#Fine Tuning News.

The generative AI customization spectrum: From prompt engineering to custom models on AWS

Pick the right generative AI customization approach on AWS with an 8-step decision framework, from prompt engineering and RAG to fine-tuning, continued pre-training, and Amazon Nova Forge. Start simple and escalate only when you must.

How Fyxer built an AI executive assistant people trust

Fyxer uses OpenAI models, fine-tuning, memory, and real user feedback to organize inboxes and draft emails in each user’s voice.

Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Preparing data for supervised fine-tuning Part 2: Advanced data strategies

The advanced side of supervised fine-tuning data prep. This second post in a two-part series covers evaluating data readiness with learning curves, selecting high-value data subsets, augmenting data with synthetic and distilled examples, and mixing data sources to prevent catastrophic forgetting.

Preparing data for supervised fine-tuning Part 1: Formatting and quality

Data preparation determines the ceiling of any supervised fine-tuning project. This first post in a two-part series covers the foundations of SFT data prep: quality checks, conversational (JSONL) formatting, reasoning and tool-calling schemas, and a representative train/evaluation split.

PROOF-Gen: From Optimized Data to Better Distillation

Supervised fine-tuning on teacher-generated trajectories is the standard first stage for distilling tool-calling capabilities into deployable models. Post-training pipelines that drive shipped tool-calling agents re-run this stage on a daily or weekly cadence, paying the frontier-teacher cost each cycle, yet the mechan...

Accelerating Transformers Fine-Tuning with NVIDIA NeMo AutoModel

A Blog post by NVIDIA on Hugging Face

Beyond LoRA: Can you beat the most popular fine-tuning technique?

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

20x Faster TRL Fine-tuning with RapidFire AI

Doppel’s AI defense system stops attacks before they spread

Doppel uses GPT-5 and reinforcement fine-tuning to stop deepfake and impersonation attacks, cutting analyst workloads by 80% and reducing response times from hours to minutes.

Introducing AgentKit, new Evals, and RFT for agents

Today, we’re releasing new tools to help developers go from prototype to production faster: AgentKit, expanded evals capabilities, and reinforcement fine-tuning for agents.

Estimating worst case frontier risks of open weight LLMs

In this paper, we study the worst-case frontier risks of releasing gpt-oss. We introduce malicious fine-tuning (MFT), where we attempt to elicit maximum capabilities by fine-tuning gpt-oss to be as capable as possible in two domains: biology and cybersecurity.

(LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware

Toward understanding and preventing misalignment generalization

We study how training on incorrect responses can cause broader misalignment in language models and identify an internal feature driving this behavior—one that can be reversed with minimal fine-tuning.

OpenAI o1 and new tools for developers

Introducing OpenAI o1, Realtime API improvements, a new fine-tuning method and more for developers.

Building smarter maps with GPT-4o vision fine-tuning

Building smarter maps with GPT-4o vision fine-tuning

Argilla 2.4: Easily Build Fine-Tuning and Evaluation Datasets on the Hub — No Code Required

Introducing vision to the fine-tuning API

Developers can now fine-tune GPT-4o with images and text to improve vision capabilities

Fine-tuning LLMs to 1.58bit: extreme quantization made easy