datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.SAND-Post-Training-Dataset
SAND-Post-Training-Dataset: High-Quality Synthetic Reasoning Dataset Built with AMD GPUs
Dataset Summary
We introduce the SAND-Post-Training-Dataset, a high-quality synthetic reasoning dataset for mathematics and science built entirely using a synthetic data pipeline running on the AMD ROCm™ stack and AMD Instinct™ MI325 GPUs.
This dataset prioritizes difficulty and novelty over volume, demonstrating that high-difficulty synthetic data can elevate… See the full description on the dataset page: https://huggingface.co/datasets/amd/SAND-Post-Training-Dataset.SAND-MATH
SAND-Math: A Synthetic Dataset of Difficult Problems to Elevate LLM Math Performance
📃 Paper | 🤗 Dataset
SAND-Math (Synthetic Augmented Novel and Difficult Mathematics) is a high-quality, high-difficulty dataset of mathematics problems and solutions. It is generated using a novel pipeline that addresses the critical bottleneck of scarce, high-difficulty training data for mathematical Large Language Models (LLMs).
Key Features
Novel Problem Generation: Problems are… See the full description on the dataset page: https://huggingface.co/datasets/amd/SAND-MATH.amd__AMD-Llama-135m-details
Dataset Card for Evaluation run of amd/AMD-Llama-135m
Dataset automatically created during the evaluation run of model amd/AMD-Llama-135m
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/amd__AMD-Llama-135m-details.autotrain-data-amdal-mining-llama2-7b-clean