Pixtral-12B-2409-unsloth-bnb-4bit
| Property | Value |
|---|---|
| Model Size | 12B parameters |
| Quantization | 4-bit dynamic quantization |
| Hugging Face | Model Repository |
| Author | Unsloth |
What is Pixtral-12B-2409-unsloth-bnb-4bit?
This is an optimized version of the Pixtral-12B model using Unsloth's Dynamic 4-bit Quantization technique. The model maintains the powerful multimodal capabilities of the original Pixtral while significantly reducing memory requirements through selective parameter quantization. It can process both text and images in a single conversation flow, making it ideal for multimodal applications.
Implementation Details
The model implements several technical innovations including GELU for vision adapter and 2D ROPE for vision encoder. It uses mistral_common for handling multimodal inputs and supports various input formats including direct images, image URLs, and base64 encoded images.
- Selective parameter quantization for optimal accuracy-memory trade-off
- Integrated vision processing capabilities
- Compatible with standard Hugging Face pipelines
- Supports multiple image input formats
Core Capabilities
- Multimodal understanding (text + images)
- Efficient memory usage through 4-bit quantization
- Flexible image input handling (URL, base64, direct)
- Enhanced accuracy compared to standard 4-bit quantization
- Compatible with popular fine-tuning frameworks
Frequently Asked Questions
Q: What makes this model unique?
The model's dynamic quantization approach selectively preserves critical parameters at higher precision while quantizing others to 4-bit, achieving better performance than standard quantization methods while maintaining low memory usage.
Q: What are the recommended use cases?
The model is ideal for applications requiring multimodal understanding, such as visual question answering, image description, and content analysis where both text and image processing is needed. It's particularly suitable for deployment scenarios with limited GPU memory.
