empirischtech/DeepSeek-R1-Distill-Llama-70B-gptq-4bit

Overview

This document presents the evaluation results of DeepSeek-R1-Distill-Llama-70B, a 4-bit quantized model using GPTQ, evaluated with the Language Model Evaluation Harness on the ARC-Challenge benchmark.

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📊 Evaluation Summary

Metric	Value	Description	8bit
Accuracy (acc,none)	21.2%	Raw accuracy - percentage of correct answers.	21.2%
Standard Error (acc_stderr,none)	1.19%	Uncertainty in the accuracy estimate.	1.2%
Normalized Accuracy (acc_norm,none)	25.4%	Accuracy after dataset-specific normalization.	25.2%
Standard Error (acc_norm_stderr,none)	1.27%	Uncertainty for normalized accuracy.	1.3%

📌 Interpretation:

• The model correctly answered 21.2% of the questions.
• After normalization, the accuracy slightly improves to 25.4%.
• The standard error (~1.27%) indicates a small margin of uncertainty.

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⚙️ Model Configuration

• Model: DeepSeek-R1-Distill-Llama-70B
• Parameters: 70 billion
• Quantization: 4-bit GPTQ
• Source: Hugging Face (hf)
• Precision: torch.float16
• Hardware: NVIDIA A100 80GB PCIe
• CUDA Version: 12.4
• PyTorch Version: 2.6.0+cu124
• Batch Size: 1
• Evaluation Time: 365.89 seconds (~6 minutes)

📌 Interpretation:

• The evaluation was performed on a high-performance GPU (A100 80GB).
• The model is significantly larger than the previous 8B version, with GPTQ 4-bit quantization reducing memory footprint.
• A single-sample batch size was used, which might slow evaluation speed.

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📂 Dataset Information

• Dataset: AI2 ARC-Challenge
• Task Type: Multiple Choice
• Number of Samples Evaluated: 1,172
• Few-shot Examples Used: 0 (Zero-shot setting)

📌 Interpretation:

• This benchmark assesses grade-school-level scientific reasoning.
• Since no few-shot examples were provided, the model was evaluated in a pure zero-shot setting.

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📈 Performance Insights

• The "higher_is_better" flag confirms that higher accuracy is preferred.
• The model's raw accuracy (21.2%) is significantly lower compared to state-of-the-art models (60–80% on ARC-Challenge).
• Quantization Impact: The 4-bit GPTQ quantization reduces memory usage but may also impact accuracy slightly.
• Zero-shot Limitation: Performance could improve with few-shot prompting (providing examples before testing).

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📊 Detailed Evaluation on MMLU Challenges

Metric	Value	Description
MMLU	37.88%	Averaged over MMLU-Stem, MMLU-Social-Sciences, MMLU-Humanities, MMLU-ther
MMLU-Humanities	31.83%	Averaged over MMLU-Formal-Logic, MMLU-Prehistory, MMLU-World-Religions, MMLU-Philosophy, MMLU-High-School-World-History, MMLU-Professional-Law, MMLU-High-School-US-History, MMLU-Logical-Fallacies, MMLU-International-Law, MMLU-High-School-European-History, MMLU-Moral-Disputes, MMLU-Moral-Scenarios, MMLU-Jurisprudence
MMLU-Social-Sciences	45.43%	Averaged over MMLU-Public-Relations, MMLU-Sociology, MMLU-Security-Studies, MMLU-High-School-Government-and-Politics, MMLU-High-School-Psychology, MMLU-Human-Sexuality, MMLU-US-Foreign-Policy, MMLU-High-School-Microeconomics, MMLU-Econometrics, MMLU-High-School-Macroeconomics, MMLU-High-School-Geography, MMLU-Professional-Psychology
MMLU-Stem	33.01%	Averaged over MMLU-Conceptual-Physics, MMLU-High-School-Chemistry, MMLU-College-Biology, MMLU-College-Chemistry, MMLU-Machine-Learning, MMLU-Elementary-Mathematics, MMLU-Abstract-Algebra, MMLU-Astronomy, MMLU-High-School-Statistics, MMLU-Anatomy, MMLU-College-Mathematics, MMLU-Computer-Security, MMLU-College-Computer-Science, MMLU-Electrical-Engineering, MMLU-College-Physics, MMLU-High-School-Computer-Science, MMLU-High-School-Physics, MMLU-High-School-Biology, MMLU-High-School-Mathematics
MMLU-Other	44.48%	Averaged over MMLU-Medical-Genetics, MMLU-Global-Facts, MMLU-Marketing, MMLU-College-Medicine, MMLU-Human-Aging, MMLU-Virology, MMLU-Business-Ethics, MMLU-Clinical-Knowledge, MMLU-Professional-Medicine, MMLU-Nutrition, MMLU-Miscellaneous, MMLU-Professional-Accounting, MMLU-Management

📌 Let us know if you need further analysis or model tuning! 🚀