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User fine-tunes Qwen3-4B model, documents exam performance

By PulseAugur Editorial · [1 sources] ·

A user documented the process of fine-tuning the Qwen3-4B model, detailing a specific instance where the model scored 47 out of 314 on a specialist exam. The fine-tuning process involved a LoRA adapter with 2.9 million weights, completed in 75 minutes on a Colab T4, resulting in a score of 202. AI

IMPACT Demonstrates practical application and performance evaluation of fine-tuning smaller language models.

RANK_REASON User-generated content detailing the fine-tuning of an existing model and its performance on a benchmark. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Mastodon — mastodon.social →

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

User fine-tunes Qwen3-4B model, documents exam performance

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15 / 100
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User-generated content detailing the fine-tuning of an existing model and its performance on a benchmark. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · R4TSQ ·

    Part 1 of 3, How to Train Your Own AI: fine-tuning, one real run start to finish. Qwen3-4B failed my specialist exam, 47 of 314. A LoRA adapter of 2.9 million w

    Part 1 of 3, How to Train Your Own AI: fine-tuning, one real run start to finish. Qwen3-4B failed my specialist exam, 47 of 314. A LoRA adapter of 2.9 million weights (0.07 percent of the model), 480 steps and 75 minutes on a Colab T4 later, it scored 202. https:// youtu.be/TWM7v…

    LINKS youtube.com/watch

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