72 lines
2.5 KiB
Markdown
72 lines
2.5 KiB
Markdown
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---
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license: apache-2.0
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language:
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- en
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- zh
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base_model:
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- Qwen/Qwen2.5-VL-7B-Instruct
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tags:
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- Image-to-text
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- text-generation
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- conversational
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- uncensored
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---
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### Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF(Vision Language)
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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.
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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.
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### Model Summary
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- **Model Identifier**: Qwen2.5-VL-Abliterated-Caption-GGUF
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- **Base Model**: Qwen2.5-VL (Vision-Language)
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- **Architecture**: Transformer-based multimodal model (text + vision)
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- **Original model**: prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF
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- **Primary Function**: Image captioning and visual-text understanding
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###Purpose & Design Goals
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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:
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- Dataset generation
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- Visual analysis research
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- Creative or descriptive captioning tasks
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- Offline or private multimodal pipelines
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### Multimodal Interaction Format
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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:
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```
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<|system|>
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You are a visual captioning assistant.
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<|user|>
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Describe the image in detail.
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<|vision_input|>
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<image>
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<|assistant|>
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```
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### Core Capabilities
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- Detailed and literal image captioning
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- Multimodal reasoning over visual scenes
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- Object, action, and context recognition
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- Long-form descriptive outputs
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- Reduced refusal behavior compared to safety-aligned VL models
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- Optimized for local inference via GGUF
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### Recommended Use Cases
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- **Image caption generation** – datasets, tagging, annotation
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- **Visual analysis** – scene breakdowns, object relationships
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- **Creative workflows** – storytelling from images
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- **Research & evaluation** – alignment and multimodal behavior testing
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- **Offline deployments** – no cloud or API dependency
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### Credits & Acknowledgements
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- Qwen team for the base Qwen2.5-VL architecture
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- GGUF tooling and local inference ecosystem contributors
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- Open-source multimodal research community
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