Categories: AI/ML Research

The Machine Learning Practitioner’s Guide to Fine-Tuning Language Models

Fine-tuning has become much more accessible in 2024–2025, with parameter-efficient methods letting even 70B+ parameter models run on consumer GPUs.

5 Problems Encountered Fine-Tuning LLMs with Solutions

Fine-tuning remains a cornerstone technique for adapting general-purpose pre-trained large language models (LLMs) models (also called foundation models) to serve more specialized, high-value downstream tasks, even as zero- and few-shot methods gain traction.

May 1, 2025

In "AI/ML Research"

Hypernetworks for Personalizing ASR to Atypical Speech

*Equal Contributors Parameter-efficient fine-tuning (PEFT) for personalizing automatic speech recognition (ASR) has recently shown promise for adapting general population models to atypical speech. However, these approaches assume a priori knowledge of the atypical speech disorder being adapted for -- the diagnosis of which requires expert knowledge that is not always…

June 18, 2024

In "FAANG"

Custom Fine-Tuning for Domain-Specific LLMs

Fine-tuning a large language model (LLM) is the process of taking a pre-trained model — usually a vast one like GPT or Llama models, with millions to billions of weights — and continuing to train it, exposing it to new data so that the model weights (or typically parts of…

May 15, 2025

In "AI/ML Research"

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