Share and discover more about AI with social posts from the community.huggingface/OpenAi
Nyströmformer: Approximating self-attention in linear time and memory via the Nyström method
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Introduction
Transformers have exhibited remarkable performance on various Natural Language Processing and Computer Vision tasks. Their success can be attributed to the self-attention mechanism, which captures the pairwise interactions between all the tokens in an input. However, the standard self-attention mechanism has a time and memory complexity of \(O(n^2)\) (where \(n\) is the length of the input sequence), making it expensive to train on long input sequences.
The Nyströmformer is one of many efficient Transformer models that approximates standard self-attention with \(O(n)\) complexity. Nyströmformer exhibits competitive performance on various downstream NLP and CV tasks while improving upon the efficiency of standard self-attention. The aim of this blog post is to give readers an overview of the Nyström method and how it can be adapted to approximate self-attention.https://github.com/huggingface/blog/blob/main/nystromformer.md
<script async defer src="https://unpkg.com/medium-zoom-element@0/dist/medium-zoom-element.min.js"></script>
Introduction
Transformers have exhibited remarkable performance on various Natural Language Processing and Computer Vision tasks. Their success can be attributed to the self-attention mechanism, which captures the pairwise interactions between all the tokens in an input. However, the standard self-attention mechanism has a time and memory complexity of \(O(n^2)\) (where \(n\) is the length of the input sequence), making it expensive to train on long input sequences.
The Nyströmformer is one of many efficient Transformer models that approximates standard self-attention with \(O(n)\) complexity. Nyströmformer exhibits competitive performance on various downstream NLP and CV tasks while improving upon the efficiency of standard self-attention. The aim of this blog post is to give readers an overview of the Nyström method and how it can be adapted to approximate self-attention.https://github.com/huggingface/blog/blob/main/nystromformer.md
GitHub
blog/nystromformer.md at main · huggingface/blog
Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.
TGI Multi-LoRA: Deploy once and serve 30 models
Are you tired of the complexity and high costs of managing multiple AI models? So what if you could deploy once and have 30 model inference services? In today’s ML world, organizations looking to unlock the full value of their data may end up in a “fine-tuned world.” In this world, organizations build a large number of models, each highly specialized for a specific task. But how do you deal with the hassle and cost of deploying models for each niche application? Multi-LoRa services offer a potential answer.
Are you tired of the complexity and high costs of managing multiple AI models? So what if you could deploy once and have 30 model inference services? In today’s ML world, organizations looking to unlock the full value of their data may end up in a “fine-tuned world.” In this world, organizations build a large number of models, each highly specialized for a specific task. But how do you deal with the hassle and cost of deploying models for each niche application? Multi-LoRa services offer a potential answer.
Open LLM Leaderboard: DROP deep dive
Recently, three new benchmarks were added to the Open LLM Leaderboard: Winogrande, GSM8k and DROP, using the original implementations reproduced in the EleutherAI Harness. A cursory look at the scores for DROP revealed something strange was going on, with the overwhelming majority of models scoring less than 10 out of 100 on their f1-score! We did a deep dive to understand what was going on, come with us to see what we found out!
Initial observations
DROP (Discrete Reasoning Over Paragraphs) is an evaluation where models must extract relevant information from English-text paragraphs before executing discrete reasoning steps on them (for example, sorting or counting items to arrive at the correct answer, see the table below for examples). The metrics used are custom f1 and exact match scores.
Recently, three new benchmarks were added to the Open LLM Leaderboard: Winogrande, GSM8k and DROP, using the original implementations reproduced in the EleutherAI Harness. A cursory look at the scores for DROP revealed something strange was going on, with the overwhelming majority of models scoring less than 10 out of 100 on their f1-score! We did a deep dive to understand what was going on, come with us to see what we found out!
Initial observations
DROP (Discrete Reasoning Over Paragraphs) is an evaluation where models must extract relevant information from English-text paragraphs before executing discrete reasoning steps on them (for example, sorting or counting items to arrive at the correct answer, see the table below for examples). The metrics used are custom f1 and exact match scores.
What's going on with the Open LLM Leaderboard?
Recently an interesting discussion arose on Twitter following the release of Falcon 🦅 and its addition to the Open LLM Leaderboard, a public leaderboard comparing open access large language models.
