Fix Bad OCR: Fine-Tune DeepSeek-V2 on Your Own Data (Unsloth)

🚀 Colab tutorial: Fine-tune DeepSeek-OCR (3B) with Unsloth + LoRA to improve handwriting & document OCR. In a demo we cut CER from 23% to 6% (~74% relative) and show a brief look at a small Persian OCR set. Notebook: https://docs.unsloth.ai/new/deepseek-ocr-run-and-fine-tune What you’ll learn DeepSeek-OCR overview (layout → vision tokens; fast inference) Colab setup (Transformers, PyTorch, Unsloth) Baseline inference + CER evaluation Dataset formatting (image + instruction, user/assistant turns) LoRA/PEFT fine-tuning via FastVisionModel.get_peft_model Training with Trainer, monitoring loss (quick 60-step run) Post-training evaluation (demo sample + Persian examples) Saving/pushing LoRA adapters to Hugging Face Hub Resources Unsloth: https://github.com/unslothai/unsloth DeepSeek-OCR (HF): https://huggingface.co/unsloth/DeepSeek-OCR Persian OCR dataset: https://huggingface.co/datasets/hezARAI/parsynth-ocr-200k Chapters 00:00 Intro — What is DeepSeek-OCR? 01:04 Fine-tuning results (demo & Persian set overview) 02:06 Colab notebook walkthrough 02:18 Install dependencies (Unsloth) 02:32 Load unsloth/DeepSeek-OCR 03:00 Baseline eval (CER on sample) 03:57 Test on a custom screenshot 04:43 Prep for LoRA fine-tuning 05:14 Data prep & formatting 06:22 Train (60 steps) 07:08 Evaluate — 23%→6% CER (demo sample) 07:50 Save LoRA / push to HF Hub 08:10 Outro Note: Results shown include a single-sample demo (23%→6% CER) and a brief, small Persian OCR evaluation. Expect variability on your data/language. 💬 What should I fine-tune next? Comment below. 👍 Like & subscribe if this helped! #DeepSeekOCR #Unsloth #LoRA