Workflow:Microsoft DeepSpeedExamples VisualChat Multimodal Training
| Knowledge Sources | |
|---|---|
| Domains | LLMs, Multimodal, Computer_Vision, Fine_Tuning, Distributed_Training |
| Last Updated | 2026-02-07 13:00 GMT |
Overview
End-to-end process for training a multimodal vision-language model that supports multi-round, multi-image interleaved conversations, combining a vision encoder with a large language model decoder using DeepSpeed distributed training.
Description
This workflow trains a DeepSpeed VisualChat (DSVL) model that can process interleaved sequences of images and text in multi-round conversations. The model architecture connects a vision encoder (OpenCLIP or QWen-VL) to a language model decoder (LLaMA-2) via learnable projection layers, enabling visual understanding and language generation in a unified framework.
Goal: A multimodal chat model capable of answering questions about images, describing visual content, and engaging in multi-turn conversations involving multiple images.
Scope: Covers vision encoder selection, projection layer configuration, multi-dataset training with blending, LoRA parameter-efficient fine-tuning, and multi-modal causal attention.
Strategy: Uses a three-component architecture (vision encoder + projection layer + LLM decoder) with LoRA for efficient fine-tuning. Supports 10+ multimodal datasets blended during training. DeepSpeed ZeRO handles distributed training across multiple GPUs.
Usage
Execute this workflow when you need to create a vision-language model that can understand and discuss images in multi-turn conversations. This is appropriate when you have access to multimodal datasets (image-text pairs, VQA, dialogue) and want to combine a pretrained vision encoder with a pretrained language model for visual chat capabilities.
Execution Steps
Step 1: Vision Encoder Setup
Select and configure the vision encoder that will process input images into visual feature representations.
Key considerations:
- Choose between OpenCLIP (clip-vit-large-patch14) or QWen-VL vision encoder
- For QWen-VL, extract the visual component from the full model using the helper script
- Configure image preprocessing (resolution, normalization) matching the selected encoder
- The vision encoder produces fixed-size feature vectors for each image
Step 2: Projection Layer Configuration
Configure the learnable projection layers that map vision features into the language model's embedding space.
What happens:
- Select projection type: linear, MLP, or perceiver-based
- The projection layer bridges the dimensionality gap between vision encoder output and LLM input
- For perceiver projection, configure the number of learnable query tokens
- These projection weights are the primary trainable parameters during pretraining stage
Step 3: Language Model Loading
Load the pretrained language model decoder that will generate text responses conditioned on visual and textual inputs.
What happens:
- Load a HuggingFace causal language model (e.g., LLaMA-2-7B or LLaMA-2-70B)
- Configure tokenizer with appropriate special tokens for image placeholders
- Optionally apply LoRA adapters to language model layers for parameter-efficient training
- Set max sequence length (4096 tokens including image token positions)
Step 4: Multi_Dataset Preparation
Prepare and blend multiple multimodal datasets for training with configurable sample counts per dataset.
What happens:
- Select from 10+ supported datasets: LLaVA, COCO Captions, VQA, A-OKVQA, OCR-VQA, MIMIC-IT variants, Sparkles dialogue
- Each dataset provides image-text pairs formatted using the DeepSpeed Template (DST)
- Configure per-dataset sample counts for training blend
- Split data for training and evaluation using configurable ratios
- Create distributed data loaders with proper sampling
Step 5: Distributed Training
Train the multimodal model using DeepSpeed with ZeRO optimization and optional multi-modal causal attention.
What happens:
- Initialize DeepSpeed engine with ZeRO Stage 2 or 3 configuration
- Configure mixed precision training (fp16 or bf16)
- Enable gradient checkpointing for memory efficiency
- Optionally enable Multi-Modal Causal Attention (MMCA) for improved cross-modal attention
- Train with causal language modeling loss on interleaved image-text sequences
- LoRA parameters and projection layers are updated; base model weights can be frozen
- Log training metrics via TensorBoard
Step 6: Model Fusion and Export
Merge LoRA adapters with base model weights and export the final multimodal model.
What happens:
- Merge trained LoRA adapter weights back into the base language model
- Save the complete model (vision encoder + projection + language model) as a checkpoint
- The exported model can be used with the interactive chat interface for inference
- Support checkpoint resume for continued training