Model Hub

Specialized code generation model. 32B parameters trained on 5.5T tokens of code data across 90+ languages.

Mistral AI's 7B instruction model. Efficient architecture with sliding window attention and strong benchmark performance.

NVIDIA's optimized Llama 3.1 70B. Custom alignment for helpfulness with strong benchmark performance.

✨ Note: For all FineInstructions resources please visit: https://huggingface.co/fineinstructions This dataset is ~1B+ synthetic instruction-answer pairs or ~300B tokens created using the FineInstructions pipeline. The FineInstructions pipeline was run over the raw pre-training documents in the Nemotron-CC pre-training corpus (a subset of high-quality documents from CommonCrawl). See our paper for more details. Each .parquet file in the data folderhas a corresponding judge-*.json file that… See the full description on the dataset page: https://huggingface.co/datasets/fineinstructions/fineinstructions_nemotron.

Innovator-VL-Instruct-46M Paper | Code 🤗🤗 The data is being uploaded continuously Introduction To further enhance the model’s ability to handle a broad range of visual tasks with accurate, grounded, and instruction-aligned responses, we perform full-parameter visual instruction supervised fine-tuning (SFT).This SFT stage serves as a critical bridge between multimodal pretraining and subsequent reinforcement learning, providing both general capability coverage… See the full description on the dataset page: https://huggingface.co/datasets/InnovatorLab/Innovator-VL-Instruct-46M.

Leopard-Instruct Paper | Github | Models-LLaVA | Models-Idefics2 Summaries Leopard-Instruct is a large instruction-tuning dataset, comprising 925K instances, with 739K specifically designed for text-rich, multiimage scenarios. It's been used to train Leopard-LLaVA [checkpoint] and Leopard-Idefics2 [checkpoint]. Loading dataset to load the dataset without automatically downloading and process the images (Please run the following codes with datasets==2.18.0)… See the full description on the dataset page: https://huggingface.co/datasets/wyu1/Leopard-Instruct.

OpenMathInstruct-2 OpenMathInstruct-2 is a math instruction tuning dataset with 14M problem-solution pairs generated using the Llama3.1-405B-Instruct model. The training set problems of GSM8K and MATH are used for constructing the dataset in the following ways: Solution augmentation: Generating chain-of-thought solutions for training set problems in GSM8K and MATH. Problem-Solution augmentation: Generating new problems, followed by solutions for these new problems.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMathInstruct-2.

LLaVA-OneVision-1.5 Instruction Data Paper | Code 📌 Introduction This dataset, LLaVA-OneVision-1.5-Instruct, was collected and integrated during the development of LLaVA-OneVision-1.5. LLaVA-OneVision-1.5 is a novel family of Large Multimodal Models (LMMs) that achieve state-of-the-art performance with significantly reduced computational and financial costs. This meticulously curated 22M instruction dataset (LLaVA-OneVision-1.5-Instruct) is part of a comprehensive and… See the full description on the dataset page: https://huggingface.co/datasets/mvp-lab/LLaVA-OneVision-1.5-Instruct-Data.

Instruction Pre-Training: Language Models are Supervised Multitask Learners (EMNLP 2024) This repo contains the general instruction-augmented corpora (containing 200M instruction-response pairs covering 40+ task categories) used in our paper Instruction Pre-Training: Language Models are Supervised Multitask Learners. We explore supervised multitask pre-training by proposing Instruction Pre-Training, a framework that scalably augments massive raw corpora with instruction-response… See the full description on the dataset page: https://huggingface.co/datasets/instruction-pretrain/general-instruction-augmented-corpora.