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GGUF Quantization for Efficient LLM Inference

gguf-quantization

Orchestra-Research/AI-Research-SKILLs

This guide details the use of the GGUF format and quantization techniques for running large language models (LLMs) efficiently. GGUF enables optimal inference on consumer hardware, Apple Silicon, and systems requiring CPU-only operation. By compressing models using various K-quant methods (like Q4_K_M), developers can significantly reduce memory footprint and hardware requirements while maintaining high performance.

GPTQ Quantization Guide

Orchestra-Research/AI-Research-SKILLs

GPTQ enables post-training 4-bit quantization for large LLMs, delivering up to 4× memory reduction and 3-4× faster inference with under 2% perplexity loss on consumer GPUs through AutoGPTQ, PEFT, and Transformer integrations.

Groq CI/CD Integration Guide

groq-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

Configure robust CI/CD pipelines for Groq API integrations using GitHub Actions. This guide covers setting up unit tests (mocked), live integration tests, and automated model deprecation checks. It ensures that your LLM-powered application remains stable and compatible as models evolve.

Groq Chat, Tools, and Structured Output

groq-core-workflow-a

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to implementing advanced LLM patterns using the Groq API. This workflow demonstrates how to handle chat completions, robust tool/function calling for integrating external services (like weather or search), and enforcing structured data output using JSON mode and strict schemas. Ideal for building real-time, production-grade AI features.

Implementing Enterprise RBAC for Groq API

groq-enterprise-rbac

jeremylongshore/claude-code-plugins-plus-skills

This guide details how to implement robust, multi-layered Role-Based Access Control (RBAC) for managing LLM API calls to Groq in an enterprise setting. It covers best practices for API key scoping, application-level authorization (model selection, token limits), rate limiting, and detailed spending control mechanisms, ensuring secure and cost-effective usage across multiple teams.

Setup And Authenticate Groq SDK Client

groq-install-auth

jeremylongshore/claude-code-plugins-plus-skills

This guide provides comprehensive instructions for installing and configuring the official Groq SDK in both Python and TypeScript environments. It covers dependency setup, secure API key management using environment variables, and provides code examples to verify a successful connection to Groq's ultra-fast LLM inference API. Essential for initiating any project powered by Groq.

Groq Local Development Workflow Setup

groq-local-dev-loop

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to setting up a fast, reproducible local development workflow for Groq applications. This setup includes structured project scaffolding, implementing singleton clients, defining model constants, and providing isolated unit tests with mocking, alongside live integration tests. Ideal for developers building low-latency LLM applications and ensuring rapid iteration cycles.

Groq LLM Migration Deep Dive

groq-migration-deep-dive

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide for migrating Large Language Model (LLM) integrations from providers like OpenAI or Anthropic to Groq. It details technical steps including SDK replacement, model ID mapping, building provider abstraction layers, and implementing zero-downtime traffic shifting using feature flags. This ensures developers can leverage Groq's superior speed while maintaining service continuity.

Multi-Environment Groq Setup Guide

groq-multi-env-setup

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to configuring and managing Groq API access across development, staging, and production environments. This skill establishes environment-specific configurations, ensuring optimal model selection, rate limit handling, and secure secret management (using AWS, GCP, Vault) for robust and scalable LLM integrations.

Groq Production Architecture Design

groq-reference-architecture

jeremylongshore/claude-code-plugins-plus-skills

This guide details a production-ready reference architecture for applications built on Groq's LPU inference API. It covers sophisticated model routing based on latency, quality, or cost needs, implementing streaming pipelines, and establishing robust multi-provider fallback chains (e.g., Groq primary to OpenAI backup). Ideal for designing and reviewing complex, resilient LLM systems.

Implementing Groq SDK Patterns

groq-sdk-patterns

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to production-ready patterns for using the Groq SDK in TypeScript and Python. Covers best practices for client singleton setup, type-safe completions, streaming, robust error handling (rate limiting, API errors), and implementing exponential backoff retries to ensure stable, high-performance LLM integration.

Groq Event-Driven Architecture Patterns

groq-webhooks-events

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to building scalable, event-driven architectures using the Groq API. Learn advanced patterns including Server-Sent Events (SSE) for real-time streaming, batch processing with job queues (e.g., BullMQ), and processing incoming webhook events for robust LLM pipelines.

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