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title: README |
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emoji: 👀 |
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sdk: static |
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license: apache-2.0 |
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## Deci was acquired by Nvidia Corporation of Santa Clara, CA. in May 2024. |
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## **[DeciLM-7B](https://huggingface.co/Deci/DeciLM-7B):** |
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A 7.04 billion-parameter decoder-only text generation model, licensed under Apache 2.0. DeciLM-7B is not only the most accurate 7B base model to date, but it also currently outpaces all models in its class with a throughput that is up to 4.4x that of Mistral-7B's. DeciLM-7B’s architecture is the result of Deci's Neural Architecture Search technology. The model was fine-tuned using LoRA on the SlimOrca dataset, creating [DeciLM-7B-instruct](https://huggingface.co/Deci/DeciLM-7B-instruct). |
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## **[DeciCoder 1B](https://huggingface.co/Deci/DeciCoder-1b):** |
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A permissively licensed 1.1 billion-parameter code generation model generated by Deci's Neural Architecture Search technology. |
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Equipped with a 2048-context window, DeciCoder 1B delivers a 3.5x increase in throughput, improved accuracy on the HumanEval benchmark, and reduced memory usage compared to widely-used code generation LLMs such as SantaCoder. |
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## **[DeciLM 6B](https://huggingface.co/Deci/DeciLM-6b):** |
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A permissively licensed, 5.7 billion-parameter pretrained text generation model using variable Grouped Query Attention (GQA) to achieve an optimal balance between performance and computational efficiency. Generated by Deci's proprietary Neural Architecture Search technology, AutoNAC™, DeciLM 6B delivers 15x the throughput of Llama 2 7B while maintaining comparable quality. |
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DeciLM-6B was fine-tuned using LoRA for instruction-following on a subset of the OpenOrca dataset, creating [DeciLM 6B-Instruct](https://huggingface.co/Deci/DeciLM-6b-instruct). |
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## **[DeciDiffusion](https://huggingface.co/Deci/DeciDiffusion-v1-0):** |
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A permissively licensed, text-to-image latent diffusion model generated by Deci's Neural Architecture Search technology. |
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DeciDiffusion generates Stable Diffusion-caliber images 3x faster. |
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## **[Infery-LLM](https://deci.ai/infery-llm-book-a-demo/?utm_campaign=repos&utm_source=hugging-face&utm_medium=org-card):** |
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The most advanced inference SDK for LLM optimization and deployment, Infery-LLM includes unique features such as optimized kernels, continuous batching, advanced selective quantization, ultra-efficient beam search, parallel execution, and more. |
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## **[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md):** |
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An object detection foundational model generated by Deci's |
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Neural Architecture Search technology. YOLO-NAS is a game-changer in the |
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world of object detection, providing superior real-time object detection |
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capabilities and production-ready performance. |
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## **[SuperGradients](https://github.com/Deci-AI/super-gradients):** |
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An open-source library for training PyTorch-based computer vision |
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models. Developed by Deci's deep learning experts for the benefit of the |
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AI community, SuperGradients boosts performance with advanced |
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techniques and enables users to easily train or fine-tune SOTA computer vision models for |
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all main tasks with one training library. |
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## **[DataGradients](https://github.com/Deci-AI/data-gradients):** |
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DataGradients is an open-source, Python-based library specifically |
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designed for computer vision dataset analysis. It automatically extracts |
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features from your datasets and combines them all into a single |
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user-friendly report. |
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## **[Deci Deep Learning Platform](http://www.deci.ai/?utm_campaign=repos&utm_source=hugging-face&utm_medium=org-card-link):** |
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Simplify and accelerate the development of computer vision, NLP, and |
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Generative AI applications with highly accurate and efficient foundation |
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models. Utilize advanced tools to customize, optimize, and deploy deep learning models to |
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production. |
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Deci is powered by a groundbreaking Automated Neural Architecture |
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Construction (AutoNAC™) technology. Deci's AutoNAC™ engine democratizes |
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the use of Neural Architecture Search for every organization and helps |
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teams quickly generate fast, accurate, and efficient deep learning |
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models. |
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