Fine-tuning and adaptation

Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling

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Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers

We Got Claude to Fine-Tune an Open Source LLM

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.

Fine-tune Any LLM from the Hugging Face Hub with Together AI

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.

Unlocking the potential of vision language models on satellite imagery through fine-tuning

Fast LoRA inference for Flux with Diffusers and PEFT

Training and Finetuning Sparse Embedding Models with Sentence Transformers

(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.

Finetuning olmOCR to be a faithful OCR-Engine

Training and Finetuning Reranker Models with Sentence Transformers

How to deploy and fine-tune DeepSeek models on AWS

OpenAI o1 and new tools for developers

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

Investing in Performance: Fine-tune small models with LLM insights - a CFM case study

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