Tag: llm fine-tuning

A practical MLX-first recipe for experimenting with openai/gpt-oss-20b on a 64GB Apple Silicon Mac without confusing local LoRA work for CUDA-scale training.

Outcome: Defined a local 64GB MacBook Pro fine-tuning path for GPT-OSS 20B that prioritizes Harmony formatting, MLX quantized LoRA, small evals, and a clear fallback to NVIDIA when scale is required.

Why Apple Silicon is useful for local LLM prototyping and LoRA experiments, but still has sharp boundaries compared with CUDA-scale NeMo or Hugging Face training.

Outcome: Separated Mac-local MPS and MLX fine-tuning paths from NVIDIA-only training features so local experiments can start with realistic hardware expectations.

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.

A practical map of NVIDIA NeMo for teams that want to curate data, fine-tune open-source LLMs, evaluate them, and move from research checkpoints to production inference.

Outcome: Separated data curation, fine-tuning, alignment, evaluation, export, and serving concerns so open-source LLM customization could move from experiments to governed production workflows.