The discussion centered around one of the four evaluations displayed on the leaderboard: a benchmark for measuring Massive Multitask Language Understanding (shortname: MMLU).
The community was surprised that MMLU evaluation numbers of the current top model on the leaderboard, the LLaMA model 🦙, were significantly lower than the numbers in the published LLaMa paper.
So we decided to dive in a rabbit hole to understand what was going on and how to fix it 🕳🐇
In our quest, we discussed with both the great @javier-m who collaborated on the evaluations of LLaMA and the amazing @slippylolo from the Falcon team. This being said, all the errors in the below should be attributed to us rather than them of course!
Along this journey with us you’ll learn a lot about the ways you can evaluate a model on a single evaluation and whether or not to believe the numbers you see online and in papers.
Ready? Then buckle up, we’re taking off 🚀.
Recently an interesting discussion arose on Twitter following the release of Falcon 🦅 and its addition to the Open LLM Leaderboard, a public leaderboard comparing open access large language models.
The discussion centered around one of the four evaluations displayed on the leaderboard: a benchmark for measuring Massive Multitask Language Understanding (shortname: MMLU).
The community was surprised that MMLU evaluation numbers of the current top model on the leaderboard, the LLaMA model 🦙, were significantly lower than the numbers in the published LLaMa paper.
So we decided to dive in a rabbit hole to understand what was going on and how to fix it 🕳🐇
In our quest, we discussed with both the great @javier-m who collaborated on the evaluations of LLaMA and the amazing @slippylolo from the Falcon team. This being said, all the errors in the below should be attributed to us rather than them of course!
Along this journey with us you’ll learn a lot about the ways you can evaluate a model on a single evaluation and whether or not to believe the numbers you see online and in papers.
Ready? Then buckle up, we’re taking off 🚀.
Can foundation models label data like humans?
Since the advent of ChatGPT, we have seen unprecedented growth in the development of Large Language Models (LLMs), and particularly chatty models that are fine-tuned to follow instructions given in the form of prompts. However, how these models compare is unclear due to the lack of benchmarks designed to test their performance rigorously. Evaluating instruction and chatty models is intrinsically difficult because a large part of user preference is centered around qualitative style while in the past NLP evaluation was far more defined.
In this line, it’s a common story that a new large language model (LLM) is released to the tune of “our model is preferred to ChatGPT N% of the time,” and what is omitted from that sentence is that the model is preferred in some type of GPT-4-based evaluation scheme. What these points are trying to show is a proxy for a different measurement: scores provided by human labelers.
Since the advent of ChatGPT, we have seen unprecedented growth in the development of Large Language Models (LLMs), and particularly chatty models that are fine-tuned to follow instructions given in the form of prompts. However, how these models compare is unclear due to the lack of benchmarks designed to test their performance rigorously. Evaluating instruction and chatty models is intrinsically difficult because a large part of user preference is centered around qualitative style while in the past NLP evaluation was far more defined.
In this line, it’s a common story that a new large language model (LLM) is released to the tune of “our model is preferred to ChatGPT N% of the time,” and what is omitted from that sentence is that the model is preferred in some type of GPT-4-based evaluation scheme. What these points are trying to show is a proxy for a different measurement: scores provided by human labelers.
Accelerate your models with 🤗 Optimum Intel and OpenVINO
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Last July, we announced that Intel and Hugging Face would collaborate on building state-of-the-art yet simple hardware acceleration tools for Transformer models.Today, we are very happy to announce that we added Intel OpenVINO to Optimum Intel. You can now easily perform inference with OpenVINO Runtime on a variety of Intel processors (see the full list of supported devices) using Transformers models which can be hosted either on the Hugging Face hub or locally. You can also quantize your model with the OpenVINO Neural Network Compression Framework (NNCF), and reduce its size and prediction latency in near minutes.
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Last July, we announced that Intel and Hugging Face would collaborate on building state-of-the-art yet simple hardware acceleration tools for Transformer models.Today, we are very happy to announce that we added Intel OpenVINO to Optimum Intel. You can now easily perform inference with OpenVINO Runtime on a variety of Intel processors (see the full list of supported devices) using Transformers models which can be hosted either on the Hugging Face hub or locally. You can also quantize your model with the OpenVINO Neural Network Compression Framework (NNCF), and reduce its size and prediction latency in near minutes.
