Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA
LLMs are known to be large, and running or training them in consumer hardware is a huge challenge for users and accessibility. Our LLM.int8 blogpost showed how the techniques in the LLM.int8 paper were integrated in transformers using the bitsandbytes library. As we strive to make models even more accessible to anyone, we decided to collaborate with bitsandbytes again to allow users to run models
A Gentle Introduction to 8-bit Matrix Multiplication for transformers at scale using transformers, accelerate and bitsandbytes
The 3 models are BLOOM-176B, T5-11B and T5-3B. Hugging Face transformers integration nuances Next let's discuss the specifics of the Hugging Face transformers integration. Let's look at the usage and the common culprit you may encounter while trying to set things up. Usage The module responsible for the whole magic described in this blog post is called Linear8bitLt and you can easily import it fro
Getting started with Pytorch 2.0 and Hugging Face Transformers
Getting started with Pytorch 2.0 and Hugging Face Transformers On December 2, 2022, the PyTorch Team announced PyTorch 2.0 at the PyTorch Conference, focused on better performance, being faster, more pythonic, and staying as dynamic as before. This blog post explains how to get started with PyTorch 2.0 and Hugging Face Transformers today. It will cover how to fine-tune a BERT model for Text Classi
Introduction to Graph Machine Learning
In this blog post, we cover the basics of graph machine learning. We first study what graphs are, why they are used, and how best to represent them. We then cover briefly how people learn on graphs, from pre-neural methods (exploring graph features at the same time) to what are commonly called Graph Neural Networks. Lastly, we peek into the world of Transformers for graphs. Graphs What is a graph?
AI開発に革命を起こすプラットフォームHugging Faceを生んだ起業家（Forbes JAPAN） - Yahoo!ニュース
今から5年前にリリースされたアプリの「ハギングフェイス（Hugging Face）」は、一部のティーンエイジャーの間で人気を博したが、ほとんどお金にはならなかったという。しかし、2018年に創設者たちが、コードの断片をオンラインで無料公開すると、グーグルやマイクロソフトなどの研究者たちが、人工知能（AI）アプリの開発にそのリソースを使うようになった。 ハギング・フェイスのアプリは、もはやアップストアには存在しないが、同社のプラットフォームは機械学習モデルの中心的存在となり、1万以上の組織がAI搭載ツールの作成に用いている。 フォーブスが、AI分野の優れた企業50社を選出する「AI 50」リストの2022年版に初登場したハギング・フェイスは5月19日、評価額20億ドルで1億ドルの資金調達を行ったことを明らかにした。ブルックリンを拠点とする同社のシリーズCラウンドは、Lux Capitalが主
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GitHub - huggingface/nanotron: Minimalistic large language model 3D-parallelism training
GitHub - shunk031/cookiecutter-huggingface-datasets: cookiecutter for huggingface datasets
GitHub - huggingface/text-generation-inference: Large Language Model Text Generation Inference
GitHub - lich99/ChatGLM-finetune-LoRA: Code for fintune ChatGLM-6b using low-rank adaptation (LoRA)
GitHub - huggingface/peft: 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
From PyTorch DDP to Accelerate to Trainer, mastery of distributed training with ease
DeepSpeed
GitHub - jonatasgrosman/huggingsound: HuggingSound: A toolkit for speech-related tasks based on Hugging Face's tools