)
- Required Python packages (see Installation)
- Together API key (optional, for advanced code analysis)
## Installation
@ -23,17 +29,21 @@ A Python-based benchmarking tool for testing and comparing different Ollama mode
```bash
git clone https://github.com/yourusername/codebench.git
cd codebench
```
2. Install required packages:
```bash
pip install -r requirements.txt
```
```
Or install the required packages manually:
```bash
pip install requests matplotlib py-cpuinfo
```
3. (Optional) Set up Together API:
3. (Optional) Set up Together API for advanced code analysis:
```bash
export TOGETHER_API_KEY='your_api_key_here'
```
```
## Usage
Basic usage:
@ -45,7 +55,7 @@ python3 main.py
Available options:
```bash
python main.py --server [local|z60] --model [model_name] --number [count|all] --verbose
python main.py --server [local|z60] --model [model_name] --number [count|all] --verbose --plot-only --no-plot --file [results_file]
```
## Arguments:
@ -54,6 +64,9 @@ python main.py --server [local|z60] --model [model_name] --number [count|all] --
- --model : Test specific model only
- --number : Number of models to test
- --verbose : Enable detailed output
- --plot-only : Skip benchmarking and just generate graphs from existing results
- --no-plot : Run benchmarking without plotting graphs at the end
- --file : Specify a benchmark results file to use for plotting (only with --plot-only)
## Supported Tests
The tool currently tests models on these coding challenges:
@ -94,7 +107,7 @@ Results are saved in the benchmark_results directory with the following naming c
```plaintext
[CPU_Model]_[Server_Address].json
```
```
Example:
@ -102,6 +115,57 @@ Example:
Apple_M1_Pro_localhost_11434.json
```
## Visualizing Results
There are two ways to generate a visual comparison of model performances as a leaderboard:
### Option 1: Using main.py (Recommended)
By default, main.py will now automatically generate graphs after benchmarking. You can also use it to just generate graphs without running benchmarks:
```bash
# Run benchmarks and generate graphs (default behavior)
python3 main.py
# Skip benchmarking and just generate graphs from the latest results
python3 main.py --plot-only
# Skip benchmarking and generate graphs from a specific results file
python3 main.py --plot-only --file path/to/results.json
# Run benchmarks without generating graphs
python3 main.py --no-plot
```
The plot will be saved as `benchmark_results/model_comparison.png` with high resolution (300 DPI).
### Option 2: Using lboard.py (Legacy)
You can still use the standalone lboard.py script:
```bash
python3 lboard.py
```
This will:
- Automatically find the latest benchmark results
- Generate a graph showing:
- Token processing speed (blue bars)
- Success rates (red markers)
- Duration ranges (green vertical lines)
You can also specify a specific results file:
```bash
python3 lboard.py path/to/results.json
# or
python3 lboard.py --file path/to/results.json
```
## Visualization Features
The visualization includes:
- Model performance comparison
- Token processing speeds with min/max ranges
- Success rates across all tests
- Execution duration ranges
- Color-coded model names (green for high performers)
## Server Configuration
Default servers are configured in the code:
@ -122,8 +186,50 @@ codellama:13b
```
## Output Files
The tool generates several output files in the `benchmark_results` directory:
1. **JSON Results File**: `[CPU_Model]_[Server_Address].json`
- Contains detailed benchmark results for all tested models
- Used for later analysis and visualization
2. **Log File**: `[CPU_Model]_[Server_Address].log`
- Contains console output from the benchmark run
- Useful for debugging and reviewing test details
3. **Plot Image**: `model_comparison.png`
- High-resolution (300 DPI) visualization of model performance
- Shows token processing speed, success rates, and duration ranges
## Recent Updates
### March 2025 Updates
- Added `--plot-only` option to skip benchmarking and directly generate plots
- Added `--no-plot` option to run benchmarks without generating plots
- Added `--file` option to specify a benchmark results file for plotting
- Fixed plot generation to ensure high-quality output images
- Improved visualization with better formatting and higher resolution
- Updated documentation with comprehensive usage instructions
## Troubleshooting
### Common Issues
1. **Ollama Server Connection**
- Ensure your Ollama server is running and accessible
- Check the server URL in the `--server` option
2. **Missing Dependencies**
- Run `pip install -r requirements.txt` to install all required packages
- Ensure matplotlib is properly installed for visualization
3. **Plot Generation**
- If plots appear empty, ensure you have the latest version of matplotlib
- Check that the benchmark results file contains valid data
## Contributing
Feel free to submit issues and enhancement requests!
## License
[Your chosen license]
CC NC BY

245
benchmark_results/Apple_M1_Pro_localhost_11434.json
View File

@ -998,6 +998,251 @@
"max_avg_duration": 12.908918361333333,
"min_tokens_per_second": 18.377766002186945,
"max_tokens_per_second": 18.9448229322312
},
{
"model": "phi4-mini:latest",
"total_duration": 10.860303611333332,
"tokens_per_second": 29.361579428697542,
"test_results": {
"Fibonacci": {
"success_rate": 61.111111111111114,
"passed_cases": 11,
"total_cases": 18,
"success_cases_rate": 0.6111111111111112,
"avg_duration": 10.860303611333332,
"avg_tokens_sec": 29.361579428697542
},
"Binary Search": {
"success_rate": 100.0,
"passed_cases": 18,
"total_cases": 18,
"success_cases_rate": 1.0,
"avg_duration": 10.22926025,
"avg_tokens_sec": 29.360358027471495
},
"Palindrome": {
"success_rate": 100.0,
"passed_cases": 18,
"total_cases": 18,
"success_cases_rate": 1.0,
"avg_duration": 7.7338954719999995,
"avg_tokens_sec": 29.349959100715157
},
"Anagram Check": {
"success_rate": 100.0,
"passed_cases": 18,
"total_cases": 18,
"success_cases_rate": 1.0,
"avg_duration": 9.66612725,
"avg_tokens_sec": 29.794841927435822
}
},
"overall_success_rate": 90.27777777777779,
"overall_success_cases_rate": 0.9027777777777778,
"min_avg_duration": 7.7338954719999995,
