72 lines
2.5 KiB
Markdown
72 lines
2.5 KiB
Markdown
---
|
||
license: apache-2.0
|
||
language:
|
||
- en
|
||
- zh
|
||
base_model:
|
||
- Qwen/Qwen2.5-VL-7B-Instruct
|
||
tags:
|
||
- Image-to-text
|
||
- text-generation
|
||
- conversational
|
||
- uncensored
|
||
---
|
||
|
||
### Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF(Vision Language)
|
||
|
||
This repository hosts Qwen2.5-VL-Abliterated-Caption-GGUF, a quantized Vision-Language (Uncensored) model optimized for image understanding and caption generation with relaxed alignment constraints. The model is designed for local inference, experimentation, and research-oriented multimodal workflows.
|
||
|
||
It targets users who want direct, descriptive visual reasoning without heavy content moderation layers, packaged in a GGUF format for efficient CPU and edge-device deployment.
|
||
|
||
### Model Summary
|
||
|
||
- **Model Identifier**: Qwen2.5-VL-Abliterated-Caption-GGUF
|
||
- **Base Model**: Qwen2.5-VL (Vision-Language)
|
||
- **Architecture**: Transformer-based multimodal model (text + vision)
|
||
- **Original model**: prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF
|
||
- **Primary Function**: Image captioning and visual-text understanding
|
||
|
||
###Purpose & Design Goals
|
||
|
||
This variant prioritizes expressive visual descriptions and caption accuracy while minimizing restrictive alignment behaviors. The “abliterated” aspect indicates reduced policy-driven refusals, making the model more suitable for:
|
||
- Dataset generation
|
||
- Visual analysis research
|
||
- Creative or descriptive captioning tasks
|
||
- Offline or private multimodal pipelines
|
||
|
||
### Multimodal Interaction Format
|
||
|
||
The model follows a standard multimodal prompt structure compatible with Qwen-VL style templates. A typical interaction may include system context, a user query, and an image reference:
|
||
```
|
||
<|system|>
|
||
You are a visual captioning assistant.
|
||
<|user|>
|
||
Describe the image in detail.
|
||
<|vision_input|>
|
||
<image>
|
||
<|assistant|>
|
||
```
|
||
|
||
### Core Capabilities
|
||
|
||
- Detailed and literal image captioning
|
||
- Multimodal reasoning over visual scenes
|
||
- Object, action, and context recognition
|
||
- Long-form descriptive outputs
|
||
- Reduced refusal behavior compared to safety-aligned VL models
|
||
- Optimized for local inference via GGUF
|
||
|
||
### Recommended Use Cases
|
||
|
||
- **Image caption generation** – datasets, tagging, annotation
|
||
- **Visual analysis** – scene breakdowns, object relationships
|
||
- **Creative workflows** – storytelling from images
|
||
- **Research & evaluation** – alignment and multimodal behavior testing
|
||
- **Offline deployments** – no cloud or API dependency
|
||
|
||
|
||
### Credits & Acknowledgements
|
||
|
||
- Qwen team for the base Qwen2.5-VL architecture
|
||
- GGUF tooling and local inference ecosystem contributors
|
||
- Open-source multimodal research community |