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.
16 articles
Fine-tune a small LLM for legal labeling
A 3B SmolLM3 model reached 81.7% on legal labeling after 74 minutes of fine-tuning.
Do You Need to Pretrain Q-Functions?
A new method says online RL fine-tuning can work better when Q-functions start from diverse policy rollouts.
QVAC turns consumer hardware into local AI
I break down Tether’s QVAC stack and give you a copy-ready pattern for local-first AI on consumer hardware.
Fine-tuning beats RAG when the goal is style, not facts
Fine-tuning is the right tool for teaching an LLM a writing style, while RAG is the wrong tool for that job.
Tether's Bitnet fine-tuning brings AI to edge devices
Tether says its Bitnet LoRA framework can fine-tune a 13B model on consumer devices, pushing AI training closer to phones and PCs.
How ESMA Teaches LLMs Self-Knowledge
A bias-controlled fine-tuning method improves LLM self-knowledge and generalizes across unseen data, languages, and new facts.
Why fine-tuning still beats prompt-only AI
Fine-tuning remains the best way to make foundation models reliable for specific tasks.
5 steps to fine tune a local LLM
5 steps to fine tune a local LLM in a weekend, from setup and data prep to training, evaluation, and GGUF export.
How to Build AI Research Foundations with DeepMind
Follow this guide to build a practical foundation in modern language models and fine-tuning.
7 reasons Unsloth Studio helps local AI
7 reasons Unsloth Studio makes local AI training, chat, and export easier with offline workflows and 500+ model support.
21 domain LLMs turn generic AI into specialists
I break down 21 specialty LLMs and turn that list into a copy-ready playbook for picking, tuning, and shipping one.
PEFT-Bench compares fine-tuning methods fairly
PEFT-Bench standardizes how to compare PEFT methods across 27 NLP datasets and 7 techniques.
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.