"max_avg_duration": 10.860303611333332,
"min_tokens_per_second": 29.349959100715157,
"max_tokens_per_second": 29.794841927435822
}
]
},
{
"timestamp": "20250313_051856",
"results": [
{
"model": "gemma3:12b",
"total_duration": 17.904428624666668,
"tokens_per_second": 11.206900603314153,
"test_results": {
"Fibonacci": {
"success_rate": 100.0,
"passed_cases": 18,
"total_cases": 18,
"success_cases_rate": 1.0,
"avg_duration": 17.904428624666668,
"avg_tokens_sec": 11.206900603314153
},
"Binary Search": {
"success_rate": 100.0,
"passed_cases": 18,
"total_cases": 18,
"success_cases_rate": 1.0,
"avg_duration": 14.096915041666666,
"avg_tokens_sec": 11.209157987254114
},
"Palindrome": {
"success_rate": 100.0,
"passed_cases": 18,
"total_cases": 18,
"success_cases_rate": 1.0,
"avg_duration": 9.514898375333333,
"avg_tokens_sec": 11.037508677057549
},
"Anagram Check": {
"success_rate": 100.0,
"passed_cases": 18,
"total_cases": 18,
"success_cases_rate": 1.0,
"avg_duration": 24.419397555666666,
"avg_tokens_sec": 11.87609409055045
}
},
"overall_success_rate": 100.0,
"overall_success_cases_rate": 1.0,
"min_avg_duration": 9.514898375333333,
"max_avg_duration": 24.419397555666666,
"min_tokens_per_second": 11.037508677057549,
"max_tokens_per_second": 11.87609409055045
}
]
},
{
"timestamp": "20250314_024439",
"results": [
{
"model": "SiliconBasedWorld/Qwen2.5-7B-Instruct-1M",
"total_duration": 20.47047556933333,
"tokens_per_second": 19.721316911932245,
"test_results": {
"Fibonacci": {
"success_rate": 61.111111111111114,
"passed_cases": 11,
"total_cases": 18,
"success_cases_rate": 0.6111111111111112,
"avg_duration": 20.47047556933333,
"avg_tokens_sec": 19.721316911932245
},
"Binary Search": {
"success_rate": 66.66666666666666,
"passed_cases": 12,
"total_cases": 18,
"success_cases_rate": 0.6666666666666666,
"avg_duration": 89.59582123599999,
"avg_tokens_sec": 19.522371869517652
},
"Palindrome": {
"success_rate": 100.0,
"passed_cases": 18,
"total_cases": 18,
"success_cases_rate": 1.0,
"avg_duration": 29.476939527666666,
"avg_tokens_sec": 19.835750358255293
},
"Anagram Check": {
"success_rate": 33.33333333333333,
"passed_cases": 6,
"total_cases": 18,
"success_cases_rate": 0.3333333333333333,
"avg_duration": 52.099640236333336,
"avg_tokens_sec": 19.661776969493513
}
},
"overall_success_rate": 65.27777777777779,
"overall_success_cases_rate": 0.6527777777777778,
"min_avg_duration": 20.47047556933333,
"max_avg_duration": 89.59582123599999,
"min_tokens_per_second": 19.522371869517652,
"max_tokens_per_second": 19.835750358255293
}
]
},
{
"timestamp": "20250314_110909",
"results": [
{
"model": "olmo2:13b",
"total_duration": 25.239670416666666,
"tokens_per_second": 8.973277631244137,
"test_results": {
"Fibonacci": {
"success_rate": 61.111111111111114,
"passed_cases": 11,
"total_cases": 18,
"success_cases_rate": 0.6111111111111112,
"avg_duration": 25.239670416666666,
"avg_tokens_sec": 8.973277631244137
},
"Binary Search": {
"success_rate": 100.0,
"passed_cases": 18,
"total_cases": 18,
"success_cases_rate": 1.0,
"avg_duration": 10.511362861,
"avg_tokens_sec": 8.094987124683419
},
"Palindrome": {
"success_rate": 100.0,
"passed_cases": 18,
"total_cases": 18,
"success_cases_rate": 1.0,
"avg_duration": 7.803927528,
"avg_tokens_sec": 8.07489922259982
},
"Anagram Check": {
"success_rate": 100.0,
"passed_cases": 18,
"total_cases": 18,
"success_cases_rate": 1.0,
"avg_duration": 16.829488430333335,
"avg_tokens_sec": 8.85685146687769
}
},
"overall_success_rate": 90.27777777777779,
"overall_success_cases_rate": 0.9027777777777778,
"min_avg_duration": 7.803927528,
"max_avg_duration": 25.239670416666666,
"min_tokens_per_second": 8.07489922259982,
"max_tokens_per_second": 8.973277631244137
}
]
},
{
"timestamp": "20250314_111430",
"results": [
{
"model": "olmo2:13b-1124-instruct-q4_K_M",
"total_duration": 27.796664694333334,
"tokens_per_second": 9.16360668962085,
"test_results": {
"Fibonacci": {
"success_rate": 27.77777777777778,
"passed_cases": 5,
"total_cases": 18,
"success_cases_rate": 0.2777777777777778,
"avg_duration": 27.796664694333334,
"avg_tokens_sec": 9.16360668962085
},
"Binary Search": {
"success_rate": 100.0,
"passed_cases": 18,
"total_cases": 18,
"success_cases_rate": 1.0,
"avg_duration": 21.839994722333333,
"avg_tokens_sec": 9.000336176480124
},
"Palindrome": {
"success_rate": 100.0,
"passed_cases": 18,
"total_cases": 18,
"success_cases_rate": 1.0,
"avg_duration": 10.587036805333334,
"avg_tokens_sec": 8.492606444397637
},
"Anagram Check": {
"success_rate": 100.0,
"passed_cases": 18,
"total_cases": 18,
"success_cases_rate": 1.0,
"avg_duration": 9.969617250333334,
"avg_tokens_sec": 8.499243210997909
}
},
"overall_success_rate": 81.94444444444444,
"overall_success_cases_rate": 0.8194444444444444,
"min_avg_duration": 9.969617250333334,
"max_avg_duration": 27.796664694333334,
"min_tokens_per_second": 8.492606444397637,
"max_tokens_per_second": 9.16360668962085
}
]
}

219
benchmark_results/Apple_M1_Pro_localhost_11434.log
View File

@ -1,4 +1,4 @@
Benchmark Run: 20250303_174821
Benchmark Run: 20250314_111430
Server: http://localhost:11434
CPU Information:
@ -15,222 +15,13 @@ Benchmark Results:
🏆 Final Model Leaderboard:
qwen2.5-coder:7b-instruct-q4_K_M
Overall Success Rate: 100.0% (72/72 cases)
Average Tokens/sec: 19.33 (18.75 - 19.58)
Average Duration: 17.32s
Min/Max Avg Duration: 8.67s / 17.99s
Test Results:
- Fibonacci: ✅ 18/18 cases (100.0%)
- Binary Search: ✅ 18/18 cases (100.0%)
- Palindrome: ✅ 18/18 cases (100.0%)
- Anagram Check: ✅ 18/18 cases (100.0%)
falcon3:10b
Overall Success Rate: 100.0% (72/72 cases)
Average Tokens/sec: 13.21 (12.53 - 13.31)
Average Duration: 13.46s
Min/Max Avg Duration: 6.76s / 13.46s
Test Results:
- Fibonacci: ✅ 18/18 cases (100.0%)
- Binary Search: ✅ 18/18 cases (100.0%)
- Palindrome: ✅ 18/18 cases (100.0%)
- Anagram Check: ✅ 18/18 cases (100.0%)
qwen2.5:14b
Overall Success Rate: 100.0% (72/72 cases)
Average Tokens/sec: 9.78 (9.78 - 9.88)
Average Duration: 35.25s
Min/Max Avg Duration: 30.09s / 35.25s
Test Results:
- Fibonacci: ✅ 18/18 cases (100.0%)
- Binary Search: ✅ 18/18 cases (100.0%)
- Palindrome: ✅ 18/18 cases (100.0%)
- Anagram Check: ✅ 18/18 cases (100.0%)