Opinion Classification with Kili and HuggingFace AutoTrain
Introduction
Understanding your users’ needs is crucial in any user-related business. But it also requires a lot of hard work and analysis, which is quite expensive. Why not leverage Machine Learning then? With much less coding by using Auto ML.
In this article, we will leverage HuggingFace AutoTrain and Kili to build an active learning pipeline for text classification. Kili is a platform that empowers a data-centric approach to Machine Learning through quality training data creation. It provides collaborative data annotation tools and APIs that enable quick iterations between reliable dataset building and model training. Active learning is a process in which you add labeled data to the data set and then retrain a model iteratively. Therefore, it is endless and requires humans to label the data.
As a concrete example use case for this article, we will build our pipeline by using user reviews of Medium from the Google Play Store. After that, we are going to categorize the reviews with the pipeline we built. Finally, we will apply sentiment analysis to the classified reviews. Then we will analyze the results, understanding the users’ needs and satisfaction will be much easier.https://github.com/huggingface/blog/blob/main/opinion-classification-with-kili.md
Introduction
Understanding your users’ needs is crucial in any user-related business. But it also requires a lot of hard work and analysis, which is quite expensive. Why not leverage Machine Learning then? With much less coding by using Auto ML.
In this article, we will leverage HuggingFace AutoTrain and Kili to build an active learning pipeline for text classification. Kili is a platform that empowers a data-centric approach to Machine Learning through quality training data creation. It provides collaborative data annotation tools and APIs that enable quick iterations between reliable dataset building and model training. Active learning is a process in which you add labeled data to the data set and then retrain a model iteratively. Therefore, it is endless and requires humans to label the data.
As a concrete example use case for this article, we will build our pipeline by using user reviews of Medium from the Google Play Store. After that, we are going to categorize the reviews with the pipeline we built. Finally, we will apply sentiment analysis to the classified reviews. Then we will analyze the results, understanding the users’ needs and satisfaction will be much easier.https://github.com/huggingface/blog/blob/main/opinion-classification-with-kili.md
GitHub
blog/opinion-classification-with-kili.md at main · huggingface/blog
Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.
Optimizing your LLM in production
Open In Colab
Note: This blog post is also available as a documentation page on Transformers.
Large Language Models (LLMs) such as GPT3/4, Falcon, and LLama are rapidly advancing in their ability to tackle human-centric tasks, establishing themselves as essential tools in modern knowledge-based industries. Deploying these models in real-world tasks remains challenging, however:
To exhibit near-human text understanding and generation capabilities, LLMs currently require to be composed of billions of parameters (see Kaplan et al, Wei et. al). This consequently amplifies the memory demands for inference.
In many real-world tasks, LLMs need to be given extensive contextual information. This necessitates the model's capability to manage very long input sequences during inference.
The crux of these challenges lies in augmenting the computational and memory capabilities of LLMs, especially when handling expansive input sequences.https://github.com/huggingface/blog/blob/main/optimize-llm.md
Open In Colab
Note: This blog post is also available as a documentation page on Transformers.
Large Language Models (LLMs) such as GPT3/4, Falcon, and LLama are rapidly advancing in their ability to tackle human-centric tasks, establishing themselves as essential tools in modern knowledge-based industries. Deploying these models in real-world tasks remains challenging, however:
To exhibit near-human text understanding and generation capabilities, LLMs currently require to be composed of billions of parameters (see Kaplan et al, Wei et. al). This consequently amplifies the memory demands for inference.
In many real-world tasks, LLMs need to be given extensive contextual information. This necessitates the model's capability to manage very long input sequences during inference.
The crux of these challenges lies in augmenting the computational and memory capabilities of LLMs, especially when handling expansive input sequences.https://github.com/huggingface/blog/blob/main/optimize-llm.md
GitHub
blog/optimize-llm.md at main · huggingface/blog
Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.
Optimizing a Text-To-Speech model using 🤗 Transformers
🤗 Transformers provides many of the latest state-of-the-art (SoTA) models across domains and tasks. To get the best performance from these models, they need to be optimized for inference speed and memory usage.
