inference-deploy

What problem does it solve?

This Skill streamlines the process of deploying trained machine learning models for inference, making them accessible for real-time predictions and applications.

Core Features & Use Cases

• Multiple Serving Frameworks: Supports vLLM, TGI, Ollama, and llama.cpp for flexible deployment.
• Quantization: Enables model optimization through various quantization techniques (GGUF, GPTQ, AWQ).
• API Endpoint Setup: Configures OpenAI-compatible API endpoints for easy integration.
• Containerization: Generates Docker configurations for reproducible deployments.
• Performance Validation: Includes load testing to benchmark inference speed and throughput.
• Use Case: Deploy a fine-tuned LLM for a customer support chatbot using vLLM for high throughput and an OpenAI-compatible API.

Quick Start

Use the inference-deploy skill to deploy the model located at '/models/my-llm' using vLLM.