Tag
fine-tuning
Fine-tuning adapts a base model to a narrower task or domain, from seeding new vocabulary and aligning instruction behavior to adapting vision-language models. The practical issues are initialization, data quality, VRAM limits, and language coverage, all of which shape output quality and deployment cost.
4 articles
Microsoft’s GoalCover finds fine-tuning gaps
Microsoft Research’s GoalCover spots missing capabilities in fine-tuning data before training, and improved Qwen-3-14B reward scores.
How to Build a Vintage LLM Testbed in 5 Steps
Build a 1930-cutoff LLM testbed to study historical reasoning and contamination-free generalization.
Unsloth Adds Part-by-Part Qwen3.5 Fine-Tuning
Unsloth now lets you fine-tune Qwen3.5 vision models by layer type, with faster training, lower VRAM, and 201-language support.
A Better Way to Seed New LM Tokens
GTI grounds new vocabulary tokens before fine-tuning, aiming to preserve distinctions that mean initialization tends to collapse.