Tag: hugging face

Why Hugging Face's faster Transformers work matters beyond GPT-OSS, and how kernels, MXFP4, parallelism, KV cache, batching, and model loading change practical LLM runtime decisions.

Outcome: Mapped the GPT-OSS-era Transformers runtime features into concrete decisions about memory, compute, cache behavior, batching, and serving boundaries.

Why LLM fine-tuning projects fail when teams jump to NeMo or Hugging Face training commands before deciding the model, data, evaluation, serving, and governance loop.

Outcome: Defined a fine-tuning operating loop that connects base-model choice, data curation, PEFT, evaluation, distributed training, serving, and governance into one repeatable release path.