DeepCaption-VLA-7B-AIO-GGUF

DeepCaption-VLA-7B-AIO-GGUF

DeepCaption-VLA-7B from prithivMLmods is a 7B-parameter vision-language model fine-tuned from Qwen2.5-VL-7B-Instruct, specialized for image captioning and Vision Language Attribution (VLA) that generates precise, attribute-rich descriptions of visual properties, object attributes, scene details, colors, environments, moods, and actions across general, artistic, technical, abstract, or low-context images in diverse aspect ratios (wide, tall, square, irregular). Trained on curated datasets like blip3o-caption-mini-arrow, Caption3o-Opt-v3/v2, Caltech101 attributes, and private domain-specific sources to emphasize object-attribute alignment and descriptive fluency, it supports variational detail control—from concise summaries to fine-grained attributions—via structured outputs including captions, comma-separated attributes, and {class_name==core_theme} syntax, primarily in English with multilingual prompt adaptability. Ideal for research/dataset creation, object detection annotation, scene understanding, and creative applications using Transformers/Qwen2VLForConditionalGeneration inference, it handles non-standard visuals robustly but may over-attribute ambiguous content or vary by prompt phrasing.

DeepCaption-VLA-7B [GGUF]

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):