Granite Vision 3.2-2B Abliterated
| Property | Value |
|---|---|
| Base Model | IBM Granite Vision 3.2 |
| Parameters | 2 Billion |
| Model Type | Vision-Language Model |
| Author | huihui-ai |
| Hub Link | huggingface.co/huihui-ai/granite-vision-3.2-2b-abliterated |
What is granite-vision-3.2-2b-abliterated?
This is a modified version of IBM's Granite Vision 3.2 model that has been processed using abliteration techniques to remove content refusal behaviors. The model maintains its original vision-language capabilities while providing more unrestricted responses. Importantly, only the text generation component has been modified, leaving the vision processing capabilities intact.
Implementation Details
The model can be implemented using the Hugging Face transformers library or through Ollama. It utilizes the AutoProcessor and AutoModelForVision2Seq architectures for processing both images and text inputs. The model supports chat-template formatting and can generate responses up to 100 tokens.
- Supports both CPU and CUDA execution
- Implements vision-to-sequence architecture
- Compatible with Hugging Face's transformer library
- Available as GGUF format for Ollama integration
Core Capabilities
- Multi-modal understanding of images and text
- Unrestricted text generation compared to base model
- Support for conversation-style interactions
- Efficient processing of visual inputs
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its abliteration processing, which removes content refusal behaviors while preserving the original vision-language capabilities of the IBM Granite Vision model. This makes it more flexible for various applications while maintaining high-quality visual understanding.
Q: What are the recommended use cases?
The model is suitable for applications requiring unrestricted vision-language processing, including image analysis, visual question answering, and multi-modal conversations. It's particularly useful when standard models' content restrictions might limit desired functionality.
