amd
Datasets
All datasets matching “amd”Instella-Long
Instella-Long
The Instella-Long dataset is a collection of pre-training and instruction following data that is used to train Instella-3B-Long-Instruct. The pre-training data is sourced from Prolong. For the SFT data, we use public datasets: Ultrachat 200K, OpenMathinstruct-2, Tülu-3 Instruction Following, and MMLU auxiliary train set. In addition, we generate synthetic long instruction data using documents of the books and arxiv from our pre-training corpus and the dclm subset from… See the full description on the dataset page: https://huggingface.co/datasets/amd/Instella-Long.amd-nrtransformers_daily_citransformers_pr_ciAM-DeepSeek-R1-Distilled-1.4MFor more open-source datasets, models, and methodologies, please visit our GitHub repository.
AM-DeepSeek-R1-Distilled-1.4M is a large-scale general reasoning task dataset composed of
high-quality and challenging reasoning problems. These problems are collected from numerous
open-source datasets, semantically deduplicated, and cleaned to eliminate test set contamination.
All responses in the dataset are distilled from the reasoning model (mostly DeepSeek-R1) and have undergone
rigorous… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M.AM-DeepSeek-Distilled-40MFor more open-source datasets, models, and methodologies, please visit our GitHub repository and paper: DeepDistill: Enhancing LLM Reasoning Capabilities via Large-Scale Difficulty-Graded Data Training.
Due to certain constraints, we are only able to open-source a subset of the complete dataset.
Model Training Performance based on our complete dataset
On AIME 2024, our 72B model achieved a score of 79.2 using only supervised fine-tuning (SFT). The 32B model reached 75.8 and… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-DeepSeek-Distilled-40M.