The 🤗 Hugging Face ecosystem offers precisely such ready & easy to use optimization tools that can be applied across the board to all the models in the library. This makes it easy to reduce memory footprint and improve inference with just a few extra lines of code.
In this hands-on tutorial, I'll demonstrate how you can optimize Bark, a Text-To-Speech (TTS) model supported by 🤗 Transformers, based on three simple optimizations. These optimizations rely solely on the Transformers, Optimum and Accelerate libraries from the 🤗 ecosystem.
This tutorial is also a demonstration of how one can benchmark a non-optimized model and its varying optimizations.
For a more streamlined version of the tutorial with fewer explanations but all the code, see the accompanying Google Colab.
This blog post is organized as follows:https://github.com/huggingface/blog/blob/main/optimizing-bark.md
🤗 Transformers provides many of the latest state-of-the-art (SoTA) models across domains and tasks. To get the best performance from these models, they need to be optimized for inference speed and memory usage.
The 🤗 Hugging Face ecosystem offers precisely such ready & easy to use optimization tools that can be applied across the board to all the models in the library. This makes it easy to reduce memory footprint and improve inference with just a few extra lines of code.
In this hands-on tutorial, I'll demonstrate how you can optimize Bark, a Text-To-Speech (TTS) model supported by 🤗 Transformers, based on three simple optimizations. These optimizations rely solely on the Transformers, Optimum and Accelerate libraries from the 🤗 ecosystem.
This tutorial is also a demonstration of how one can benchmark a non-optimized model and its varying optimizations.
For a more streamlined version of the tutorial with fewer explanations but all the code, see the accompanying Google Colab.
This blog post is organized as follows:https://github.com/huggingface/blog/blob/main/optimizing-bark.md
GitHub
blog/optimizing-bark.md at main · huggingface/blog
Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.
Accelerated Inference with Optimum and Transformers Pipelines
Inference has landed in Optimum with support for Hugging Face Transformers pipelines, including text-generation using ONNX Runtime.
The adoption of BERT and Transformers continues to grow. Transformer-based models are now not only achieving state-of-the-art performance in Natural Language Processing but also for Computer Vision, Speech, and Time-Series. 💬 🖼 🎤 ⏳
Companies are now moving from the experimentation and research phase to the production phase in order to use Transformer models for large-scale workloads. But by default BERT and its friends are relatively slow, big, and complex models compared to traditional Machine Learning algorithms.
To solve this challenge, we created Optimum – an extension of Hugging Face Transformers to accelerate the training and inference of Transformer models like BERT.https://github.com/huggingface/blog/blob/main/optimum-inference.md
Inference has landed in Optimum with support for Hugging Face Transformers pipelines, including text-generation using ONNX Runtime.
The adoption of BERT and Transformers continues to grow. Transformer-based models are now not only achieving state-of-the-art performance in Natural Language Processing but also for Computer Vision, Speech, and Time-Series. 💬 🖼 🎤 ⏳
Companies are now moving from the experimentation and research phase to the production phase in order to use Transformer models for large-scale workloads. But by default BERT and its friends are relatively slow, big, and complex models compared to traditional Machine Learning algorithms.
To solve this challenge, we created Optimum – an extension of Hugging Face Transformers to accelerate the training and inference of Transformer models like BERT.https://github.com/huggingface/blog/blob/main/optimum-inference.md
GitHub
blog/optimum-inference.md at main · huggingface/blog
Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.
Optimum-NVIDIA on Hugging Face enables blazingly fast LLM inference in just 1 line of code
Large Language Models (LLMs) have revolutionized natural language processing and are increasingly deployed to solve complex problems at scale. Achieving optimal performance with these models is notoriously challenging due to their unique and intense computational demands. Optimized performance of LLMs is incredibly valuable for end users looking for a snappy and responsive experience, as well as for scaled deployments where improved throughput translates to dollars saved.
That's where the Optimum-NVIDIA inference library comes in. Available on Hugging Face, Optimum-NVIDIA dramatically accelerates LLM inference on the NVIDIA platform through an extremely simple API. By changing just a single line of code, you can unlock up to 28x faster inference and 1,200 tokens/second on the NVIDIA platform.