qwen2.5-coder:14b-instruct-q4_K_M
Overall Success Rate: 100.0% (72/72 cases)
Average Tokens/sec: 9.68 (9.65 - 9.88)
Average Duration: 37.18s
Min/Max Avg Duration: 23.06s / 37.18s
Test Results:
- Fibonacci: ✅ 18/18 cases (100.0%)
- Binary Search: ✅ 18/18 cases (100.0%)
- Palindrome: ✅ 18/18 cases (100.0%)
- Anagram Check: ✅ 18/18 cases (100.0%)
phi4:latest
Overall Success Rate: 100.0% (72/72 cases)
Average Tokens/sec: 9.01 (8.96 - 9.32)
Average Duration: 23.44s
Min/Max Avg Duration: 23.44s / 38.82s
Test Results:
- Fibonacci: ✅ 18/18 cases (100.0%)
- Binary Search: ✅ 18/18 cases (100.0%)
- Palindrome: ✅ 18/18 cases (100.0%)
- Anagram Check: ✅ 18/18 cases (100.0%)
deepseek-r1:14b
Overall Success Rate: 97.2% (70/72 cases)
Average Tokens/sec: 9.05 (8.90 - 9.38)
Average Duration: 278.32s
Min/Max Avg Duration: 174.30s / 482.10s
Test Results:
- Fibonacci: ✅ 18/18 cases (100.0%)
- Binary Search: ✅ 18/18 cases (100.0%)
- Palindrome: ❌ 16/18 cases (88.9%)
- Anagram Check: ✅ 18/18 cases (100.0%)
llama3.2-vision:11b-instruct-q4_K_M
Overall Success Rate: 95.8% (69/72 cases)
Average Tokens/sec: 15.68 (14.92 - 15.92)
Average Duration: 22.33s
Min/Max Avg Duration: 16.31s / 28.85s
Test Results:
- Fibonacci: ❌ 16/18 cases (88.9%)
- Binary Search: ❌ 17/18 cases (94.4%)
- Palindrome: ✅ 18/18 cases (100.0%)
- Anagram Check: ✅ 18/18 cases (100.0%)
llama3.2:3b
Overall Success Rate: 94.4% (68/72 cases)
Average Tokens/sec: 36.09 (30.85 - 37.53)
Average Duration: 2.67s
Min/Max Avg Duration: 1.04s / 2.76s
Test Results:
- Fibonacci: ❌ 14/18 cases (77.8%)
- Binary Search: ✅ 18/18 cases (100.0%)
- Palindrome: ✅ 18/18 cases (100.0%)
- Anagram Check: ✅ 18/18 cases (100.0%)
llama3.1:8b
Overall Success Rate: 94.4% (68/72 cases)
Average Tokens/sec: 17.92 (17.92 - 18.45)
Average Duration: 18.04s
Min/Max Avg Duration: 14.68s / 19.56s
Test Results:
- Fibonacci: ❌ 14/18 cases (77.8%)
- Binary Search: ✅ 18/18 cases (100.0%)
- Palindrome: ✅ 18/18 cases (100.0%)
- Anagram Check: ✅ 18/18 cases (100.0%)
hhao/qwen2.5-coder-tools:7b
Overall Success Rate: 91.7% (66/72 cases)
Average Tokens/sec: 17.75 (16.05 - 17.75)
Average Duration: 9.35s
Min/Max Avg Duration: 4.17s / 9.35s
Test Results:
- Fibonacci: ❌ 12/18 cases (66.7%)
- Binary Search: ✅ 18/18 cases (100.0%)
- Palindrome: ✅ 18/18 cases (100.0%)
- Anagram Check: ✅ 18/18 cases (100.0%)
Qwen2.5-Coder-7B-Instruct-s1k:latest
Overall Success Rate: 88.9% (64/72 cases)
Average Tokens/sec: 18.38 (18.38 - 18.94)
Average Duration: 9.95s
Min/Max Avg Duration: 9.06s / 12.91s
Test Results:
- Fibonacci: ❌ 16/18 cases (88.9%)
- Binary Search: ❌ 12/18 cases (66.7%)
- Palindrome: ✅ 18/18 cases (100.0%)
- Anagram Check: ✅ 18/18 cases (100.0%)
deepseek-r1:8b
Overall Success Rate: 86.1% (62/72 cases)
Average Tokens/sec: 17.43 (17.29 - 18.01)
Average Duration: 168.97s
Min/Max Avg Duration: 107.91s / 168.97s
Test Results:
- Fibonacci: ✅ 18/18 cases (100.0%)
- Binary Search: ✅ 18/18 cases (100.0%)
- Palindrome: ❌ 16/18 cases (88.9%)
- Anagram Check: ❌ 10/18 cases (55.6%)
llama3.2:1b-instruct-q4_K_M
olmo2:13b-1124-instruct-q4_K_M
Overall Success Rate: 81.9% (59/72 cases)
Average Tokens/sec: 88.24 (88.24 - 88.93)
Average Duration: 3.64s
Min/Max Avg Duration: 1.87s / 4.93s
Average Tokens/sec: 9.16 (8.49 - 9.16)
Average Duration: 27.80s
Min/Max Avg Duration: 9.97s / 27.80s
Test Results:
- Fibonacci: ❌ 5/18 cases (27.8%)
- Binary Search: ✅ 18/18 cases (100.0%)
- Palindrome: ✅ 18/18 cases (100.0%)
- Anagram Check: ✅ 18/18 cases (100.0%)
samantha-mistral:latest
Overall Success Rate: 80.6% (58/72 cases)
Average Tokens/sec: 23.92 (23.91 - 24.79)
Average Duration: 12.21s
Min/Max Avg Duration: 7.59s / 12.21s
Test Results:
- Fibonacci: ❌ 8/18 cases (44.4%)
- Binary Search: ✅ 18/18 cases (100.0%)
- Palindrome: ❌ 16/18 cases (88.9%)
- Anagram Check: ❌ 16/18 cases (88.9%)
marco-o1:latest
Overall Success Rate: 80.6% (58/72 cases)
Average Tokens/sec: 19.19 (19.19 - 19.39)
Average Duration: 41.14s
Min/Max Avg Duration: 33.28s / 51.50s
Test Results:
- Fibonacci: ✅ 18/18 cases (100.0%)
- Binary Search: ❌ 6/18 cases (33.3%)
- Palindrome: ✅ 18/18 cases (100.0%)
- Anagram Check: ❌ 16/18 cases (88.9%)
deepseek-r1:7b
Overall Success Rate: 80.6% (58/72 cases)
Average Tokens/sec: 18.01 (18.01 - 19.07)
Average Duration: 336.87s
Min/Max Avg Duration: 78.71s / 336.87s
Test Results:
- Fibonacci: ❌ 10/18 cases (55.6%)
- Binary Search: ✅ 18/18 cases (100.0%)
- Palindrome: ❌ 12/18 cases (66.7%)
- Anagram Check: ✅ 18/18 cases (100.0%)
deepseek-r1:1.5b-qwen-distill-q8_0
Overall Success Rate: 52.8% (38/72 cases)
Average Tokens/sec: 57.37 (53.88 - 59.60)
Average Duration: 137.59s
Min/Max Avg Duration: 41.38s / 371.13s
Test Results:
- Fibonacci: ❌ 11/18 cases (61.1%)
- Binary Search: ❌ 12/18 cases (66.7%)
- Palindrome: ❌ 6/18 cases (33.3%)
- Anagram Check: ❌ 9/18 cases (50.0%)
openthinker:7b
Overall Success Rate: 47.2% (34/72 cases)
Average Tokens/sec: 18.16 (17.98 - 18.29)
Average Duration: 263.00s
Min/Max Avg Duration: 168.91s / 302.79s
Test Results:
- Fibonacci: ❌ 0/18 cases (0.0%)
- Binary Search: ✅ 18/18 cases (100.0%)
- Palindrome: ❌ 12/18 cases (66.7%)
- Anagram Check: ❌ 4/18 cases (22.2%)
wizard-vicuna-uncensored:latest
Overall Success Rate: 9.7% (7/72 cases)
Average Tokens/sec: 22.01 (22.01 - 24.42)
Average Duration: 9.06s
Min/Max Avg Duration: 5.60s / 11.45s
Test Results:
- Fibonacci: ❌ 0/18 cases (0.0%)
- Binary Search: ❌ 0/18 cases (0.0%)
- Palindrome: ❌ 6/18 cases (33.3%)
- Anagram Check: ❌ 1/18 cases (5.6%)
mxbai-embed-large:latest
Overall Success Rate: 0.0% (0/72 cases)
Average Tokens/sec: 0.00 (0.00 - 0.00)
Average Duration: 0.00s
Min/Max Avg Duration: 0.00s / 0.00s
Test Results:
- Fibonacci: ❌ 0/18 cases (0.0%)
- Binary Search: ❌ 0/18 cases (0.0%)
- Palindrome: ❌ 0/18 cases (0.0%)
- Anagram Check: ❌ 0/18 cases (0.0%)

BIN
benchmark_results/model_comparison.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 2.3 KiB

After

Width:  |  Height:  |  Size: 490 KiB

0
computest.py
View File

67
lboard.py
View File

@ -17,15 +17,52 @@ def calculate_model_stats(model_result):
success_rates = [test['success_rate'] for test in test_results.values()]
overall_success_rate = sum(success_rates) / len(success_rates)
