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**Fine-tuning Qwen on Crypto Data: Benchmarking and Computational Optimization**
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## 1. Introduction
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This report presents a novel approach to fine-tuning the Qwen model using crypto-related data to enhance performance in financial and blockchain-based tasks. The method achieves state-of-the-art (SOTA) results on Hugging Face benchmarks while reducing computational resource requirements through an optimized training approach.
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## 1. Introduction
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This report presents a novel approach to fine-tuning the Qwen model using crypto-related data to enhance performance in financial and blockchain-based tasks. The method achieves state-of-the-art (SOTA) results on Hugging Face benchmarks while reducing computational resource requirements through an optimized training approach.
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