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QLoRA fine-tuning boosts Qwen2.5 model for JSON extraction

By PulseAugur Editorial · [1 sources] ·

A developer fine-tuned the Qwen2.5-1.5B-Instruct model using QLoRA to extract structured JSON data from unstructured text. The fine-tuning process significantly improved performance, with field-level accuracy rising from 54% to 97% and exact match accuracy reaching 62%. This demonstrates the effectiveness of QLoRA for domain-specific JSON extraction tasks. AI

IMPACT Demonstrates a practical method for improving LLM performance on specialized data extraction tasks.

RANK_REASON The item details a fine-tuning process for a specific LLM to improve its performance on a particular task (JSON extraction), including evaluation metrics and results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

QLoRA fine-tuning boosts Qwen2.5 model for JSON extraction

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The item details a fine-tuning process for a specific LLM to improve its performance on a particular task (JSON extraction), including evaluation metrics and results. [lever_c_demoted from research…
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Jebra ·

    Fine-Tuning with QLoRA for JSON Extraction

    <p>We needed to extract structured JSON from unstructured text for a domain-specific use case. We decided to train the Qwen2.5-1.5B-Instruct model with quantization levels of 4-bit, 8-bit and 16-bit separately.</p> <p><strong>Dataset</strong><br /> Total samples: ~280</p> <p><str…

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