# Handle the case where some test results might not have avg_duration or avg_tokens_sec
# This is for backward compatibility with older benchmark results
min_avg_duration = max_avg_duration = None
min_tokens_per_second = max_tokens_per_second = None
# First try to get these values from the model_result directly (new format)
if 'min_avg_duration' in model_result and 'max_avg_duration' in model_result:
min_avg_duration = model_result['min_avg_duration']
max_avg_duration = model_result['max_avg_duration']
if 'min_tokens_per_second' in model_result and 'max_tokens_per_second' in model_result:
min_tokens_per_second = model_result['min_tokens_per_second']
max_tokens_per_second = model_result['max_tokens_per_second']
# If not available in the model_result, try to calculate from test_results (old format)
if min_avg_duration is None or max_avg_duration is None:
try:
min_avg_duration = min(test.get('avg_duration', float('inf')) for test in test_results.values() if 'avg_duration' in test)
max_avg_duration = max(test.get('avg_duration', 0) for test in test_results.values() if 'avg_duration' in test)
# If no test has avg_duration, use total_duration as fallback
if min_avg_duration == float('inf') or max_avg_duration == 0:
min_avg_duration = max_avg_duration = model_result['total_duration']
except (ValueError, KeyError):
# If calculation fails, use total_duration as fallback
min_avg_duration = max_avg_duration = model_result['total_duration']
if min_tokens_per_second is None or max_tokens_per_second is None:
try:
min_tokens_per_second = min(test.get('avg_tokens_sec', float('inf')) for test in test_results.values() if 'avg_tokens_sec' in test)
max_tokens_per_second = max(test.get('avg_tokens_sec', 0) for test in test_results.values() if 'avg_tokens_sec' in test)
# If no test has avg_tokens_sec, use tokens_per_second as fallback
if min_tokens_per_second == float('inf') or max_tokens_per_second == 0:
min_tokens_per_second = max_tokens_per_second = model_result['tokens_per_second']
except (ValueError, KeyError):
# If calculation fails, use tokens_per_second as fallback
min_tokens_per_second = max_tokens_per_second = model_result['tokens_per_second']
return {
'model': model_result['model'],
'overall_success_rate': overall_success_rate,
'tokens_per_second': model_result['tokens_per_second'],
'total_duration': model_result['total_duration'],
'min_avg_duration': model_result.get('min_avg_duration', min(test['avg_duration'] for test in test_results.values())),
'max_avg_duration': model_result.get('max_avg_duration', max(test['avg_duration'] for test in test_results.values())),
'min_tokens_per_second': model_result.get('min_tokens_per_second', min(test['avg_tokens_sec'] for test in test_results.values())),
'max_tokens_per_second': model_result.get('max_tokens_per_second', max(test['avg_tokens_sec'] for test in test_results.values())),
'min_avg_duration': min_avg_duration,
'max_avg_duration': max_avg_duration,
'min_tokens_per_second': min_tokens_per_second,
'max_tokens_per_second': max_tokens_per_second,
'test_results': test_results
}
@ -120,12 +157,26 @@ def print_leaderboard(benchmark_data):
print("No benchmark data to display")
return
# Get the latest benchmark results
latest_benchmark = benchmark_data['benchmarks'][-1]
model_results = latest_benchmark['results']
# Get all benchmark results and combine them
all_model_results = []
model_names = set()
# Process all benchmarks, keeping only the latest result for each model
for benchmark in benchmark_data['benchmarks']:
for model_result in benchmark.get('results', []):
model_name = model_result.get('model')
if model_name and model_name not in model_names:
all_model_results.append(model_result)
model_names.add(model_name)
elif model_name in model_names:
# Replace existing model with newer version
for i, existing_model in enumerate(all_model_results):
if existing_model.get('model') == model_name:
all_model_results[i] = model_result
break
# Calculate stats and sort models
model_stats = [calculate_model_stats(model) for model in model_results]
model_stats = [calculate_model_stats(model) for model in all_model_results]
sorted_stats = sorted(model_stats,
key=lambda x: (x['overall_success_rate'], x['tokens_per_second']),
reverse=True)

756
main copie.py
View File

@ -1,756 +0,0 @@
from tabnanny import verbose
import ollama
import time
from typing import List, Dict, Any
import json
from statistics import mean
import re
import ast
import argparse
import requests
import os
from together import Together
from cpuinfo import get_cpu_info
import subprocess
# ANSI color codes
SUCCESS = '\033[38;5;78m' # Soft mint green for success
ERROR = '\033[38;5;203m' # Soft coral red for errors
INFO = '\033[38;5;75m' # Sky blue for info
HEADER = '\033[38;5;147m' # Soft purple for headers
WARNING = '\033[38;5;221m' # Warm gold for warnings
EMPHASIS = '\033[38;5;159m' # Cyan for emphasis
MUTED = '\033[38;5;246m' # Subtle gray for less important text
ENDC = '\033[0m'
BOLD = '\033[1m'
# Replace existing color usages
GREEN = SUCCESS
RED = ERROR
BLUE = INFO
YELLOW = WARNING
WHITE = MUTED
# Server configurations
SERVERS = {
'local': 'http://localhost:11434',
'z60': 'http://192.168.196.60:11434'
}
class Timer:
def __init__(self):
self.start_time = None
self.end_time = None
def start(self):
self.start_time = time.time()
def stop(self):
self.end_time = time.time()
def elapsed_time(self):
if self.start_time is None:
return 0
if self.end_time is None:
return time.time() - self.start_time
return self.end_time - self.start_time
def extract_code_from_response(response: str) -> str:
"""Extract Python code from a markdown-formatted string."""
code_blocks = re.findall(r'```python\n(.*?)```', response, re.DOTALL)
if code_blocks:
return code_blocks[0].strip()
return response
def is_valid_python(code: str) -> bool:
"""Check if the code is valid Python syntax."""
try:
ast.parse(code)
return True
except SyntaxError:
return False
def analyze_failed_code(code: str, test_case: tuple, expected: any, actual: any, function_name: str, model: str) -> bool:
"""Analyze why code failed using Together API. Returns True if Together thinks the code should work."""
