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Qwen3.5 Fine-Tuning Guide – Unsloth Documentation
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Overview
Qwen3.5 can be fine-tuned locally with Unsloth, supporting both vision and text fine-tuning for model sizes ranging from 0.8B to 122B. Unsloth enables Qwen3.5 to train 1.5× faster and use 50% less VRAM compared to FA2 setups, with specific VRAM requirements for bf16 LoRA across different model sizes.
Summary from unsloth.ai.
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Hacker News · unsloth.ai
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Based on 5 sampled comments from this thread. Analyzed 2026-03-04.
- Praise: Fine-tuned Qwen models demonstrate impressive performance on NVIDIA Jetson hardware, making them suitable for edge AI tasks where low latency is crucial.
- Praise: LoRA fine-tuning allows models to maintain small sizes while achieving production-grade inference speeds, enhancing their applicability in resource-constrained environments.
- Praise: The power efficiency of running Qwen models on Jetson Orin is notable, as it significantly reduces energy consumption compared to cloud-based solutions.
- Concern: The relevance of fine-tuning modern LLMs is questioned, as their few-shot learning capabilities often render it unnecessary for many applications.
- Concern: Concerns arise about the shift in leadership towards more business-oriented individuals, potentially jeopardizing the open-source ethos of the Qwen project.