Optimum-NVIDIA is the first Hugging Face inference library to benefit from the new float8 format supported on the NVIDIA Ada Lovelace and Hopper architectures. FP8, in addition to the advanced compilation capabilities of NVIDIA TensorRT-LLM software software, dramatically accelerates LLM inference.https://github.com/huggingface/blog/blob/main/optimum-nvidia.md
Large Language Models (LLMs) have revolutionized natural language processing and are increasingly deployed to solve complex problems at scale. Achieving optimal performance with these models is notoriously challenging due to their unique and intense computational demands. Optimized performance of LLMs is incredibly valuable for end users looking for a snappy and responsive experience, as well as for scaled deployments where improved throughput translates to dollars saved.
That's where the Optimum-NVIDIA inference library comes in. Available on Hugging Face, Optimum-NVIDIA dramatically accelerates LLM inference on the NVIDIA platform through an extremely simple API. By changing just a single line of code, you can unlock up to 28x faster inference and 1,200 tokens/second on the NVIDIA platform.
Optimum-NVIDIA is the first Hugging Face inference library to benefit from the new float8 format supported on the NVIDIA Ada Lovelace and Hopper architectures. FP8, in addition to the advanced compilation capabilities of NVIDIA TensorRT-LLM software software, dramatically accelerates LLM inference.https://github.com/huggingface/blog/blob/main/optimum-nvidia.md
GitHub
blog/optimum-nvidia.md at main · huggingface/blog
Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.
Optimum + ONNX Runtime: Easier, Faster training for your Hugging Face models
Introduction
Transformer based models in language, vision and speech are getting larger to support complex multi-modal use cases for the end customer. Increasing model sizes directly impact the resources needed to train these models and scale them as the size increases. Hugging Face and Microsoft’s ONNX Runtime teams are working together to build advancements in finetuning large Language, Speech and Vision models. Hugging Face’s Optimum library, through its integration with ONNX Runtime for training, provides an open solution to improve training times by 35% or more for many popular Hugging Face models. We present details of both Hugging Face Optimum and the ONNX Runtime Training ecosystem, with performance numbers highlighting the benefits of using the Optimum library.https://github.com/huggingface/blog/blob/main/optimum-onnxruntime-training.md
Introduction
Transformer based models in language, vision and speech are getting larger to support complex multi-modal use cases for the end customer. Increasing model sizes directly impact the resources needed to train these models and scale them as the size increases. Hugging Face and Microsoft’s ONNX Runtime teams are working together to build advancements in finetuning large Language, Speech and Vision models. Hugging Face’s Optimum library, through its integration with ONNX Runtime for training, provides an open solution to improve training times by 35% or more for many popular Hugging Face models. We present details of both Hugging Face Optimum and the ONNX Runtime Training ecosystem, with performance numbers highlighting the benefits of using the Optimum library.https://github.com/huggingface/blog/blob/main/optimum-onnxruntime-training.md
GitHub
blog/optimum-onnxruntime-training.md at main · huggingface/blog
Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.
Accelerating over 130,000 Hugging Face models with ONNX Runtime
What is ONNX Runtime?
ONNX Runtime is a cross-platform machine learning tool that can be used to accelerate a wide variety of models, particularly those with ONNX support.https://github.com/huggingface/blog/blob/main/ort-accelerating-hf-models.md
What is ONNX Runtime?
ONNX Runtime is a cross-platform machine learning tool that can be used to accelerate a wide variety of models, particularly those with ONNX support.https://github.com/huggingface/blog/blob/main/ort-accelerating-hf-models.md
GitHub
blog/ort-accelerating-hf-models.md at main · huggingface/blog
Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.
Open-Source Text Generation & LLM Ecosystem at Hugging Face
[Updated on July 24, 2023: Added Llama 2.]
Text generation and conversational technologies have been around for ages. Earlier challenges in working with these technologies were controlling both the coherence and diversity of the text through inference parameters and discriminative biases. More coherent outputs were less creative and closer to the original training data and sounded less human. Recent developments overcame these challenges, and user-friendly UIs enabled everyone to try these models out. Services like ChatGPT have recently put the spotlight on powerful models like GPT-4 and caused an explosion of open-source alternatives like Llama to go mainstream. We think these technologies will be around for a long time and become more and more integrated into everyday products.
