{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/camels-in-a-changing-climate-enhancing-lm","title":"Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2","arxiv_id":"2311.10702","date":"2023-11-17","proceeding":null,"authors":["Hamish Ivison","Yizhong Wang","Valentina Pyatkin","Nathan Lambert","Matthew Peters","Pradeep Dasigi","Joel Jang","David Wadden","Noah A. Smith","Iz Beltagy","Hannaneh Hajishirzi"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2311.10702v2","url_pdf":"https://arxiv.org/pdf/2311.10702v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"camels-in-a-changing-climate-enhancing-lm","repo_url":"https://github.com/allenai/open-instruct","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"camels-in-a-changing-climate-enhancing-lm","repo_url":"https://github.com/alisawuffles/proxy-tuning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"camels-in-a-changing-climate-enhancing-lm","repo_url":"https://github.com/john-hewitt/implicit-ins","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"base","method_name":"BASE"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"cosine-annealing","method_name":"Cosine Annealing"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"gpt-3","method_name":"GPT-3"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-cosine-annealing","method_name":"Linear Warmup With Cosine Annealing"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.10702","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}