prompt = f"""Analyze this Python code and explain why it failed the test case. Format your response EXACTLY as follows:
ASSESSMENT: [Write a one-line assessment: either "SHOULD PASS" or "SHOULD FAIL" followed by a brief reason]
ANALYSIS:
[Detailed analysis of why the code failed and how to fix it]
Code:
{code}
Test case:
Input: {test_case}
Expected output: {expected}
Actual output: {actual}
Function name required: {function_name}
Model: {model}"""
try:
TOGETHER_API_KEY = os.environ["TOGETHER_API_KEY"]
together_client = Together(api_key=TOGETHER_API_KEY)
response = together_client.chat.completions.create(
model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
messages=[
{"role": "system", "content": "You are a Python expert analyzing code failures. Always format your response with ASSESSMENT and ANALYSIS sections."},
{"role": "user", "content": prompt}
],
max_tokens=1000,
temperature=0.7,
top_p=0.7,
top_k=50,
repetition_penalty=1,
stop=["<|eot_id|>", "<|eom_id|>"]
)
analysis = response.choices[0].message.content
should_pass = "SHOULD PASS" in analysis.upper()
if verbose: print(f"\n{BLUE}[{model}] Together Analysis:{ENDC}")
if verbose: print(f"{GREEN if should_pass else RED}{analysis}{ENDC}")
return should_pass
except Exception as e:
print(f"\n{RED}Error getting Together API analysis: {e}{ENDC}")
return False
def validate_with_debug(code: str, function_name: str, test_cases: List[tuple], model: str) -> tuple[bool, str, List[bool]]:
"""Validate code with detailed debug information. Returns (success, debug_info, test_results)"""
debug_info = []
test_results = [] # Track individual test case results
test_outputs = [] # Store test outputs for combined display
try:
# Create a local namespace
namespace = {}
debug_info.append(f"Executing code:\n{code}")
try:
# Redirect stdout to capture prints from the executed code
import io
import sys
stdout = sys.stdout
sys.stdout = io.StringIO()
# Execute the code
exec(code, namespace)
# Restore stdout
sys.stdout = stdout
except Exception as e:
if 'sys' in locals(): # Restore stdout if it was changed
sys.stdout = stdout
if verbose: print(f"\n{RED}Failed code:{ENDC}\n{code}")
return False, f"Error executing code: {str(e)}", test_results
if function_name not in namespace:
if verbose: print(f"\n{RED}Failed code:{ENDC}\n{code}")
together_opinion = analyze_failed_code(code, "N/A", f"Function named '{function_name}'",
f"Found functions: {list(namespace.keys())}", function_name, model)
print(f"\nTests passed: ❌ Together opinion: {'✅' if together_opinion else '❌'}")
return False, f"Function '{function_name}' not found in code. Available names: {list(namespace.keys())}", test_results
function = namespace[function_name]
debug_info.append(f"Function {function_name} found")
# Run test cases
all_passed = True
for i, (test_input, expected) in enumerate(test_cases):
try:
# Redirect stdout for each test case
stdout = sys.stdout
sys.stdout = io.StringIO()
if isinstance(test_input, tuple):
result = function(*test_input)
else:
result = function(test_input)
# Restore stdout
sys.stdout = stdout
# Store result but don't print individually
test_outputs.append(str(result))
test_passed = result == expected
test_results.append(test_passed)
if not test_passed:
if verbose: print(f"\n{RED}Failed code:{ENDC}\n{code}")
print(f"\n{RED}Test case {i+1} failed:{ENDC}")
print(f"Input: {test_input} Expected: {expected} Got: {result}")
together_opinion = analyze_failed_code(code, test_input, expected, result, function_name, model)
print(f"Tests passed: ❌ Together opinion: {'✅' if together_opinion else '❌'}")
all_passed = False
continue
debug_info.append(f"Test case {i+1} passed: {test_input} → {result}")
except Exception as e:
if 'sys' in locals(): # Restore stdout if it was changed
sys.stdout = stdout
test_outputs.append(f"Error: {str(e)}")
if verbose: print(f"\n{RED}Failed code:{ENDC}\n{code}")
print(f"\n{RED}{str(e)} in test case {i+1} Input: {test_input} Expected: {expected}")
together_opinion = analyze_failed_code(code, test_input, expected, f"Error: {str(e)}", function_name, model)
print(f"Tests passed: ❌ Together opinion: {'✅' if together_opinion else '❌'}")
test_results.append(False)
all_passed = False
continue
finally:
if 'sys' in locals(): # Always restore stdout
sys.stdout = stdout
# Print all test outputs on one line
# print(f"{WHITE}{BOLD}Test outputs: {join(test_outputs)}{ENDC}")
print(f"{WHITE}Test outputs: {', '.join(test_outputs)}{ENDC}")
if all_passed:
print(f"Tests passed: ✅")
return True, "All tests passed!\n" + "\n".join(debug_info), test_results
print(f"Tests passed: ❌")
return False, "Some tests failed", test_results
except Exception as e:
if 'sys' in locals(): # Restore stdout if it was changed
sys.stdout = stdout
print(f"\n{RED}Error in validate_with_debug: {str(e)}{ENDC}")
return False, f"Unexpected error: {str(e)}", test_results
def test_fibonacci():
question = """Write a Python function named EXACTLY 'fibonacci' (not fibonacci_dp or any other name) that returns the nth Fibonacci number.
The function signature must be: def fibonacci(n)
Requirements:
1. Handle edge cases:
- For n = 0, return 0
- For n = 1 or n = 2, return 1
- For negative numbers, return -1
2. For n > 2: F(n) = F(n-1) + F(n-2)
3. Use dynamic programming or memoization for efficiency
4. Do NOT use any print statements - just return the values
Example sequence: 0,1,1,2,3,5,8,13,21,...
Example calls:
- fibonacci(6) returns 8
- fibonacci(0) returns 0
- fibonacci(-1) returns -1"""
test_cases = [
(0, 0), # Edge case: n = 0
(1, 1), # Edge case: n = 1
(2, 1), # Edge case: n = 2
(6, 8), # Regular case
(10, 55), # Larger number
(-1, -1), # Edge case: negative input
]
def validate(code: str) -> bool:
success, debug_info, test_results = validate_with_debug(code, 'fibonacci', test_cases, "N/A")
return success
return (question, validate, test_cases)
def test_binary_search():
question = """Write a Python function named EXACTLY 'binary_search' that performs binary search on a sorted list.
The function signature must be: def binary_search(arr, target)
Requirements:
1. The function takes two arguments:
- arr: a sorted list of integers
- target: the integer to find
2. Return the index of the target if found
3. Return -1 if the target is not in the list
4. Do NOT use any print statements - just return the values
Example:
- binary_search([1,2,3,4,5], 3) returns 2
- binary_search([1,2,3,4,5], 6) returns -1"""
test_cases = [
(([1,2,3,4,5], 3), 2), # Regular case: target in middle
(([1,2,3,4,5], 1), 0), # Edge case: target at start
(([1,2,3,4,5], 5), 4), # Edge case: target at end
(([1,2,3,4,5], 6), -1), # Edge case: target not in list
(([], 1), -1), # Edge case: empty list
(([1], 1), 0), # Edge case: single element list
]
def validate(code: str) -> bool:
success, debug_info, test_results = validate_with_debug(code, 'binary_search', test_cases, "N/A")
return success
return (question, validate, test_cases)
def test_palindrome():
question = """Write a Python function named EXACTLY 'is_palindrome' that checks if a string is a palindrome.
The function signature must be: def is_palindrome(s)
Requirements:
1. The function takes one argument:
- s: a string to check
2. Return True if the string is a palindrome, False otherwise
3. Ignore case (treat uppercase and lowercase as the same)
4. Ignore non-alphanumeric characters (spaces, punctuation)
5. Do NOT use any print statements - just return the values
Example:
- is_palindrome("A man, a plan, a canal: Panama") returns True
- is_palindrome("race a car") returns False"""
test_cases = [
("A man, a plan, a canal: Panama", True), # Regular case with punctuation
("race a car", False), # Regular case, not palindrome
("", True), # Edge case: empty string
("a", True), # Edge case: single character
("Was it a car or a cat I saw?", True), # Complex case with punctuation
("hello", False), # Simple case, not palindrome
]
def validate(code: str) -> bool:
success, debug_info, test_results = validate_with_debug(code, 'is_palindrome', test_cases, "N/A")
return success
return (question, validate, test_cases)
def test_anagram():
question = """Write a Python function named EXACTLY 'are_anagrams' that checks if two strings are anagrams.
The function signature must be: def are_anagrams(str1, str2)
Requirements:
1. The function takes two arguments:
- str1: first string
- str2: second string
2. Return True if the strings are anagrams, False otherwise
3. Ignore case (treat uppercase and lowercase as the same)
4. Ignore spaces
5. Consider only alphanumeric characters
6. Do NOT use any print statements - just return the values
Example:
- are_anagrams("listen", "silent") returns True
- are_anagrams("hello", "world") returns False"""
test_cases = [
(("listen", "silent"), True), # Regular case
(("hello", "world"), False), # Not anagrams
(("", ""), True), # Edge case: empty strings
(("a", "a"), True), # Edge case: single char
(("Debit Card", "Bad Credit"), True), # Case and space test
(("Python", "Java"), False), # Different lengths
]
def validate(code: str) -> bool:
success, debug_info, test_results = validate_with_debug(code, 'are_anagrams', test_cases, "N/A")
return success
return (question, validate, test_cases)
# List of all test cases
CODING_QUESTIONS = [
test_fibonacci(),
test_binary_search(),
test_palindrome(),
test_anagram()
]
# Add test names as constants
TEST_NAMES = {
"Write a Python func": "Fibonacci",
"Write a Python func": "Binary Search",
"Write a Python func": "Palindrome",
"Write a Python func": "Anagram Check"
}
def get_test_name(question: str) -> str:
"""Get a friendly name for the test based on the question."""
if "fibonacci" in question.lower():
return "Fibonacci"
elif "binary_search" in question.lower():
return "Binary Search"
elif "palindrome" in question.lower():
return "Palindrome"
elif "anagram" in question.lower():
return "Anagram Check"
return question[:20] + "..."
def get_model_stats(model: str, question_tuple: tuple, server_url: str) -> Dict:
"""
Get performance statistics for a specific model and validate the response.
