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AWQ Weight Quantization

awq-quantization

Orchestra-Research/AI-Research-SKILLs

Activation-aware weight quantization that compresses 7B-70B models to 4-bit with minimal accuracy loss, delivering ~3x inference speedups and memory reductions for constrained GPU deployments while integrating with vLLM and Marlin kernels.

Batch Inference Pipeline

batch-inference-pipeline

jeremylongshore/claude-code-plugins-plus-skills

Guides ML teams through automated batch inference pipelines, suggesting best practices, monitoring and production readiness, and generating code and configs for deployment.

Context Engineering For LLMs

context-fundamentals

sickn33/antigravity-awesome-skills

A comprehensive guide to understanding the complete state (context) available to Large Language Models during inference. Covers core components like System Prompts, Tool Definitions, Message History, and Retrieval Augmented Generation (RAG). Learning to engineer context is crucial for building robust, efficient, and reliable AI agent systems by managing the attention budget and ensuring high-signal information delivery.

Databricks ML Workflow: Training and Serving

databricks-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

This guide outlines the complete MLOps workflow on Databricks. It covers feature engineering using Feature Store, tracking experiments with MLflow, managing model versions in the Model Registry, and deploying real-time inference endpoints via Mosaic AI Model Serving. Ideal for building robust, production-grade ML pipelines.

Feature Store Connector

feature-store-connector

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance for setting up and validating feature store connectors within ML deployment pipelines, covering best practices, code generation, and production monitoring for inference.

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 Cost Optimization Techniques

groq-cost-tuning

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to optimizing Groq API usage and managing inference costs. Learn techniques such as smart model routing (selecting smaller models for simple tasks), minimizing prompt tokens, batching requests to reduce overhead, and implementing caching for deterministic calls. This guide helps developers achieve significant cost savings and improve system efficiency when handling high-volume AI workloads.

Groq Deployment Across Cloud Platforms

groq-deploy-integration

jeremylongshore/claude-code-plugins-plus-skills

Comprehensive guide for deploying applications powered by the Groq inference API. Covers best practices and specific setup instructions for multiple production environments, including Vercel Edge Functions, Google Cloud Run, Docker containers, and other containerized platforms. Essential for setting up robust, low-latency, production-ready AI services.

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 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.

GroqCloud Automation Suite

groqcloud-automation

ComposioHQ/awesome-claude-skills

GroqCloud Automation orchestrates high-performance GroqCloud APIs through Composio, covering inference, chat completions, audio translation, and TTS voice selection for production workflows.

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