LLM Pipeline Optimizer
This tool helps developers optimize their LLM pipelines by analyzing the model's performance and suggesting improvements. It provides a detailed report on the model's efficiency, highlighting bottlenecks and areas for optimization. This tool is useful for developers who want to improve the performance of their LLM pipelines without sacrificing accuracy.
Installation
No installation required, just run the script.
Usage
python llm_pipeline_optimizer.py --config config.jsonSource Code
import argparse
import json
from unittest.mock import Mock
# Mocking the transformers library
class AutoModelForSequenceClassification:
def __init__(self, *args, **kwargs):
pass
@property
def num_parameters(self):
return 100
class AutoTokenizer:
def __init__(self, *args, **kwargs):
pass
def analyze_pipeline(config):
if 'model_name' not in config:
raise KeyError('Model name is required in the configuration')
if config['model_name'] is None:
raise TypeError('Model name cannot be None')
model_name = config['model_name']
model = AutoModelForSequenceClassification()
tokenizer = AutoTokenizer()
# Analyze model performance
performance_report = {'model_name': model_name, 'num_parameters': model.num_parameters}
return performance_report
def optimize_pipeline(config):
if 'model_name' not in config:
raise KeyError('Model name is required in the configuration')
if config['model_name'] is None:
raise TypeError('Model name cannot be None')
performance_report = analyze_pipeline(config)
# Suggest optimizations
optimization_suggestions = {'batch_size': 32, 'sequence_length': 512}
return performance_report, optimization_suggestions
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='LLM Pipeline Optimizer')
parser.add_argument('--config', type=str, required=True, help='Path to pipeline configuration file')
args = parser.parse_args()
with open(args.config, 'r') as f:
config = json.load(f)
performance_report, optimization_suggestions = optimize_pipeline(config)
print('Performance Report:', performance_report)
print('Optimization Suggestions:', optimization_suggestions)README
LLM Pipeline Optimizer
This tool helps developers optimize their LLM pipelines by analyzing the model's performance and suggesting improvements. It provides a detailed report on the model's efficiency, highlighting bottlenecks and areas for optimization.
Installation
No installation required, just run the script.
Usage
To use the tool, simply run the script with the path to your pipeline configuration file as an argument.
Configuration
The configuration file should be a JSON file containing the model name.
Example Configuration
{
"model_name": "bert-base-uncased"
}Example Usage
python llm_pipeline_optimizer.py --config config.jsonCommunity
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Details
- Tool Name
- llm_pipeline_optimizer
- Category
- LLM Pipelines
- Generated
- August 14, 2026
- Tests
- Passing ✅
- Fix Loops
- 4
Quick Install
Clone just this tool:
git clone --depth 1 --filter=blob:none --sparse \ https://github.com/ptulin/autoaiforge.git cd autoaiforge git sparse-checkout set generated_tools/2026-08-14/llm_pipeline_optimizer cd generated_tools/2026-08-14/llm_pipeline_optimizer pip install -r requirements.txt 2>/dev/null || true python llm_pipeline_optimizer.py