A good introductory tutorial to do so is An Introduction to Using Transformers and Hugging Face. Fine-tuning is a concept you should be aware of as well, as most of the time, models need to be optimized for specific goals. You can learn how to fine-tune a model with the An ...
Hugging Face is a hub for state-of-the-art AI models. It’s primarily known for its wide range of open-source transformer-based models that excel in natural language processing (NLP), computer vision, and audio tasks. The platform offers several resources and services that cater to developers...
sample-factory is a codebase for high throughput asynchronous reinforcement learning. It has integrations with the Hugging Face Hub to share models with evaluation results and training metrics. Repository: https://github.com/alex-petrenko/sample-factory 安装: pip install sample-factory 示例: python...
This setup uses Data Parallelism, but it is also possible to use Model Parallelism for very large models that don't fit in one GPU. The first option consists of Tensor Parallelism that splits the execution of a single transformer module over multiple GPUs, you will need to change...
This tutorial showcases how to accelerate finetuning a full Llama 2 or Llama 3 models from Hugging Face by using TransformerLayer from the Transformer Engine library in BF16 and FP8 precisions.Dependencies for this tutorial Following files and media are necessary to effecti...
It's also compatible with Inference for PROs curated list of powerful models with higher rate limits. Make sure to create your personal token first in your User Access Tokens settings. Read the full tutorial here. Setup The default config for Chat UI is stored in the .env file. You will ...
This notebook is designed to use a pretrained transformers model and fine-tune it on a classification task. The focus of this tutorial will be on the code itself and how to adjust it to your needs. This notebook is using theAutoClassesfromtransformerbyHugging Facefunctionality. This functionali...
本章主要介绍Hugging Face下的另外一个重要库:Datasets库,用来处理数据集的一个python库。当微调一个模型时候,需要在以下三个方面使用该库,如下。 从Huggingface Hub上下载和缓冲数据集(也可以本地哟!) 使用Dataset.map()预处理数据 加载和计算指标 ...
For the 1.7B parameter model, Hugging Face used 1 trillion tokens from the SmolLM-Corpus, while the 135M and 360M parameter models were trained on 600 billion tokens. The training process employed a trapezoidal learning rate scheduler with a co...
Yi-VL-34B模型托管在Hugging Face上,是全球首个开源的340亿视觉语言模型,代表了人工智能领域的重大进展。它以其双语多模态能力脱颖而出,可以进行英文和中文的多轮文本-图像对话。该模型在图像理解方面表现出色,并在MMMU和CMMMU等基准测试中取得了顶尖性能(截至2024年1月)。它的架构包括视觉Transformer和大型语言模...