AI users debate LoRA training parameters for Qwen 2.1, Llama 2, and MOSS

By PulseAugur Editorial · [1 sources] · 2026-10-24 17:19

A user on Reddit is seeking advice regarding the optimal training parameters for LoRA models, specifically for the Qwen 2.1, Llama 2, and MOSS models. They are unsure whether to use the 'shift' or 'sigmoid' parameter, noting that 'sigmoid balanced' has been suggested for face learning, but their recent LoRA training for MOSS resulted in a broken output. The user is experimenting with different 'shift' variations for Qwen 2.1 and MOSS, questioning if 'shift' might be superior to 'sigmoid' in certain training stages. AI

IMPACT Discussion highlights ongoing challenges and community-driven experimentation in fine-tuning large language models.

RANK_REASON User-generated discussion about model training parameters, not a primary release or research finding.

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

AI users debate LoRA training parameters for Qwen 2.1, Llama 2, and MOSS

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Newsworthiness bucket: Commentary; User-generated discussion about model training parameters, not a primary release or research finding.
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COVERAGE [1]

r/StableDiffusion TIER_2 English(EN) · /u/Inevitable_Pen9043 · 2026-10-24 17:19

It is not clear to me which parameter we should use for LoRA training—shift or sigmoid—for the new models (Qwen 2.1, LLaMA 2, and MOSS) within the AI toolkit.

<div class="md">In the past, I heard some people say that "sigmoid balanced" is better—that it’s the only one capable of learning faces. I recently trained a LoRA for "Moss," and the result turned out completely broken. Sigmoid …

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COVERAGE [1]

It is not clear to me which parameter we should use for LoRA training—shift or sigmoid—for the new models (Qwen 2.1, LLaMA 2, and MOSS) within the AI ​​toolkit.

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It is not clear to me which parameter we should use for LoRA training—shift or sigmoid—for the new models (Qwen 2.1, LLaMA 2, and MOSS) within the AI toolkit.