Task:Implement LLM integration and RAG system

• [ ] 4. Implement LLM integration and RAG system
• [ ] 4.1 Create LLM model serving infrastructure
  • Implement model loading and inference using TensorRT/vLLM
  • Create GPU resource management and auto-scaling logic
  • Write model versioning and deployment automation
  • Implement LoRA adapter loading for domain-specific fine-tuning
  • Requirements: 1.1, 1.5, 7.2, 7.5

Here is a clear and detailed summary of the completed Task 4.1 for the LLM Model Serving Infrastructure, outlining its key components, features, and API endpoints:

✅ Task 4.1 Complete: LLM Model Serving Infrastructure

Core Components Created

• Model Manager (model_manager.py)

  • Supports multiple backends such as HuggingFace Transformers, vLLM, and TensorRT.
  • Includes semiconductor-specific model configurations for Llama 2, Mistral, Qwen, and CodeLlama.
  • Advanced optimizations including quantization, flash attention, and transformer improvements.
  • Supports LoRA/QLoRA fine-tuning for domain-specific customization.
  • Efficient GPU memory management and multi-GPU scaling.
  • Async inference with real-time streaming support.

• LLM Service (llm_service.py)

  • FastAPI-based REST API managing the model lifecycle.
  • Supports text generation, chat completions, and semiconductor-specific analysis endpoints.
  • Provides endpoints for fine-tuning and LoRA adapter creation.
  • Implements authentication and rate limiting for security.
  • Real-time streaming responses enabled via server-sent events (SSE).

• Configuration (llm_config.yaml)

  • Comprehensive model serving configuration including semiconductor domain settings.
  • Performance tuning parameters and security monitoring configurations.
  • Allows environment-specific overrides.

• Infrastructure (docker-compose.yml)

  • GPU-enabled containerized deployment.
  • MLflow model registry integration and Jupyter notebooks for development.
  • TensorBoard for training monitoring.
  • NVIDIA Triton inference server support for optimized deployments.
  • Prometheus and Grafana for monitoring.

• Testing (test_model_manager.py)

  • Extensive unit tests verifying all components.
  • Mock-based testing of model operations.
  • Async testing for inference workflows.
  • Robust error handling validation.

Here is a comprehensive and organized file mapping summary for Task 4.1 LLM Model Serving Infrastructure, detailing the core components, REST API service, configuration, dependencies, container setup, tests, and documentation:

📋 Task 4.1: LLM Model Serving Infrastructure - File Mapping & Content

Key Features Implemented

• Multi-model support: Llama 2/3, Mistral 7B, Qwen, CodeLlama with 7B+ parameter sizes.
• High-performance inference leveraging vLLM, TensorRT, and 4-bit/8-bit quantization techniques.
• Fine-tuning with LoRA/QLoRA adapters facilitating semiconductor-specific domain customization.
• Domain knowledge integration for semiconductor manufacturing including SEMI and JEDEC standards, process expertise, defect analysis, yield optimization, and troubleshooting.
• Full REST API for comprehensive model management, text generation, chat completion, and semiconductor-specific analytics endpoints.
• Streaming support with Server-Sent Events (SSE) for real-time response delivery.
• Advanced GPU resource management with multi-GPU scaling and auto-scaling capabilities.
• Robust monitoring via Prometheus with detailed metrics and health checks.
• Containerized deployment ensuring reproducibility and scalable operation on GPU-enabled infrastructure.
• Rigorous unit tests ensuring reliability and code coverage.

API Endpoints Summary

Requirements Satisfied