Tutorials on Performance

Learn about Performance from fellow newline community members!

  • React
  • Angular
  • Vue
  • Svelte
  • NextJS
  • Redux
  • Apollo
  • Storybook
  • D3
  • Testing Library
  • JavaScript
  • TypeScript
  • Node.js
  • Deno
  • Rust
  • Python
  • GraphQL
  • React
  • Angular
  • Vue
  • Svelte
  • NextJS
  • Redux
  • Apollo
  • Storybook
  • D3
  • Testing Library
  • JavaScript
  • TypeScript
  • Node.js
  • Deno
  • Rust
  • Python
  • GraphQL
NEW

Low-Latency LLM Inference with GPU Partitioning

Explore how GPU partitioning enhances LLM performance, balancing latency and throughput for real-time applications.
NEW

Prompt Debugging vs. Fine-Tuning: Key Differences

Explore the differences between prompt debugging and fine-tuning for optimizing language models, including when and how to use each approach effectively.

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NEW

Ultimate Guide to Task-Specific Benchmarking

Explore the significance of task-specific benchmarking for AI models, focusing on practical applications, evaluation methods, and emerging trends.
NEW

Stemming vs Lemmatization: Impact on LLMs

Explore the differences between stemming and lemmatization in LLMs, their impacts on efficiency vs. accuracy, and optimal strategies for usage.

Hyperparameter Tuning in Hugging Face Pipelines

Master hyperparameter tuning in Hugging Face pipelines to enhance model performance effectively through automated techniques and best practices.

Key Metrics for Multimodal Benchmarking Frameworks

Explore essential metrics for evaluating multimodal AI systems, focusing on performance, efficiency, stability, and fairness to ensure reliable outcomes.

Event-Driven Pipelines for AI Agents

Explore how event-driven pipelines enhance AI agents with real-time processing, scalability, and efficient data handling for modern applications.

Relative vs. Absolute Positional Embedding in Decoders

Explore the differences between absolute and relative positional embeddings in transformers, highlighting their strengths, limitations, and ideal use cases.

Annotated Transformer: LayerNorm Explained

Explore how LayerNorm stabilizes transformer training, enhances gradient flow, and improves performance in NLP tasks through effective normalization techniques.

How to Scale Hugging Face Pipelines for Large Datasets

Learn practical strategies to efficiently scale Hugging Face pipelines for large datasets, optimizing memory, performance, and workflows.

LLM Monitoring vs. Traditional Logging: Key Differences

Explore the critical differences between LLM monitoring and traditional logging in AI systems, focusing on output quality, safety, and compliance.

QLoRA: Fine-Tuning Quantized LLMs

QLoRA revolutionizes fine-tuning of large language models, slashing memory usage and training times while maintaining performance.

Real-Time Monitoring for RAG Agents: Key Metrics

Explore essential metrics and challenges in real-time monitoring of Retrieval-Augmented Generation agents to ensure optimal performance and reliability.

How to Evaluate Prompts for Specific Tasks

Learn effective strategies for evaluating AI prompts tailored to specific tasks, ensuring improved accuracy and relevance in outputs.

How to Use Optuna for LLM Fine-Tuning

Learn how to efficiently fine-tune large language models using Optuna's advanced hyperparameter optimization techniques.

Real-World LLM Benchmarks: Metrics and Methods

Explore essential metrics, methods, and frameworks for evaluating large language models, addressing performance, accuracy, and environmental impact.

Lightweight Transformers with Knowledge Distillation

Explore how lightweight transformers and knowledge distillation enhance AI performance on edge devices, achieving efficiency without sacrificing accuracy.

How RAG Enables Real-Time Knowledge Updates

Explore how Retrieval-Augmented Generation (RAG) enhances real-time knowledge updates, improving accuracy and efficiency across various industries.

Research on Mixed-Precision Training for LLMs

Explore how mixed-precision training revolutionizes large language models by enhancing speed and efficiency while maintaining accuracy.

Agentic RAG: Optimizing Knowledge Personalization

Explore the evolution from Standard RAG to Agentic RAG, highlighting advancements in knowledge personalization and AI's role in complex problem-solving.

Error Tracking for LLMs in Cloud Hosting

Learn how effective error tracking for large language models in cloud environments boosts performance, reduces costs, and ensures reliability.

Best Practices for LLM Latency Benchmarking

Optimize LLM latency by mastering benchmarking techniques, key metrics, and best practices for improved user experience and performance.

Energy-Saving Techniques for LLM Inference

Explore effective strategies to reduce energy consumption during large language model inference without sacrificing performance.

Best Practices for Labeling Error Detection

Learn best practices for detecting labeling errors in AI data, combining automated tools and manual reviews for reliable outcomes.

Guide to AI Agent Performance Metrics

Explore vital performance metrics for AI agents, including accuracy, efficiency, and advanced metrics to optimize effectiveness and user satisfaction.

Root Cause Analysis for AI Automation Errors

Explore how Root Cause Analysis can resolve underlying issues in AI automation, improving reliability and reducing costly errors.

Ultimate Guide to Feedback-Driven LLM Fine-Tuning

Explore how feedback-driven fine-tuning enhances the effectiveness of Large Language Models in customer service by leveraging real user insights.

How to Build Dashboards for LLM Monitoring

Learn how to effectively monitor large language models with dashboards that track performance, quality, costs, and compliance.

Fine-Tuning Decoder-Only Transformers with LoRA

Learn how LoRA simplifies the fine-tuning of large language models, reducing resource requirements while maintaining performance.

Challenges in Multi-Agent LLM Collaboration

Explore the challenges of multi-agent collaboration using large language models, including task assignment, communication, and memory management.