CoolFace
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llama3.3

evanellis /Codeforces-Python_Submissions_reformatted_deduped_llama3.3_nomic_ftabular10K<n<100K0 likes201 downloads1y agoHugging Facetwinkle-ai /Llama-3.3-70B-Instruct-eval-logs-and-scorestabular100K<n<1M0 likes180 downloads7mo agoHugging Facefuture-architect /Llama-3.3-Future-Code-Instructions Llama 3.3 Future Code Instructions Llama 3.3 Future Code Instructions is a large-scale instruction dataset synthesized with the Meta Llama 3.3 70B Instruct model. The dataset was generated with the method called Magpie, where we prompted the model to generate instructions likely to be asked by the users. In addition to the original prompt introduced by the authors, we conditioned the system prompt on what specific programming language the user has an interest in, gaining control… See the full description on the dataset page: https://huggingface.co/datasets/future-architect/Llama-3.3-Future-Code-Instructions.text1M<n<10M0 likes159 downloads1y agoHugging Faceevanellis /Codeforces-Python_Submissions_reformatted_deduped_llama3.3_x6_multiplied_with_blind_null_f_emptabular10K<n<100K0 likes103 downloads1y agoHugging Facenebius /Llama-3.3-70B-Instruct-Infinity-Instruct-0625 Llama-3.3-70B-Instruct-Infinity-Instruct-0625 Dataset Description This dataset is part of the LK-Speculators collection for speculative decoding research. It contains 660K prompt-response pairs designed for training draft models that are used alongside Llama-3.3-70B-Instruct as the target model. The dataset was created by generating responses to the prompts from Infinity-Instruct-0625 with meta-llama/Llama-3.3-70B-Instruct at temperature=1. For more details on the… See the full description on the dataset page: https://huggingface.co/datasets/nebius/Llama-3.3-70B-Instruct-Infinity-Instruct-0625.texttext-generation100K<n<1M0 likes74 downloads7mo agoHugging Facemzhaoshuai /Llama-3.3-70B-Inst-awq_SafeRLHF Llama-3.3-70B-Inst-awq Responses for RefAlign Safety Alignment This dataset contains responses generated for the paper Learning from Reference Answers: Versatile Language Model Alignment without Binary Human Preference Data, which introduces the RefAlign alignment algorithm. Code Repository: https://github.com/mzhaoshuai/RefAlign This dataset specifically consists of responses generated by the casperhansen/llama-3.3-70b-instruct-awq model, given the prompts from the… See the full description on the dataset page: https://huggingface.co/datasets/mzhaoshuai/Llama-3.3-70B-Inst-awq_SafeRLHF.text-generation0 likes67 downloads1y agoHugging Face