Tag: #vision-language-model
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 5 posts
Vision and Multimodal: Image Understanding, OCR, and VLMs
Vision models are not one category. Conversational VLMs, dedicated OCR, document structuring, and image retrieval embeddings are different objects and do not substitute for one another. This post organizes the card value
2026-08-12 · 7 min read #ai#huggingface#open-source-llm#vision-language-model#ocrOCR and Document Understanding Technical Reports: What to Read, and Why Parsing Is Not Finished
Ten OCR and document understanding technical reports, each verified by opening the arXiv abstract page directly. From Donut and Nougat through GOT-OCR2.0, olmOCR, DeepSeek-OCR and its successor, GLM-OCR, Qianfan-OCR, Min
2026-08-12 · 7 min read #ai-papers#paper-review#technical-report#ocr#document-aiTraining Vision LLMs — How to Teach Input and Output
A vision-language model is trained in stages, from alignment pretraining to instruction fine-tuning. We organize what gets taught and how, from the angle of the training pipeline: vision encoder freezing strategy, data c
2026-06-26 · 17 min read #mlops#vision-language-model#multimodal#training#instruction-tuningVision LLM Architecture — How an Image Becomes Language
A vision-language model processes an image with a vision encoder, then passes it through a projector to produce tokens an LLM can read. From patch embedding to arbitrary-resolution handling, we trace the full path by whi
2026-06-26 · 20 min read #llm#vision-language-model#multimodal#vit#qwen2-vlBeyond OCR — OCR-free Document Understanding and Unified Models
A traditional OCR pipeline splits into detection, recognition, and layout stages, but errors accumulate. We organize the shift in document AI: Donut-style and VLM-based OCR-free document understanding, high-resolution an
2026-06-26 · 20 min read #ai-papers#ocr-free#document-understanding#multimodal#donut