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- Update SFT, VLM-SFT, DPO notebooks to use uv pip - Add emoji section headers to GRPO notebook for consistency - Consolidate GRPO install commands Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add new datasets.mdx page covering SFT, DPO, VLM-SFT, GRPO formats - Include example datasets for each training method - Add dataset sources section (HuggingFace, JSONL, CSV, Parquet) - Add PIL image loading example in accordion Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add Vision Language Model Fine-Tuning (VLM-SFT) section with LoRA example - Replace placeholder datasets with real examples (smoltalk, orpo-dpo-mix, llava-instruct) - Update Colab links to point to GitHub main branch - Add dataset format links to each training section Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Reduce from ~295 lines to ~90 lines - Link to official Unsloth LFM2.5 documentation - Add Unsloth Colab notebooks (SFT, GRPO, CPT) - Keep essential Quick Start code example and tips - Add dataset format link Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Summary
Comprehensive overhaul of the fine-tuning documentation with better structure, real dataset examples, and clearer guidance:
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