Robuta

Fine-tuning large language models (LLMs) is increasingly costly as models scale to hundreds of billions of parameters, and even parameter-efficient fine-tuning...

Zorik Gekhman, Gal Yona, Roee Aharoni, Matan Eyal, Amir Feder, Roi Reichart, Jonathan Herzig. Proceedings of the 2024 Conference on Empirical Methods in...

Open source fine-tuning & reinforcment learning (RL) for gpt-oss, Llama 4, DeepSeek-R1, Gemma, and Qwen3 LLMs! Beginner friendly.

Offered by IBM. "Fine-tuning large language models (LLMs) is essential for aligning them with specific business needs, improving accuracy, ... Enroll for free.

What is fine-tuning? How can it help businesses do more with large language models?

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Nov 27, 2025 - I share my top techniques for fine-tuning LLMs, along with best practices, a step-by-step guide, and how it compares to RAG and prompt engineering.

Apply fine-tuning and reinforcement learning techniques to shape model behavior, improve reasoning, and make LLMs safer and more reliable.

This paper introduces TempSamp-R1, a new reinforcement fine-tuning framework designed to improve the effectiveness of adapting multimodal large language models...

Discover 7 essential tips for fine-tuning LLMs effectively—from choosing the right base model to avoiding common performance pitfalls.

From common-sense reasoning to domain-specific tasks, parameter-efficient fine tuning (PEFT) methods for large language models (LLMs) have showcased...