First Time in AI History | Apple open-sourced the Whole LLM Pipeline |
Important 🔗 Links 🔗:
Website Link : https://simplifyai.in/
OpenELM: An Efficient Language Model Family with Open-source Training
and Inference Framework
https://arxiv.org/pdf/2404.14619v1
Apple OpenELM License - https://huggingface.co/apple/OpenELM-...
OpenELM Collection on Hugging Face - https://huggingface.co/collections/ap...
OpenELM FULL (CoreNet) Training and other Scripts - https://github.com/apple/corenet/tree...
Check out our playlist:
Tech update: • 2) Tech Update | All
Local GPT: • RAG | LocalGPT
AI Application: • Playlist
Description :
In this video, we discuss the full open-source LLM (large language model) pipeline released by Apple. An open-source LLM doesn't just mean the weights are open source, but the entire pipeline is. Unlike other models where we often don't know the pre-training strategies or the pipeline used, Apple has made everything transparent with their new model called OpenELM.
Apple has released the paper, training recipes, scripts for pre-training, instruct models, and guidelines for using it within machine learning frameworks for inference. This is significant as it opens new possibilities for researchers and developers to understand and improve upon the underlying mechanisms of LLMs. Apple's approach fosters a more transparent and collaborative environment in AI development.
Initially, Apple is comparing OpenELM with other public LLMs like Pythia from Facebook, which was popular for its open release. The public dataset, code, and weights are all available. For those unfamiliar, weights are essentially what we call the model. OpenELM's model size is 1.1 billion parameters with 1.5 trillion training tokens and an average accuracy of 45.93%. Although it's not the best state-of-the-art model, the smaller models released are practical, especially for applications on iOS.
This video also explores the importance of reproducibility and transparency in large language models. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the Transformer models, leading to enhanced accuracy with fewer tokens. This makes the model development cycle more efficient by requiring less data, compute, and time.
Apple's release includes a complete framework for training and evaluating language models on publicly available datasets. They cleverly used existing datasets, promoting a culture of openness initiated by OpenAI. This approach allows for better understanding and utilization of the data composition, which is often lacking in current LLM research papers.
Moreover, Apple's CoreML framework, CoreNLP, and other tools are used in OpenELM, with detailed instructions and YAML files available for pre-training, evaluation, and tuning. The datasets used include Refined Web, Red Pajama, Dolma, and others, providing transparency in data usage and model architecture.
This video aims to educate viewers about Apple's groundbreaking move, akin to a paper review, and encourages checking out the detailed information provided in the linked resources.
Tags:
1. #AppleLLM
2. #OpenELM
3. #OpenSourceAI
4. #AIResearch
5. #MachineLearning
6. #LLM
7. #CoreML
8. #ArtificialIntelligence
9. #AIModels
10. #TechNews
Tags: Apple LLM, OpenELM, open source AI, large language model, AI research, machine learning, CoreML, AI models, tech news, artificial intelligence
Смотрите видео First Time in AI History | Apple open-sourced the Whole LLM Pipeline | онлайн без регистрации, длительностью часов минут секунд в хорошем качестве. Это видео добавил пользователь Simplify AI 20 Июль 2024, не забудьте поделиться им ссылкой с друзьями и знакомыми, на нашем сайте его посмотрели 1,398 раз и оно понравилось 19 людям.