Phi-4-multimodal-instruct-abliterated
| Property | Value |
|---|---|
| Author | huihui-ai |
| Base Model | Microsoft Phi-4-multimodal-instruct |
| Model Type | Multimodal LLM |
| Implementation | PyTorch with Flash Attention 2 |
What is Phi-4-multimodal-instruct-abliterated?
This is a modified version of Microsoft's Phi-4 multimodal model that has been processed using abliteration techniques to remove standard refusal responses while preserving the model's core functionality. The modification specifically targets the text processing component, leaving the image processing capabilities intact.
Implementation Details
The model utilizes PyTorch's bfloat16 precision and implements Flash Attention 2 for improved performance. It can be easily integrated using Hugging Face's transformers library and supports both text and image inputs through a specialized processor.
- Implements custom tokenization with specific prompt formats
- Uses Flash Attention 2 for optimized performance
- Supports bfloat16 precision for efficient processing
- Maintains multimodal capabilities with focus on text and image processing
Core Capabilities
- Text generation and processing without standard refusal responses
- Image processing capabilities (unchanged from base model)
- Support for custom generation configurations
- Flexible input processing with specialized tokenizer
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its ability to process both text and images while removing standard refusal responses through abliteration, making it more versatile for applications where standard content restrictions might be limiting.
Q: What are the recommended use cases?
The model is suitable for applications requiring multimodal processing capabilities, particularly where standard content restrictions need to be bypassed. However, users should exercise appropriate judgment and responsibility in deployment.
