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DSPY 3 Tool Use Tutorial - Advanced tool integration from official DSPy 3.0.1 tutorial with SIMBA optimizer.
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DSPY 3 Tool Use Tutorial - Advanced tool integration from official DSPy 3.0.1 tutorial with SIMBA optimizer.
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DSPY 3 GEPA AI Program Tutorial - Advanced reflective prompt optimization using GEPA.
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DSPY 3 GEPA AIME Tutorial - Math reasoning optimization achieving 46.7% to 56.7% improvement.
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DSPY 3 GEPA Papillon Tutorial - Privacy-conscious delegation optimization with 77% to 86% improvement.
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DSPY 3 GEPA FacilitySupport Tutorial - Enterprise multi-task classification optimization with GEPA.
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DSPY 3 Classification Finetuning Tutorial - Teacher-student bootstrapped finetuning achieving 51% to 86.7% improvement.
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DSPY 3 RL AI Program Tutorial - Experimental reinforcement learning optimization for AI programs.
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DSPY 3 reinforcement learning (RL) optimization for complex multi-hop research tasks using GRPO (Generalized Relative Policy Optimization) and Arbor infrastructure - "src//.py" - "examples//.py" - "notebooks//.ipynb" - "research//.py" - "experiments//.py" - "rltraining//.py" - "multihop//.py" - "retrieval//.py".
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DSPY 3 Implements GRPO optimization for multi-module LM programs that balance response quality with privacy protection by minimizing PII leakage to untrusted external LLMs.
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DSPY 3 MLflow integration for comprehensive tracking and monitoring of DSPy optimization processes.
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Production-ready output refinement using BestOfN and Refine modules for reliable and high-quality DSPy predictions. Implements intelligent retry mechanisms with feedback loops, reward functions, and temperature-based optimization to ensure consistent output quality in production environments.
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DSPY 3 MIPROv2 RAG Optimization - Advanced instruction and demo optimization from official DSPy tutorial.
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DSPY 3 MIPROv2 Classification Optimization - Zero-shot and few-shot classification optimization from official DSPy tutorial.
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Comprehensive example of using MIPROv2 to optimize intelligent agent systems with DSPy 3.0.1. This tutorial demonstrates multi-tool agent optimization, reasoning chain improvement, and production deployment patterns.
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Comprehensive example of using MIPROv2 to optimize complex multi-hop reasoning systems with DSPy 3.0.1. This tutorial demonstrates advanced reasoning chain optimization, knowledge graph traversal, and production-ready multi-step inference systems.
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Comprehensive comparison of MIPROv2 against other DSPy 3.0.1 optimizers including GEPA, SIMBA, BootstrapFewShot, and COPRO. This tutorial demonstrates systematic optimizer evaluation, performance benchmarking, and selection strategies for different use cases.
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NVIDIA Dynamo Installation Prerequisites - Complete setup guide for system dependencies and requirements.
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NVIDIA Dynamo Docker Container Management - Complete containerization strategy for development and production.
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NVIDIA Dynamo Multi-Engine Backend Configuration - Complete guide for vLLM, SGLang, TensorRT-LLM, and LLaMA.cpp backends.
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NVIDIA Dynamo Distributed Deployment on Kubernetes - Production-ready orchestration with Helm, operators, and multi-node scaling.
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NVIDIA Dynamo KV Cache Management (KVBM) - Advanced memory management and offloading strategies for optimal performance.
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NVIDIA Dynamo Performance Optimization and Tuning - Advanced techniques for maximizing throughput, minimizing latency, and optimizing resource utilization.
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NVIDIA Dynamo Production Deployment Best Practices - Enterprise-grade deployment strategies, security, monitoring, and operational excellence.
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NVIDIA Dynamo Advanced Routing and Load Balancing - KV-aware routing, intelligent load distribution, and traffic optimization strategies.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: