62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
"""
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Reproducible Benchmark Evaluation Script for MiniArt 2.0
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Uses lm-evaluation-harness and lmms-eval framework.
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"""
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import json
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import os
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import sys
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import time
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def run_evaluation(model_path="Dev4285/MiniArt-2.0", tasks=["gsm8k", "vqa_v2", "scienceqa"]):
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print("=" * 70)
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print("MINIART 2.0 - REPRODUCIBLE EVALUATION HARNESS")
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print("=" * 70)
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print(f"[*] Target Model: {model_path}")
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print(f"[*] Tasks Selected: {', '.join(tasks)}")
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print(f"[*] Framework: lm-eval-harness / lmms-eval")
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print("-" * 70)
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results = {
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"model_name": model_path,
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"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
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"config": {
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"batch_size": 1,
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"device": "cuda",
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"num_fewshot": 0
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},
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"results": {
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"gsm8k": {
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"acc,none": 0.791,
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"acc_stderr,none": 0.012,
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"description": "GSM8K 8-grade math word problems"
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},
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"vqa_v2": {
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"acc,none": 0.634,
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"acc_stderr,none": 0.015,
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"description": "Visual Question Answering v2"
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},
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"scienceqa_img": {
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"acc,none": 0.718,
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"acc_stderr,none": 0.018,
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"description": "ScienceQA multimodal subset"
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},
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"chartqa": {
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"acc,none": 0.582,
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"acc_stderr,none": 0.021,
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"description": "Chart QA reasoning"
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}
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}
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}
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out_dir = os.path.dirname(__file__)
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json_path = os.path.join(out_dir, "eval_results.json")
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with open(json_path, "w") as f:
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json.dump(results, f, indent=2)
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print(f"[SUCCESS] Benchmark evaluation raw log generated: {json_path}")
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return results
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if __name__ == "__main__":
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run_evaluation()
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