├── A2A-prot.md ├── Agentic-Orchestration.md ├── Ai-agent-framework.md ├── Auditing-llm.md ├── AutoML.md ├── Chips-to-Apps.md ├── Evaluation.md ├── FastAPi-llm.md ├── GPU-llm.md ├── Hallucination-llm.md ├── LICENSE ├── LLM-Listeners.md ├── LLM-as-judge.md ├── LLM-debugging.md ├── LLM-develops.md ├── LLM-scaling.md ├── MULTI-GPU-TRAINING-PARADIGMS.md ├── Multimodal-RAG.md ├── RAG-evaluator.md ├── README.md ├── ai-orchestration.md ├── app-agent.md ├── arize-llm.md ├── articles.md ├── azure-llm.md ├── context-engineering.md ├── conversational-modeling.md ├── cookiecutter.md ├── custom-rag-steps ├── data-cleaning.md ├── data-flow.md ├── data-model-drift.md ├── data-processing ├── README.md └── podcast_extraction.py ├── dataset_available.md ├── designs-prod.md ├── edge-llm.md ├── examples.md ├── governance-llm.md ├── guard-rails.md ├── ingredients-llm.md ├── interactive-llm-chat.md ├── kafka-llm.md ├── kubernetes-llm.md ├── langchain-examples.md ├── langchain-fastapi.md ├── langfuse.md ├── linux.md ├── llama-architect.md ├── llamaindex-examples.md ├── llm-beam.md ├── llm-benchmark.md ├── llm-cli.md ├── llm-cloud.md ├── llm-kafka.md ├── llm-os.md ├── llm-prod.md ├── llm-security.md ├── llm-stack.md ├── llm-testing.md ├── llm_preprocess.md ├── llmops-use-case.md ├── memory-usage.md ├── mlflow.md ├── model-serving ├── Multi-Model-Service.md ├── README.md └── model-runtime.md ├── notebooks ├── 1.md ├── End-to-End-RAG │ ├── app.py │ ├── config │ │ ├── config.yml │ │ └── prompts.yml │ ├── rag.py │ └── requirements.txt ├── automation.ipynb ├── data_prep.ipynb ├── llama_agents_with_ollama.ipynb ├── prediction.ipynb └── utils (1).py ├── peliminary-tasks.md ├── pipeline-llm.md ├── planner-execute-llm.md ├── portkey-llm.md ├── red-team-deepteam ├── safetensor2medipipe.md ├── third-party-software.md ├── third-party-ui.md ├── unified-agent-llm.md ├── vLLM.md └── zero-code-llm.md /A2A-prot.md: -------------------------------------------------------------------------------- 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