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ML beginner seeks advice on 3B vs 7B model for multi-task reasoning fine-tuning

A self-taught individual is seeking advice on fine-tuning a language model for a complex multi-task reasoning project. The user needs to determine if a 3 billion or 7 billion parameter model, such as Phi-4-mini or Qwen 2.5, would be more suitable for tasks involving identifying underlying questions, holding multiple perspectives, and discerning critical information from noise. They have a dataset of 40-60k examples and are concerned about potential confusion between related reasoning modes and the difficulty of training such tasks. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Guidance for fine-tuning smaller models on complex reasoning tasks.

RANK_REASON User is asking for advice on fine-tuning a model for a specific research task.

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COVERAGE [1]

  1. r/MachineLearning TIER_1 · /u/retarded_770 ·

    First time fine-tuning, need a sanity check — 3B or 7B for multi-task reasoning? [D]

    <!-- SC_OFF --><div class="md"><p>Ok so this is my first post here, been lurking for a while. I’m about to start my first fine-tuning project and I don’t want to commit to the wrong direction so figured I’d ask.</p> <p>Background on me: I’m not from an ML background, self-taught,…