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from benchmarking import BenchmarkEvaluator
def main():
# Configuration
model_name = "aaditya/Llama3-OpenBioLLM-8B"
FILE_PATH = "_Benchmarking_DB.json"
# Initialize evaluator
evaluator = BenchmarkEvaluator(model_name, model_type)
try:
# Run benchmark
results = evaluator.run_benchmark(FILE_PATH)
# Print results
print("\nBenchmark Results:")
print(f"Accuracy: {results['accuracy']:.2%}")
print(f"Total samples: {results['total_samples']}")
print(f"Processed samples: {results['processed_samples']}")
except Exception as e:
logger.error(f"Error during benchmarking: {e}")
if __name__ == "__main__":
main()
# instruct_client = VLLMClient(
# model_path="meta-llama/Llama-3.2-3B-Instruct",
# model_type=ModelType.INSTRUCT
# )
# # Single prompt with instruct model
# response = instruct_client.send_message(
# system="You are a helpful assistant.",
# content="What is the capital of France?",
# max_tokens=16000,
# temperature=0.0
# )
# print("Instruct model response:", response)
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