《大模型项目实战:多领域智能应用开发》配套资源
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Updated
Mar 14, 2026 - JavaScript
《大模型项目实战:多领域智能应用开发》配套资源
A curated collection of open-source Large Language Model (LLM) projects that are production-ready and can be used for solving real-world problems. This repository focuses on high-performance, scalable LLM solutions across various industries and applications.
Production-ready checklists and frameworks for deploying LLMs, GenAI models, and AI infrastructure. Covers vLLM, Kubernetes, GPU optimization, observability, compliance, and Day-0 to Day-2 operations.
🚀 AI-Native Developer & Computer Engineer | Designing scalable cognitive ecosystems through advanced AI orchestration. Specializing in LLM deployment, Semantic Q&A, and high-performance backend architecture. "I don’t just write code — I orchestrate intelligence."
🧠 A comprehensive toolkit for benchmarking, optimizing, and deploying local Large Language Models. Includes performance testing tools, optimized configurations for CPU/GPU/hybrid setups, and detailed guides to maximize LLM performance on your hardware.
5-Day Hands-on AI Agents Course using Google ADK & Vertex AI | From first agent to production deployment
Structured repository covering LLM foundations, fine-tuning workflows, optimization strategies, deployment patterns, evaluation methods, and Responsible AI considerations.
API to efficiently deploy Language Model (LLM) applications using Flask API
ModelSpec is an open, declarative specification for describing how AI models especially LLMs are deployed, served, and operated in production. It captures execution, serving, and orchestration intent to enable validation, reasoning, and automation across modern AI infrastructure.
Live index of the newest LLMOps tooling — track what's shipping in LLM observability and deployment
Python codes generation from latex expressions. Using synthetic dataset and CodeT5-base model.
Open-source LLM inference benchmarks — TTFT, TPOT, Throughput, Latency & Cost-per-token for models like Llama, Qwen, Gemma, DeepSeek, Gpt-Oss etc. deployed on different dedicated GPUs.
🧠 Инфраструктура для деплоймента Zeroclaw, докерезированная и совместимая с Portainer
LLMOps infrastructure and deployment pipelines using Bicep. Azure-based MLOps for large language model deployment and management.
AWS EKS + IRSA, Volumes, ISTIO & KServe+ NextJS App + Fastapi Serve + kubernetes + Helm charts + Multimodel or LLM-Deployment The School of AI EMLO-V4 course assignment https://theschoolof.ai/#programs
A 6-week hands-on masterclass in production MLOps engineering. Build a file-backed experiment tracker, a containerized model registry, an inference server with dynamic batching, a cached pipeline DAG, automated drift detectors (PSI/KS), and high-performance LLM serving infra (KV-cache/continuous batching) from scratch.
LLM App for summarization of Terms and Conditions agreements available on the internet.
A simple CLI tool to fetch Hugging Face model metadata and estimate required VRAM/RAM for inference.
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