Qwen3-235B outperforms Inkling as base for fine-tuned models

A Reddit discussion on the r/LocalLLaMA subreddit explores the effectiveness of fine-tuning large language models, specifically questioning whether the base model's architecture is as crucial as its fine-tuning behavior. The conversation highlights that while the Tinker fine-tuning method can improve open-source models, the best-performing public result used Qwen3-235B as its base, not the Inkling model. Evidence suggests Inkling leads in instruction following but lags behind other models like GLM 5.2 and DeepSeek V4 Pro on other benchmarks, and its suitability as a base for fine-tuning remains unproven publicly. AI

IMPACT Highlights the ongoing debate about the importance of base models versus fine-tuning techniques in achieving specialized AI performance.

RANK_REASON Discussion on a subreddit about model performance and fine-tuning, not a primary release or research paper.

Read on r/LocalLLaMA →

• DeepSeek V4 Pro
• GLM 5.2
• GPT-5.6 Sol
• IFBench
• Inkling
• Nemotron 3 Ultra
• Qwen3-235B
• Thinking machines
• Tinker

• model release
• product

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