DeepCaption-VLA-V2.0-7B-AIO-GGUF

DeepCaption-VLA-V2.0-7B-AIO-GGUF

DeepCaption-VLA-V2.0-7B from prithivMLmods is an advanced 7B-parameter vision-language model fine-tuned from Qwen2.5-VL-7B-Instruct, enhancing the original DeepCaption-VLA with significantly improved multilingual inference for precise image captioning and Vision Language Attribution (VLA) across English, Chinese (Zh), Thai (Th), and other languages, generating attribute-rich descriptions of visual properties, object attributes, scene details, colors, environments, moods, and actions in diverse formats (wide, tall, square, irregular aspect ratios) for general, artistic, technical, abstract, or low-context images. V2.0 delivers higher captioning quality and attribution accuracy through refined training on curated datasets emphasizing semantic precision and descriptive fluency, supporting variational detail control—from concise summaries to fine-grained outputs—via structured formats like Caption:<description>, Attributes:<list>, and {class_name==core_theme}, making it ideal for research, dataset annotation, object detection, scene understanding, and multilingual creative applications using Transformers/Qwen2VLForConditionalGeneration. While robust across non-standard visuals, it may over-attribute ambiguous content, vary by prompt phrasing, or degrade slightly on highly synthetic domains, positioning it as an experimental upstream VLM for advanced vision-language tasks.

DeepCaption-VLA-V2.0-7B [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):