FineTuning

ORPO: Preference Optimization without the Supervised Positive-tuning (SFT) Step

A less expensive alignment method performing in addition to DPOThere are actually many methods to align large language models (LLMs) with human preferences. Reinforcement learning with human feedback (RLHF) was one in all the...

Introducing improvements to the fine-tuning API and expanding our custom models program

Assisted Advantageous-TuningAt DevDay last November, we announced a Custom Model program designed to coach and optimize models for a selected domain, in partnership with a dedicated group of OpenAI researchers. Since then, we have...

RAFT – A High quality-Tuning and RAG Approach to Domain-Specific Query Answering

Because the applications of enormous language models expand into specialized domains, the necessity for efficient and effective adaptation techniques becomes increasingly crucial. Enter RAFT (Retrieval Augmented High quality Tuning), a novel approach that mixes...

A Full Guide to Tremendous-Tuning Large Language Models

Large language models (LLMs) like GPT-4, LaMDA, PaLM, and others have taken the world by storm with their remarkable ability to know and generate human-like text on an unlimited range of topics. These models...

Understanding LoRA — Low Rank Adaptation For Finetuning Large Models

Math behind this parameter efficient finetuning methodNice-tuning large pre-trained models is computationally difficult, often involving adjustment of thousands and thousands of parameters. This traditional fine-tuning approach, while effective, demands substantial computational resources and time,...

GPT-3.5 Turbo fine-tuning and API updates

Developers can now bring their very own data to customize GPT-3.5 Turbo for his or her use cases.

OpenAI partners with Scale to supply support for enterprises fine-tuning models

OpenAI and Scale are joining forces to assist more firms profit from fine-tuning our most advanced models.Corporations expect high performance, steerability, and customization relating to deploying AI in production. We recently launched fine-tuning for...

Text Classification Challenge with Extra-Small Datasets: Positive-Tuning Versus ChatGPT The dataset Regular fine-tuning with RoBERTa Few-shot with ChatGPT Positive-tuning a GPT-3 model Conclusion Sources

LLMs excel on extra-small datasets, but classical approaches shine as datasets growOnce more, performance was heavily influenced by the prompt and the samples provided. The model also generated several categories outside the goal list,...

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