"""
question, validator = question_tuple
timer = Timer()
results = {
'model': model,
'total_duration': 0,
'tokens_per_second': 0,
'code_valid': False,
'tests_passed': False,
'error': None,
'test_results': [] # Track individual test case results
}
try:
timer.start()
print(f'{WHITE}Requesting code from {server_url} with {model}{ENDC}')
response = requests.post(
f"{server_url}/api/chat",
json={
"model": model,
"messages": [{'role': 'user', 'content': question}],
"stream": False
}
).json()
timer.stop()
# Get performance metrics from response
total_tokens = response.get('eval_count', 0)
total_duration = response.get('total_duration', 0)
total_response_time = float(total_duration) / 1e9
results['total_duration'] = total_response_time
if total_tokens > 0 and total_response_time > 0:
results['tokens_per_second'] = total_tokens / total_response_time
# Print concise performance metrics
print(f"Total Duration (s): {total_response_time:.2f} / Total Tokens: {total_tokens} / Tokens per Second: {results['tokens_per_second']:.2f}")
# Extract code from response
if 'message' in response and 'content' in response['message']:
code = extract_code_from_response(response['message']['content'])
# Validate code
results['code_valid'] = is_valid_python(code)
if results['code_valid']:
print(f"Code validation: ✅")
# Get validation results
print(f'{WHITE}Running tests...{ENDC}')
for test_case in CODING_QUESTIONS:
if test_case[0] == question: # Found matching test case
function_name = get_function_name_from_question(question)
test_cases = test_case[2] # Get test cases from tuple
success, debug_info, test_results = validate_with_debug(code, function_name, test_cases, model)
results['tests_passed'] = success
results['test_results'] = test_results
break
else:
print(f"Code Validation: ❌")
else:
results['error'] = f"Unexpected response format: {response}"
except Exception as e:
print(f"\n{RED}Error in get_model_stats: {str(e)}{ENDC}")
results['error'] = str(e)
return results
def get_function_name_from_question(question: str) -> str:
"""Extract function name from question."""
if "fibonacci" in question.lower():
return "fibonacci"
elif "binary_search" in question.lower():
return "binary_search"
elif "palindrome" in question.lower():
return "is_palindrome"
elif "anagram" in question.lower():
return "are_anagrams"
return ""
def run_model_benchmark(model: str, server_url: str, num_runs: int = 4) -> Dict:
"""
Run multiple benchmarks for a model and calculate average metrics.
"""
metrics = []
for i in range(num_runs):
print(f"\n{YELLOW}[{model}] Run {i+1}/{num_runs}:{ENDC}")
run_results = {}
for question, validator, test_cases in CODING_QUESTIONS:
test_name = get_test_name(question)
print(f"\n{BOLD}Testing {test_name}...{ENDC}")
try:
result = get_model_stats(model, (question, validator), server_url)
result['total_tests'] = len(test_cases)
run_results[test_name] = result
except Exception as e:
print(f"Error in run {i+1}: {e}")
continue
if run_results:
metrics.append(run_results)
# Take only the last 3 runs for averaging
metrics = metrics[-3:]
if not metrics:
return {}
# Aggregate results
aggregated = {
'model': model,
'total_duration': mean([m[list(m.keys())[0]]['total_duration'] for m in metrics if m]),
'tokens_per_second': mean([m[list(m.keys())[0]]['tokens_per_second'] for m in metrics if m]),
'test_results': {}
}
# Print final test results summary
print(f"\n{BLUE}[{model}] Test Results Summary (last {len(metrics)} runs):{ENDC}")
for test_name in metrics[-1].keys():
# Calculate success rate across all runs
passed_cases = 0
total_cases = 0
for m in metrics:
if test_name in m:
test_results = m[test_name].get('test_results', [])
passed_cases += sum(1 for r in test_results if r)
total_cases += len(test_results)
success_rate = (passed_cases / total_cases * 100) if total_cases > 0 else 0
status = '✅' if success_rate == 100 else '❌'
print(f"{test_name}: {status} ({passed_cases}/{total_cases} cases)")
# Calculate average duration and tokens/sec for this test
avg_duration = mean([m[test_name]['total_duration'] for m in metrics])
avg_tokens_sec = mean([m[test_name]['tokens_per_second'] for m in metrics])
aggregated['test_results'][test_name] = {
'success_rate': success_rate,
'passed_cases': passed_cases,
'total_cases': total_cases,
'avg_duration': avg_duration,
'avg_tokens_sec': avg_tokens_sec
}
return aggregated
def print_leaderboard(results: List[Dict]):
"""Print leaderboard of model results."""
if not results:
print("No results to display")
return
# Sort by success rate first, then by tokens per second
sorted_results = sorted(results, key=lambda x: (
sum(t['passed_cases'] for t in x['test_results'].values()) / sum(t['total_cases'] for t in x['test_results'].values()) if sum(t['total_cases'] for t in x['test_results'].values()) > 0 else 0,
x['tokens_per_second']
), reverse=True)
print(f"\n{HEADER}{BOLD}🏆 Final Model Leaderboard:{ENDC}")
for i, result in enumerate(sorted_results, 1):
# Calculate stats for each model
total_passed = sum(t['passed_cases'] for t in result['test_results'].values())
total_cases = sum(t['total_cases'] for t in result['test_results'].values())
success_rate = (total_passed / total_cases * 100) if total_cases > 0 else 0
print(f"\n{BOLD}{YELLOW}{result['model']}{ENDC}")
print(f" {BOLD}Overall Success Rate:{ENDC} {success_rate:.1f}% ({total_passed}/{total_cases} cases)")
print(f" {BOLD}Average Tokens/sec:{ENDC} {result['tokens_per_second']:.2f}")
print(f" {BOLD}Average Duration:{ENDC} {result['total_duration']:.2f}s")
print(f" {BOLD}Test Results:{ENDC}")
for test_name, test_result in result['test_results'].items():
status = '✅' if test_result['success_rate'] == 100 else '❌'
print(f" - {test_name}: {status} {test_result['passed_cases']}/{test_result['total_cases']} cases ({test_result['success_rate']:.1f}%)")
def get_available_models(server_url: str) -> List[str]:
"""Get list of available models from the specified Ollama server."""
