Principle:Axolotl ai cloud Axolotl Multimodal Processor Loading
| Knowledge Sources | |
|---|---|
| Domains | Multimodal, Vision_Language, Data_Processing |
| Last Updated | 2026-02-06 23:00 GMT |
Overview
A data processing pattern that loads and configures multimodal processors for handling both image and text inputs in vision-language model training.
Description
Multimodal Processor Loading handles the initialization of processors that can process both visual and textual data for vision-language models. A processor combines an image processor (for resizing, normalizing, and encoding images) with a tokenizer (for encoding text), producing combined inputs suitable for multimodal models.
Different vision-language architectures (LLaVA, Qwen2-VL, Pixtral, Llama Vision) use different processor implementations. This principle abstracts the processor selection and configuration, handling architecture-specific quirks like Mistral's custom tokenizer integration and dynamic image size detection.
Usage
Use multimodal processor loading when:
- Fine-tuning vision-language models (LLaVA, Qwen2-VL, Pixtral, Llama Vision)
- Training on image-text instruction data
- The model requires joint image and text preprocessing
Theoretical Basis
Multimodal Processing Pipeline:
# Abstract multimodal processing
image_features = image_processor(raw_image) # Resize, normalize, encode
text_tokens = tokenizer(text_prompt) # Tokenize text
combined = processor(images=raw_image, text=text_prompt)
# Returns: input_ids, attention_mask, pixel_values
Key components:
- Image Processor: Resizes, normalizes, and converts images to tensors
- Tokenizer: Encodes text and special image tokens
- Processor: Combines both, handles interleaving of image/text tokens