From OCR to open-source VLMs, the practical path is finally clear. High-volume OCR tooling is catching up to multimodal work, with PaddleOCR often beating Tesseract on complex layouts [1]. For bulk labeling, DocLing runs on CPU powered by granite-docling-258M, a handy CPU-first option [1]. Other strong tools include Kraken, nougat, Pylaia, and TrOCR; teams map OCR outputs into multimodal models before fine-tuning [1]. Some folks even use Gemini 2.5 Pro to annotate thousands of documents, then fine-tune open-source VLMs on OCR data [1].
OCR Tooling to Feed Multimodal Models OCR feeding matters: you want robust text and reliable image extraction to power a VLM's vision-language work [1]. PaddleOCR often handles embedded images better, while Kraken-like workflows help with ground-truth labeling when CPU budgets are tight [1].
Multimodal Fine-Tuning: Model Choices & VRAM Fine-tuning multimodal LLMs is resource-hungry. For a 7B model, you’ll probably need multiple 20GB GPUs or a beefy H100 with ~80GB [2]. Practical paths include Qwen models, plus distilled Qwen variants from Unsloth to speed up training [2]. Other useful options are AutoGluon and open ecosystems like unsloth docs for vision-tuning guidance [2]. If you’re short on GPUs, try Colab Pro pipelines and lean into context-engineered prompts with a near-deterministic temperature of 0–0.1 [2].
LLM-JEPA: Large Language Models Meet Joint Embedding Predictive Architectures LLM-JEPA is a JEPA-based solution for finetuning and pretraining that can outperform standard LLM objectives, across models like Llama3, OpenELM, Gemma2, and Olmo [3]. The approach adds two hyperparameters (λ, k) and can double training compute, though the gains justify the cost. The bottleneck today is compute, mitigated by random loss dropout [3].
Closing thought: connect OCR with open-source VLMs using concrete VRAM plans, then experiment with JEPA-style objectives to push accuracy without blowing budget.
References
Any suggestions for Open source OCR tools [D]
Discusses non-LLM OCR tools, pros/cons of LLMs for OCR, models like Granite-Docling, PaddleOCR, Docling; debate on open source VLMs.
View source[D] Advice needed for Fine Tuning Multimodal Language model
Discusses fine-tuning multimodal models for price prediction; recommends models (Qwen, Mistral, BLIP2); GPUs, VRAM, and efficiency tips in Colab contexts.
View sourceLLM-JEPA: Large Language Models Meet Joint Embedding Predictive Architectures
LLM-JEPA presents JEPA-based LLM training; reports gains over standard objectives across Llama3/OpenELM/Gemma2/Olmo; notes hyperparameters and compute.
View source