try:
response = requests.get(f"{server_url}/api/tags").json()
return [model['name'] for model in response['models']]
except Exception as e:
print(f"{RED}Error getting model list from {server_url}: {e}{ENDC}")
return []
def get_model_details(model_name):
try:
result = subprocess.run(
["ollama", "show", model_name],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
encoding='utf-8',
errors='replace'
)
if result.returncode != 0:
print(f"Error: {result.stderr.strip()}")
return None
if not result.stdout.strip():
print(f"No details available for model: {model_name}")
return None
raw_output = result.stdout.strip()
lines = raw_output.split('\n')
current_section = None
for line in lines:
line = line.rstrip()
if line and not line.startswith(' '): # Section headers
current_section = line.strip()
print(f"\n {current_section}")
elif line and current_section: # Section content
# Split by multiple spaces and filter out empty parts
parts = [part for part in line.split(' ') if part.strip()]
if len(parts) >= 2:
key, value = parts[0].strip(), parts[-1].strip()
# Ensure consistent spacing for alignment
print(f" {key:<16} {value}")
elif len(parts) == 1:
# Handle single-value lines (like license text)
print(f" {parts[0].strip()}")
return None # No need to return formatted details anymore
except Exception as e:
print(f"An error occurred while getting model details: {e}")
return None
def update_server_results(server_url: str, results: List[Dict]) -> None:
try:
# Get CPU brand and format it for filename
cpu_info = get_cpu_info()
cpu_brand = cpu_info.get('brand_raw', 'Unknown_CPU').replace(' ', '_')
# Create a unique filename for this server's results
server_id = server_url.replace('http://', '').replace(':', '_').replace('/', '_')
results_dir = "benchmark_results"
# Create results directory if it doesn't exist
os.makedirs(results_dir, exist_ok=True)
# Include CPU brand in filename
filename = os.path.join(results_dir, f"{cpu_brand}_{server_id}.json")
timestamp = time.strftime("%Y%m%d_%H%M%S")
# Load existing results or create new file
try:
with open(filename, 'r') as f:
existing_data = json.load(f)
except FileNotFoundError:
existing_data = {
'server_url': server_url,
'benchmarks': []
}
# Add new results with timestamp
existing_data['benchmarks'].append({
'timestamp': timestamp,
'results': results
})
# Save updated results
with open(filename, 'w') as f:
json.dump(existing_data, f, indent=2)
print(f"{GREEN}Successfully saved results to {filename}{ENDC}")
except Exception as e:
print(f"{RED}Failed to save results: {str(e)}{ENDC}")
def main():
parser = argparse.ArgumentParser(description='Run Ollama model benchmarks')
parser.add_argument('--server', choices=['local', 'z60'], default='local',
help='Choose Ollama server (default: local)')
parser.add_argument('--model', type=str, help='Specific model to benchmark')
parser.add_argument('--number', type=str, help='Number of models to benchmark (number or "all")')
parser.add_argument('--verbose', action='store_true', help='Enable verbose output')
args = parser.parse_args()
server_url = SERVERS[args.server]
print()
print(f"{HEADER}{BOLD}CPU Information:{ENDC}")
cpu_info = get_cpu_info()
for key, value in cpu_info.items():
print(f"{MUTED}{key}: {value}{ENDC}")
print()
print(f"{INFO}Using Ollama server at {server_url}...{ENDC}")
# Get available models or use specified model
if args.model:
models = [args.model]
else:
models = get_available_models(server_url)
if not models:
print(f"{RED}No models found on server {server_url}. Exiting.{ENDC}")
return
# Handle number of models to test
if args.number and args.number.lower() != 'all':
try:
num_models = int(args.number)
if num_models > 0:
models = models[:num_models]
else:
print(f"{WARNING}Invalid number of models. Using all available models.{ENDC}")
except ValueError:
print(f"{WARNING}Invalid number format. Using all available models.{ENDC}")
print(f"{INFO}Testing {len(models)} models :{ENDC}")
for i, model in enumerate(models, 1):
print(f"{YELLOW}{i}. {model}{ENDC}")
# Run benchmarks
all_results = []
for model in models:
print(f"\n{HEADER}{BOLD}Benchmarking {model}...{ENDC}")
details = get_model_details(model)
if details:
print(f"\n{INFO}Model Details:{ENDC}")
if "details" in details:
for section, items in details["details"].items():
print(f"\n{BOLD}{section}{ENDC}")
for key, value in items.items():
print(f" {key}: {value}")
else:
print(json.dumps(details, indent=2))
result = run_model_benchmark(model, server_url)
if 'error' not in result:
all_results.append(result)
# Print and save results
print_leaderboard(all_results)
update_server_results(server_url, all_results)
'''
# Create leaderboard data structure
leaderboard = []
for result in sorted(all_results, key=lambda x: (
sum(t['passed_cases'] for t in x['test_results'].values()) / sum(t['total_cases'] for t in x['test_results'].values()) if sum(t['total_cases'] for t in x['test_results'].values()) > 0 else 0,
x['tokens_per_second']
), reverse=True):
total_passed = sum(t['passed_cases'] for t in result['test_results'].values())
total_cases = sum(t['total_cases'] for t in result['test_results'].values())
success_rate = (total_passed / total_cases * 100) if total_cases > 0 else 0
leaderboard.append({
'model': result['model'],
'success_rate': success_rate,
'total_passed': total_passed,
'total_cases': total_cases,
'tokens_per_second': result['tokens_per_second'],
'average_duration': result['total_duration']
})
# Save detailed results and leaderboard to file
timestamp = time.strftime("%Y%m%d_%H%M%S")
filename = f"benchmark_results/model_benchmark_{timestamp}.json"
with open(filename, 'w') as f:
json.dump({
'timestamp': timestamp,
'server_url': server_url,
'leaderboard': leaderboard,
'detailed_results': all_results
}, f, indent=2)
print(f"\n{GREEN}Detailed results saved to {filename}{ENDC}")
'''
if __name__ == "__main__":
main()

234
main.py
View File

@ -9,6 +9,8 @@ import ast
import argparse
import requests
import os
import glob
import matplotlib.pyplot as plt
from together import Together
from cpuinfo import get_cpu_info
import subprocess
@ -633,7 +635,7 @@ def get_model_details(model_name):
print(f"An error occurred while getting model details: {e}")
return None
def update_server_results(server_url: str, results: List[Dict]) -> None:
def update_server_results(server_url: str, results: List[Dict]) -> str:
try:
# Get CPU brand and format it for filename
cpu_info = get_cpu_info()
@ -711,8 +713,215 @@ def update_server_results(server_url: str, results: List[Dict]) -> None:
print(f"{GREEN}Console output saved to {log_filename}{ENDC}")
return json_filename
except Exception as e:
print(f"{RED}Failed to save results: {str(e)}{ENDC}")
return None
def plot_benchmark_results(json_file=None):
"""
Plot benchmark results using the same functionality as lboard.py
Args:
json_file: Path to the JSON file with benchmark results. If None, uses the latest file.
"""
try:
# If no file specified, find the latest
if not json_file:
json_file = get_latest_json_file('benchmark_results')
if not json_file:
print(f"{RED}No benchmark results found{ENDC}")
return
with open(json_file, 'r') as f:
benchmark_data = json.load(f)
print(f"{INFO}Using benchmark file: {json_file}{ENDC}")
# Get all benchmark results and combine them
all_model_results = []
model_names = set()
# Process all benchmarks, keeping only the latest result for each model
for benchmark in benchmark_data['benchmarks']:
for model_result in benchmark.get('results', []):
model_name = model_result.get('model')
if model_name and model_name not in model_names:
all_model_results.append(model_result)
model_names.add(model_name)
elif model_name in model_names:
# Replace existing model with newer version
for i, existing_model in enumerate(all_model_results):
if existing_model.get('model') == model_name:
all_model_results[i] = model_result
break
# Calculate stats and sort models
model_stats = [calculate_model_stats(model) for model in all_model_results]
sorted_stats = sorted(model_stats,
key=lambda x: (x['overall_success_rate'], x['tokens_per_second']),
reverse=True)
print(f"\n🏆 Final Model Leaderboard:")
for stats in sorted_stats:
print(f"\n{stats['model']}")
print(f" Overall Success Rate: {stats['overall_success_rate']:.1f}%")
print(f" Average Tokens/sec: {stats['tokens_per_second']:.2f} ({stats['min_tokens_per_second']:.2f} - {stats['max_tokens_per_second']:.2f})")
print(f" Average Duration: {stats['total_duration']:.2f}s")
print(f" Min/Max Avg Duration: {stats['min_avg_duration']:.2f}s / {stats['max_avg_duration']:.2f}s")
print(f" Test Results:")
for test_name, test_result in stats['test_results'].items():
status = '✅' if test_result['success_rate'] == 100 else '❌'
print(f" - {test_name}: {status} {test_result['passed_cases']}/{test_result['total_cases']} cases ({test_result['success_rate']:.1f}%)")
# Generate visualization
plot_model_comparison(sorted_stats)
except Exception as e:
print(f"{RED}Error loading benchmark data: {e}{ENDC}")
def calculate_model_stats(model_result):
"""Calculate average stats for a model from its test results."""
