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Taki
Taki

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Open source AI stack components

Here’s a comprehensive and categorized list of open source AI stack components that you can mix and match when building GenAI applications — especially when focusing on modularity, scalability, and performance. This includes components for data processing, model serving, retrieval-augmented generation (RAG), vector search, and orchestration.


🧠 Foundational Model Alternatives

Models you can self-host or fine-tune:

  • LLMs

  • Multimodal

    • llava – Language + vision.
    • bakllava – More optimized multimodal variant.
    • CLIP – Text-image understanding.
  • Fine-Tuning

    • QLoRA, LoRA, PEFT (via 🤗 Transformers + PEFT)
    • Axolotl – Full stack fine-tuning.

📚 RAG (Retrieval-Augmented Generation) Stack

Tools to power knowledge-based Q&A systems:


🔧 Serving & Orchestration

Serving models with APIs, managing prompts, memory, and chaining tools:


🖼️ Frontend / Chat UI

For chatbots or multimodal interfaces:


🚀 Inference & Runtime Optimization

  • llm.rs – LLM inference in Rust.
  • ggml – Quantized models, runs on CPU.
  • exllama – High-perf quantized inference.

🔒 Security & DevOps (for production)

  • AuthN/AuthZ: [Auth.js (NextAuth)], [Clerk], [Ory], [ZITADEL]
  • Logging/Tracing: [Langfuse], [OpenTelemetry], [Sentry]
  • DevOps: Docker, Kubernetes, GitHub Actions, Terraform

🧱 Full Stack Boilerplates

If you're looking to start fast:


🧪 Experimental Tools

  • Ollama – Run and manage LLMs locally.
  • Modal – Serverless infra for AI.
  • LiteLLM – Drop-in proxy for OpenAI-compatible APIs.

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Top comments (1)

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ngtduc693 profile image
Duc Nguyen Thanh

Could you write a new topic about how to use that

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