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Small LLM Qwen2.5 3B Instruct repeats itself when generating large JSON datasets

By PulseAugur Editorial · [1 sources] ·

A small language model, Qwen2.5 3B Instruct, running on an Apple M1 chip, exhibits repetitive behavior when asked to generate large JSON datasets. When prompted to create around 40 rows or more, the model begins to copy its own previous outputs, creating loops of identical sentences. This issue persists even with a temperature setting of 0.8, as the model assigns near-certain probabilities to tokens within repeated sentences, preventing it from deviating and generating new content. The problem is exacerbated when using Japanese prompts, leading to even more severe repetition. AI

IMPACT Highlights limitations in current small LLMs for synthetic data generation, impacting training data pipelines.

RANK_REASON Analysis of a specific LLM's behavior and limitations on a particular hardware setup. [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 →

Small LLM Qwen2.5 3B Instruct repeats itself when generating large JSON datasets

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40 / 100
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Analysis of a specific LLM's behavior and limitations on a particular hardware setup. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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model release
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High
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Breaking (< 6h)
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COVERAGE [1]

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

    Why does a small local LLM repeat itself when you ask it for 100 rows of JSON?

    <p>Asking a language model for synthetic training data usually means one prompt: "Generate N rows as a JSON array", a schema, a few example rows. With a 3B model running on a laptop, that works at 20 rows and starts to fall apart at 40, in a very specific way: the model starts co…

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