This post is divided into the following sections:
Brief background on text generation
Licensing
Tools in the Hugging Face Ecosystem for LLM Serving
Parameter Efficient Fine Tuning (PEFT)https://github.com/huggingface/blog/blob/main/os-llms.md
[Updated on July 24, 2023: Added Llama 2.]
Text generation and conversational technologies have been around for ages. Earlier challenges in working with these technologies were controlling both the coherence and diversity of the text through inference parameters and discriminative biases. More coherent outputs were less creative and closer to the original training data and sounded less human. Recent developments overcame these challenges, and user-friendly UIs enabled everyone to try these models out. Services like ChatGPT have recently put the spotlight on powerful models like GPT-4 and caused an explosion of open-source alternatives like Llama to go mainstream. We think these technologies will be around for a long time and become more and more integrated into everyday products.
This post is divided into the following sections:
Brief background on text generation
Licensing
Tools in the Hugging Face Ecosystem for LLM Serving
Parameter Efficient Fine Tuning (PEFT)https://github.com/huggingface/blog/blob/main/os-llms.md
GitHub
blog/os-llms.md at main · huggingface/blog
Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.
Overview of natively supported quantization schemes in 🤗 Transformers
We aim to give a clear overview of the pros and cons of each quantization scheme supported in transformers to help you decide which one you should go for.
Currently, quantizing models are used for two main purposes:
Running inference of a large model on a smaller device
Fine-tune adapters on top of quantized models
So far, two integration efforts have been made and are natively supported in transformers : bitsandbytes and auto-gptq. Note that some additional quantization schemes are also supported in the 🤗 optimum library, but this is out of scope for this blogpost.
To learn more about each of the supported schemes, please have a look at one of the resources shared below. Please also have a look at the appropriate sections of the documentation.
Note also that the details shared below are only valid for PyTorch models, this is currently out of scope for Tensorflow and Flax/JAX models.
We aim to give a clear overview of the pros and cons of each quantization scheme supported in transformers to help you decide which one you should go for.
Currently, quantizing models are used for two main purposes:
Running inference of a large model on a smaller device
Fine-tune adapters on top of quantized models
So far, two integration efforts have been made and are natively supported in transformers : bitsandbytes and auto-gptq. Note that some additional quantization schemes are also supported in the 🤗 optimum library, but this is out of scope for this blogpost.
To learn more about each of the supported schemes, please have a look at one of the resources shared below. Please also have a look at the appropriate sections of the documentation.
Note also that the details shared below are only valid for PyTorch models, this is currently out of scope for Tensorflow and Flax/JAX models.
Creating Privacy Preserving AI with Substra
With the recent rise of generative techniques, machine learning is at an incredibly exciting point in its history. The models powering this rise require even more data to produce impactful results, and thus it’s becoming increasingly important to explore new methods of ethically gathering data while ensuring that data privacy and security remain a top priority.
In many domains that deal with sensitive information, such as healthcare, there often isn’t enough high quality data accessible to train these data-hungry models. Datasets are siloed in different academic centers and medical institutions and are difficult to share openly due to privacy concerns about patient and proprietary information. Regulations that protect patient data such as HIPAA are essential to safeguard individuals’ private health information, but they can limit the progress of machine learning research as data scientists can’t access the volume of data required to effectively train their models. Technologies that work alongside existing regulations by proactively protecting patient data will be crucial to unlocking these silos and accelerating the pace of machine learning research and deployment in these domains.
This is where Federated Learning comes in. Check out the space we’ve created with Substra to learn more!https://github.com/huggingface/blog/blob/main/owkin-substra.md
With the recent rise of generative techniques, machine learning is at an incredibly exciting point in its history. The models powering this rise require even more data to produce impactful results, and thus it’s becoming increasingly important to explore new methods of ethically gathering data while ensuring that data privacy and security remain a top priority.
