Category: Machine Learning
Running LLMs on Edge Devices: A Practical Guide to Model Compression
Learn how to deploy LLMs on smartphones and IoT devices using model compression. We cover quantization, pruning, and distillation with practical tips for real-world hardware.
Calibrating Confidence in Non-English LLM Outputs: A Practical Guide
Learn how to fix overconfidence in non-English AI outputs. We cover practical methods like multicalibration and temperature scaling to ensure your LLMs are trustworthy in any language.
Calibrating Generative AI: Reducing Hallucination Risk by Aligning Confidence with Accuracy
Learn how to calibrate generative AI models to reduce hallucination risk. We explore the CGM framework, traditional methods like Platt scaling, and practical strategies to align model confidence with real-world accuracy.
Latency Budgets for Interactive LLM Apps: A Practical Guide
Learn how to set effective latency budgets for interactive LLM apps. Understand TTFT, decode bottlenecks, and optimization techniques like speculative decoding.
Prompt Length vs Output Quality: LLM Decoding Tradeoffs
Discover why longer prompts often reduce LLM accuracy. Learn the optimal token ranges, the impact of recency bias, and strategies like RAG to boost output quality and cut costs.
Mixed-Precision Training for LLMs: FP16, BF16, and Beyond
Master mixed-precision training for LLMs. Learn the differences between FP16 and BF16, how to implement AMP in PyTorch, and why BF16 is the preferred choice for stable, fast training pipelines.
Parallel Transformer Decoding Strategies for Low-Latency LLM Responses
Explore parallel transformer decoding strategies like Skeleton-of-Thought and FocusLLM that cut LLM latency by up to 50%. Learn how these methods replace slow sequential generation with simultaneous token processing.
How LLMs Are Transforming Healthcare: A Guide to AI Documentation and Triage
Explore how Large Language Models are revolutionizing healthcare by automating clinical documentation and improving triage accuracy. Learn about key tools, risks, and implementation challenges.
How to Stop LLM Drift and Repetition in Long-Form Generation
Struggling with AI-generated text that goes off-track? Learn practical strategies to stop LLM drift and repetition in long-form content using RAG, prompt engineering, and temperature tuning.
Tokenization in Generative AI: BPE, WordPiece, and Future Methods Explained
Explore how tokenization powers generative AI. Learn the differences between Byte Pair Encoding, WordPiece, and new methods, and why choosing the right tokenizer impacts cost and accuracy.
Vision-Language Applications with Multimodal Large Language Models: A Practical Guide
Explore how Vision-Language Models merge sight and speech. We compare top MLLMs like GLM-4.6V, analyze costs, and discuss real-world apps in finance and healthcare.
Fine-Tuned Models for Niche Stacks: When Specialization Beats General LLMs
Discover when fine-tuned models beat general LLMs. Learn about QLoRA, data requirements, and why specialization wins in niche stacks.
