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Search LLM Integration , found 14 results

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AWQ Weight Quantization

awq-quantization

Orchestra-Research/AI-Research-SKILLs

AWQ provides activation-aware 4-bit quantization for large language models, delivering ~3x inference speedup and sub-5% accuracy loss to deploy instruction-tuned or multimodal models on memory-constrained GPUs with vLLM integration and Marlin kernels.

Groq Reference Architecture

groq-reference-architecture

jeremylongshore/claude-code-plugins-plus-skills

Defines best-practice Groq deployment with tiered model routing, middleware, streaming pipelines, and fallback chains for ultra-fast LLM inference and production monitoring when launching new Groq integrations.

LangChain Hello World

langchain-hello-world

jeremylongshore/claude-code-plugins-plus-skills

Provides a minimal LangChain example that walks through defining a prompt, chaining it to an LLM, and invoking it from Python or TypeScript to confirm the integration setup and learn basic chain/prompt patterns.

LangChain Webhooks Events

langchain-webhooks-events

jeremylongshore/claude-code-plugins-plus-skills

Build LangChain callback handlers that push LLM, chain, and tool events to webhooks, stream tokens via WebSocket/SSE, and coordinate real-time integrations.

Langfuse Observability Setup

langfuse-observability

jeremylongshore/claude-code-plugins-plus-skills

Set up comprehensive Langfuse observability with Prometheus metrics, Grafana dashboards, and AlertManager rules to monitor LLM operations, track latency, costs, and errors, and ensure integration health.

LlamaGuard Content Moderation

Orchestra-Research/AI-Research-SKILLs

LlamaGuard is Meta’s 7–8B safety-specialized LLM that filters both prompts and responses by classifying six threat categories, enabling fast inference via vLLM/SageMaker and integration into NeMo Guardrails for end-to-end moderation.

AI Assistant Development

llm-application-dev-ai-assistant

sickn33/antigravity-awesome-skills

Guides AI assistant development experts to build intelligent conversational interfaces, chatbots, and AI-powered workflows with natural language understanding, context management, and integration checklists.

LLM Integration Patterns

llm-integration

rohitg00/awesome-claude-code-toolkit

Practical guide to integrating Claude models: covers API client usage, streaming responses, tool calls, RAG pipelines, document chunking, cost-aware model selection, and anti-pattern warnings for efficient LLM workflows.

Mistral Security Basics

mistral-security-basics

jeremylongshore/claude-code-plugins-plus-skills

Guides teams through securing Mistral AI integrations by managing API keys, sanitizing prompts and outputs, logging safely, and rotating secrets to reduce exposure when deploying LLM workloads.

Programmable LLM Safety Rails

nemo-guardrails

Orchestra-Research/AI-Research-SKILLs

NVIDIA’s NeMo Guardrails adds runtime safety rails to LLM apps, covering jailbreak detection, hallucination and fact-checking, PII filtering, and toxicity checks via Colang 2.0 rules and integrations for production use on T4 GPUs.

Ollama Local Deployment

jeremylongshore/claude-code-plugins-plus-skills

Automates Ollama installation, model selection, and integration so you can run self-hosted LLMs offline on macOS, Linux, or Docker with curated hardware guidance, Python/Node.js clients, and REST API verification.

Retell AI Performance Tuning

retellai-performance-tuning

jeremylongshore/claude-code-plugins-plus-skills

Guide for optimizing Retell AI voice agents by tuning LLM prompts, agent settings, WebSocket pooling, and caching/analytics so API integrations stay responsive under load.

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