test_results = model_result['test_results']
# Calculate overall success rate (average of all test success rates)
success_rates = [test['success_rate'] for test in test_results.values()]
overall_success_rate = sum(success_rates) / len(success_rates)
# Handle the case where some test results might not have avg_duration or avg_tokens_sec
# This is for backward compatibility with older benchmark results
min_avg_duration = max_avg_duration = None
min_tokens_per_second = max_tokens_per_second = None
# First try to get these values from the model_result directly (new format)
if 'min_avg_duration' in model_result and 'max_avg_duration' in model_result:
min_avg_duration = model_result['min_avg_duration']
max_avg_duration = model_result['max_avg_duration']
if 'min_tokens_per_second' in model_result and 'max_tokens_per_second' in model_result:
min_tokens_per_second = model_result['min_tokens_per_second']
max_tokens_per_second = model_result['max_tokens_per_second']
# If not available in the model_result, try to calculate from test_results (old format)
if min_avg_duration is None or max_avg_duration is None:
try:
min_avg_duration = min(test.get('avg_duration', float('inf')) for test in test_results.values() if 'avg_duration' in test)
max_avg_duration = max(test.get('avg_duration', 0) for test in test_results.values() if 'avg_duration' in test)
# If no test has avg_duration, use total_duration as fallback
if min_avg_duration == float('inf') or max_avg_duration == 0:
min_avg_duration = max_avg_duration = model_result['total_duration']
except (ValueError, KeyError):
# If calculation fails, use total_duration as fallback
min_avg_duration = max_avg_duration = model_result['total_duration']
if min_tokens_per_second is None or max_tokens_per_second is None:
try:
min_tokens_per_second = min(test.get('avg_tokens_sec', float('inf')) for test in test_results.values() if 'avg_tokens_sec' in test)
max_tokens_per_second = max(test.get('avg_tokens_sec', 0) for test in test_results.values() if 'avg_tokens_sec' in test)
# If no test has avg_tokens_sec, use tokens_per_second as fallback
if min_tokens_per_second == float('inf') or max_tokens_per_second == 0:
min_tokens_per_second = max_tokens_per_second = model_result['tokens_per_second']
except (ValueError, KeyError):
# If calculation fails, use tokens_per_second as fallback
min_tokens_per_second = max_tokens_per_second = model_result['tokens_per_second']
return {
'model': model_result['model'],
'overall_success_rate': overall_success_rate,
'tokens_per_second': model_result['tokens_per_second'],
'total_duration': model_result['total_duration'],
'min_avg_duration': min_avg_duration,
'max_avg_duration': max_avg_duration,
'min_tokens_per_second': min_tokens_per_second,
'max_tokens_per_second': max_tokens_per_second,
'test_results': test_results
}
def plot_model_comparison(model_stats):
"""Plot model comparison with dual y-axes for tokens/sec and success rate."""
models = [stat['model'] for stat in model_stats]
token_speeds = [stat['tokens_per_second'] for stat in model_stats]
success_rates = [stat['overall_success_rate'] for stat in model_stats]
durations = [stat['total_duration'] for stat in model_stats]
# Create figure and primary axis
fig, ax1 = plt.subplots(figsize=(15, 8))
# Plot tokens/sec bars using min and max values
for i, stat in enumerate(model_stats):
min_tokens = stat['min_tokens_per_second']
max_tokens = stat['max_tokens_per_second']
# Plot lower part (0 to min) with slightly darker blue
ax1.bar(i, min_tokens, color='royalblue', alpha=0.4)
# Plot upper part (min to max) with lighter blue
bar_height = max_tokens - min_tokens
ax1.bar(i, bar_height, bottom=min_tokens, color='royalblue', alpha=0.3)
ax1.set_ylabel('Tokens per Second', color='blue')
ax1.tick_params(axis='y', labelcolor='blue')
# Set y-axis range for tokens per second
max_token_speed = max(stat['max_tokens_per_second'] for stat in model_stats)
ax1.set_ylim(0, max(100, max_token_speed * 1.1)) # Add 10% padding above max value
# Set x-axis labels
ax1.set_xticks(range(len(models)))
ax1.set_xticklabels(models, rotation=45, ha='right', rotation_mode='anchor')
# Create secondary y-axis for success rate
ax2 = ax1.twinx()
ax2.plot(models, success_rates, 'r+', markersize=15, label='Success Rate', linestyle='None')
ax2.set_ylabel('Success Rate (%)', color='red')
ax2.tick_params(axis='y', labelcolor='red')
ax2.set_ylim(0, 100)
# Create third y-axis for duration
ax3 = ax1.twinx()
ax3.spines['right'].set_position(('outward', 60)) # Move third axis outward
# Add min and max duration markers
min_durations = [stat['min_avg_duration'] for stat in model_stats]
max_durations = [stat['max_avg_duration'] for stat in model_stats]
# Plot duration ranges with vertical lines and markers
for i, (min_d, max_d) in enumerate(zip(min_durations, max_durations)):
ax3.plot([i, i], [min_d, max_d], 'g-', linewidth=1) # Vertical line
ax3.plot(i, min_d, 'g-', markersize=10) # Min marker
ax3.plot(i, max_d, 'g-', markersize=10) # Max marker
ax3.set_ylabel('Duration (s)', color='green')
ax3.tick_params(axis='y', labelcolor='green')
# Customize x-axis labels with proper rotation
ax1.set_xticks(range(len(models)))
ax1.set_xticklabels(models, rotation=45, ha='right', rotation_mode='anchor')
for i, model in enumerate(models):
# Shorten model names by removing common suffixes
short_name = model.replace(':latest', '').replace('-uncensored', '')
ax1.get_xticklabels()[i].set_text(short_name)
# Updated conditions: success rate > 95% AND success rate / duration >= 5
if success_rates[i] > 95 and (success_rates[i] / durations[i] >= 5):
ax1.get_xticklabels()[i].set_color('green')
# Adjust layout to prevent label cutoff
plt.subplots_adjust(bottom=0.25, left=0.1, right=0.85)
plt.title('Model Performance Comparison')
plt.tight_layout()
# Save the figure before showing it
output_path = 'benchmark_results/model_comparison.png'
plt.savefig(output_path, dpi=300, bbox_inches='tight')
print(f"{INFO}Plot saved as '{output_path}'{ENDC}")
# Show the figure (optional - can be removed for headless environments)
plt.show()
def get_latest_json_file(directory):
"""Find the latest JSON file in the specified directory."""
json_files = glob.glob(os.path.join(directory, '*.json'))
print(f"{INFO}Found JSON files: {json_files}{ENDC}")
latest_file = max(json_files, key=os.path.getmtime) if json_files else None
return latest_file
def main():
parser = argparse.ArgumentParser(description='Run Ollama model benchmarks')
@ -721,8 +930,24 @@ def main():
parser.add_argument('--model', type=str, help='Specific model to benchmark')
parser.add_argument('--number', type=str, help='Number of models to benchmark (number or "all")')
parser.add_argument('--verbose', action='store_true', help='Enable verbose output')
parser.add_argument('--plot-only', action='store_true',
help='Skip benchmarking and just plot graphs from existing results')
parser.add_argument('--no-plot', action='store_true',
help='Run benchmarking without plotting graphs at the end')
parser.add_argument('--file', type=str,
help='Specify a benchmark results file to use for plotting (only with --plot-only)')
args = parser.parse_args()
# Set global verbose flag
global verbose
verbose = args.verbose
# Handle plot-only mode
if args.plot_only:
print(f"{INFO}Running in plot-only mode...{ENDC}")
plot_benchmark_results(args.file)
return
server_url = SERVERS[args.server]
print()
@ -780,7 +1005,12 @@ def main():
# Print and save results
print_leaderboard(all_results)
update_server_results(server_url, all_results)
json_file = update_server_results(server_url, all_results)
# Plot results unless --no-plot is specified
if not args.no_plot:
print(f"{INFO}Generating performance plot...{ENDC}")
plot_benchmark_results(json_file)
if __name__ == "__main__":
main()

12
requirements.txt
View File

@ -1,6 +1,12 @@
# Core dependencies
requests>=2.31.0
together>=0.2.8
ollama>=0.1.6
python-dotenv>=1.0.0
GPUtil==1.4.0
py-cpuinfo
py-cpuinfo>=9.0.0
# Visualization
matplotlib>=3.7.0
# Analysis
together>=0.2.8
GPUtil==1.4.0