Camels In A Changing Climate: Enhancing LM Adaptation With Tulu 2

Ivison Hamish, Wang Yizhong, Pyatkin Valentina, Lambert Nathan, Peters Matthew, Dasigi Pradeep, Jang Joel, Wadden David, Smith Noah A., Beltagy Iz, Hajishirzi Hannaneh. Arxiv 2023

[Paper]    
Efficiency And Optimization GPT Model Architecture Reinforcement Learning Training Techniques Uncategorized

Since the release of T"ULU [Wang et al., 2023b], open resources for instruction tuning have developed quickly, from better base models to new finetuning techniques. We test and incorporate a number of these advances into T"ULU, resulting in T"ULU 2, a suite of improved T"ULU models for advancing the understanding and best practices of adapting pretrained language models to downstream tasks and user preferences. Concretely, we release: (1) T"ULU-V2-mix, an improved collection of high-quality instruction datasets; (2) T"ULU 2, LLAMA-2 models finetuned on the V2 mixture; (3) T"ULU 2+DPO, T"ULU 2 models trained with direct preference optimization (DPO), including the largest DPO-trained model to date (T"ULU 2+DPO 70B); (4) CODE T"ULU 2, CODE LLAMA models finetuned on our V2 mix that outperform CODE LLAMA and its instruction-tuned variant, CODE LLAMA-Instruct. Our evaluation from multiple perspectives shows that the T"ULU 2 suite achieves state-of-the-art performance among open models and matches or exceeds the performance of GPT-3.5-turbo-0301 on several benchmarks. We release all the checkpoints, data, training and evaluation code to facilitate future open efforts on adapting large language models.

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