Create and Optimize a Compressed AI Model for Multi-modal File Management (Teacher-Student Distillation)
Budget: $250 – $750 USD
Project Description:
I am building an AI-powered file management system that can process text documents, images, PDFs, and spreadsheets using OCR, multi-modal image understanding. To ensure the system is efficient and scalable, we need to distill and compress both LLaMA 3.2 90B (for text tasks) and LLava (for image processing and OCR) into a single, smaller, efficient model using the teacher-student distillation method.
Project Goal:
Combine LLaMA 3.2 90B (for text generation and reasoning) and LLava (for vision and OCR tasks) into a single compressed model that can handle both text-based and image-based tasks.
Apply knowledge distillation using a teacher-student method to create a single, resource-efficient model that retains the performance of the larger models but requires less VRAM and compute power.
Detailed Steps for the Project:
##Step 1: Prepare the Teacher Models
**LLaMA 3.2 90B (Teacher for Text Tasks)
Pre-trained Model: Start with the LLaMA 3.2 90B model, which excels at text-based tasks such as generation, summarization, and reasoning.
Optimize the model: Apply 4-bit or 8-bit quantization to reduce VRAM and improve inference time.
Fine-tuning (optional): Fine-tune LLaMA for your specific tasks, like document summarization or Q&A, if necessary.
**LLava (Teacher for Vision Tasks)
Pre-trained Model: Use LLava, a multi-modal vision model, for handling tasks like OCR, image understanding, and visual reasoning.
Optimize the model: Apply 8-bit quantization for efficiency.
Fine-tuning (optional): Fine-tune LLava for your specific image-based tasks (e.g., text extraction from images, chart recognition).
##Step 2: Distillation Setup (Teacher-Student Method)
Choose the Student Model
The student model should be a smaller version of LLaMA (e.g., LLaMA-13B, LLaMA-7B) and LLava (e.g., TinyLLava).
The student model will learn to mimic the performance of both LLaMA and LLava while being much smaller and more efficient.
Distillation Process (Teacher-Student)
Text Distillation:
Train the student model (LLaMA-13B or LLaVA student) to mimic the output of the teacher model (LLaMA 90B) on text generation, reasoning, and query answering tasks.
Use teacher soft targets (i.e., logits from the teacher model) to guide the student model's learning process.
Vision Distillation:
Train the student model to replicate LLava’s outputs on vision tasks like OCR and image-based reasoning (image captioning, text extraction).
Use vision-specific distillation methods (e.g., mimicking the intermediate vision layers from LLava).
Training the Student Model
Use a combination of cross-entropy loss and KL-divergence loss to distill knowledge from both teachers (LLaMA and LLava) into the student model.
You can use LoRA (Low-Rank Adapters) to further fine-tune the student model for better performance and lower memory requirements.
##Step 3: Compress the Distilled Model
Quantization (4-bit / 8-bit)
After distillation, quantize the student model to reduce its memory footprint and speed up inference.
Use libraries like bitsandbytes or AutoGPTQ to apply 4-bit or 8-bit quantization.
Pruning
Apply model pruning to remove unused neurons/weights in the student model, further reducing its size and improving inference speed without sacrificing too much performance.
Knowledge Transfer (for multi-modal tasks)
Ensure that both text generation (LLaMA) and OCR/image reasoning (LLava) are equally well-represented in the final student model.
Fine-tune using a combination of text and image data to optimize the model for both domains.
Step 4: Testing & Evaluation (Performance Benchmarking)
Evaluate the performance of the distilled model on both text and image tasks.
Ensure that it matches or nearly matches the performance of the original teacher models (LLaMA 90B and LLava) on your specific use cases (OCR, text summarization, image reasoning).
Scalability Testing
Test the model on different hardware setups to ensure it can run efficiently on lesser GPUs (e.g., 3090, or even 16GB VRAM cards).
Requirements:
Proven experience with LLaMA models, knowledge distillation, and multi-modal AI models like LLava.
Familiarity with model quantization, pruning, and compression techniques (e.g., bitsandbytes, AutoGPTQ).
Strong understanding of OCR, vision models, and RAG systems.
Experience deploying multi-modal AI models in a production environment with low hardware requirements.
