Tag: Fine Tuning

3 articles

• LLM Gradient Accumulation in Production: Token-Normalized Loss, DDP no_sync, and Scheduler Alignment to Avoid Equivalent Batch Distortion

  Gradient accumulation seems like simple batch summing, but variable-length samples, distributed sync, mixed precision, and learning rate schedulers silently alter the equivalent batch. This article dives into production-grade solutions like token normalization, DDP no_sync, AMP update boundaries, and tail window handling to achieve mathematically consistent gradient accumulation in LLM fine-tuning.

  07/14/2026 LLM Training / Gradient Accumulation / PyTorch DDP / Fine-Tuning / Mixed Precision

• LLM FP8 Fine-Tuning in Production: Amax Scaling, Layer Whitelists, and BF16 Fallback for Stable Convergence

  FP8 reduces compute and memory for LLM fine-tuning, but poor scaling, outliers, and distributed sync issues can cause silent accuracy degradation. This guide provides a complete engineering solution with Amax monitoring, layer whitelists, BF16 fallback, dual-track evaluation, and release gates.

  07/14/2026 LLM Training / FP8 / Transformer Engine / Fine-tuning / MLOps / Mixed Precision Training / Distributed Training / Amax Scaling

• LLM LoRA Adapter Cache in Production: Stabilizing Multi-Tenant Fine-Tuning Services with Heat Tiering, Cold-Start Budgets, and Eviction Policies

  Production strategies for LoRA adapter cache management in multi-tenant fine-tuning services: heat tiering, cold-start budgets, eviction policies, and a governance framework for stable model serving.

  07/09/2026 LLM Serving / LoRA / Adapter Cache / Multi Tenant / Model Serving / GPU / Fine Tuning / RAG / vLLM