? Timeline: ASAP (Looking for an efficient and cost-effective solution).
I am building an AI-powered file management system that can process text documents, images, PDFs, and spreadsheets using OCR, multi-modal image understanding. To ensure the system is efficient and scalable, we need to distill and compress both LLaMA 3.2 90B (for text tasks) and LLava (for image processing and OCR) into a single, smaller, efficient model using the teacher-student distillation method.
Project Goal:
Combine LLaMA 3.2 90B (for text generation and reasoning) and LLava (for vision and OCR tasks) into a single compressed model that can handle both text-based and image-based tasks.
Apply knowledge distillation using a teacher-student method to create a single, resource-efficient model that retains the performance of the larger models but requires less VRAM and compute power.
Detailed Steps for the Project:
##Step 1: Prepare the Teacher Models
**LLaMA 3.2 90B (Teacher for Text Tasks)
Pre-trained Model: Start with the LLaMA 3.2 90B model, which excels at text-based tasks such as generation, summarization, and reasoning.
Optimize the model: Apply 4-bit or 8-bit quantization to reduce VRAM and improve inference time.
Fine-tuning (optional): Fine-tune LLaMA for your specific tasks, like document summarization or Q&A, if necessary.
**LLava (Teacher for Vision Tasks)
Pre-trained Model: Use LLava, a multi-modal vision model, for handling tasks like OCR, image understanding, and visual reasoning.
Optimize the model: Apply 8-bit quantization for efficiency.
Fine-tuning (optional): Fine-tune LLava for your specific image-based tasks (e.g., text extraction from images, chart recognition).
##Step 2: Distillation Setup (Teacher-Student Method)
Choose the Student Model
The student model should be a smaller version of LLaMA (e.g., LLaMA-13B, LLaMA-7B) and LLava (e.g., TinyLLava).
The student model will learn to mimic the performance of both LLaMA and LLava while being much smaller and more efficient.
Distillation Process (Teacher-Student)
Text Distillation:
Train the student model (LLaMA-13B or LLaVA student) to mimic the output of the teacher model (LLaMA 90B) on text generation, reasoning, and query answering tasks.
Use teacher soft targets (i.e., logits from the teacher model) to guide the student model's learning process.
Vision Distillation:
Train the student model to replicate LLava’s outputs on vision tasks like OCR and image-based reasoning (image captioning, text extraction).
Use vision-specific distillation methods (e.g., mimicking the intermediate vision layers from LLava).
Training the Student Model
Use a combination of cross-entropy loss and KL-divergence loss to distill knowledge from both teachers (LLaMA and LLava) into the student model.
You can use LoRA (Low-Rank Adapters) to further fine-tune the student model for better performance and lower memory requirements.
##Step 3: Compress the Distilled Model
Quantization (4-bit / 8-bit)
After distillation, quantize the student model to reduce its memory footprint and speed up inference.
Use libraries like bitsandbytes or AutoGPTQ to apply 4-bit or 8-bit quantization.
Pruning
Apply model pruning to remove unused neurons/weights in the student model, further reducing its size and improving inference speed without sacrificing too much performance.
Knowledge Transfer (for multi-modal tasks)
Ensure that both text generation (LLaMA) and OCR/image reasoning (LLava) are equally well-represented in the final student model.
Fine-tune using a combination of text and image data to optimize the model for both domains.
Step 4: Testing & Evaluation (Performance Benchmarking)
Evaluate the performance of the distilled model on both text and image tasks.
Ensure that it matches or nearly matches the performance of the original teacher models (LLaMA 90B and LLava) on your specific use cases (OCR, text summarization, image reasoning).
Scalability Testing
Test the model on different hardware setups to ensure it can run efficiently on lesser GPUs (e.g., 3090, or even 16GB VRAM cards).
Requirements:
Proven experience with LLaMA models, knowledge distillation, and multi-modal AI models like LLava.
Familiarity with model quantization, pruning, and compression techniques (e.g., bitsandbytes, AutoGPTQ).
Strong understanding of OCR, vision models, and RAG systems.
Experience deploying multi-modal AI models in a production environment with low hardware requirements.
? Timeline: ASAP (Looking for an efficient and cost-effective solution).