In many domains that deal with sensitive information, such as healthcare, there often isn’t enough high quality data accessible to train these data-hungry models. Datasets are siloed in different academic centers and medical institutions and are difficult to share openly due to privacy concerns about patient and proprietary information. Regulations that protect patient data such as HIPAA are essential to safeguard individuals’ private health information, but they can limit the progress of machine learning research as data scientists can’t access the volume of data required to effectively train their models. Technologies that work alongside existing regulations by proactively protecting patient data will be crucial to unlocking these silos and accelerating the pace of machine learning research and deployment in these domains.
This is where Federated Learning comes in. Check out the space we’ve created with Substra to learn more!https://github.com/huggingface/blog/blob/main/owkin-substra.md
GitHub
blog/owkin-substra.md at main · huggingface/blog
Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.
Welcome PaddlePaddle to the Hugging Face Hub
We are happy to share an open source collaboration between Hugging Face and PaddlePaddle on a shared mission to advance and democratize AI through open source!
First open sourced by Baidu in 2016, PaddlePaddle enables developers of all skill levels to adopt and implement Deep Learning at scale. As of Q4 2022, PaddlePaddle is being used by more than 5.35 million developers and 200,000 enterprises, ranking first in terms of market share among Deep Learning platforms in China. PaddlePaddle features popular open source repositories such as the Paddle Deep Learning Framework, model libraries across different modalities (e.g. PaddleOCR, PaddleDetection, PaddleNLP, PaddleSpeech), PaddleSlim for model compression, FastDeploy for model deployment and many more.
We are happy to share an open source collaboration between Hugging Face and PaddlePaddle on a shared mission to advance and democratize AI through open source!
First open sourced by Baidu in 2016, PaddlePaddle enables developers of all skill levels to adopt and implement Deep Learning at scale. As of Q4 2022, PaddlePaddle is being used by more than 5.35 million developers and 200,000 enterprises, ranking first in terms of market share among Deep Learning platforms in China. PaddlePaddle features popular open source repositories such as the Paddle Deep Learning Framework, model libraries across different modalities (e.g. PaddleOCR, PaddleDetection, PaddleNLP, PaddleSpeech), PaddleSlim for model compression, FastDeploy for model deployment and many more.
PaliGemma – Google's Cutting-Edge Open Vision Language Model
Updated on 23-05-2024: We have introduced a few changes to the transformers PaliGemma implementation around fine-tuning, which you can find in this notebook.
PaliGemma is a new family of vision language models from Google. PaliGemma can take in an image and a text and output text.
The team at Google has released three types of models: the pretrained (pt) models, the mix models, and the fine-tuned (ft) models, each with different resolutions and available in multiple precisions for convenience.
All models are released in the Hugging Face Hub model repositories with their model cards and licenses and have transformers integration.https://github.com/huggingface/blog/blob/main/paligemma.md
Updated on 23-05-2024: We have introduced a few changes to the transformers PaliGemma implementation around fine-tuning, which you can find in this notebook.
PaliGemma is a new family of vision language models from Google. PaliGemma can take in an image and a text and output text.
The team at Google has released three types of models: the pretrained (pt) models, the mix models, and the fine-tuned (ft) models, each with different resolutions and available in multiple precisions for convenience.
All models are released in the Hugging Face Hub model repositories with their model cards and licenses and have transformers integration.https://github.com/huggingface/blog/blob/main/paligemma.md
GitHub
blog/paligemma.md at main · huggingface/blog
Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.
Panel on Hugging Face
We are thrilled to announce the collaboration between Panel and Hugging Face! 🎉 We have integrated a Panel template in Hugging Face Spaces to help you get started building Panel apps and deploy them on Hugging Face effortlessly.
What does Panel offer?
Panel is an open-source Python library that lets you easily build powerful tools, dashboards and complex applications entirely in Python. It has a batteries-included philosophy, putting the PyData ecosystem, powerful data tables and much more at your fingertips. High-level reactive APIs and lower-level callback based APIs ensure you can quickly build exploratory applications, but you aren’t limited if you build complex, multi-page apps with rich interactivity. Panel is a member of the HoloViz ecosystem, your gateway into a connected ecosystem of data exploration tools. Panel, like the other HoloViz tools, is a NumFocus-sponsored project, with support from Anaconda and Blackstone.https://github.com/huggingface/blog/blob/main/panel-on-hugging-face.md
We are thrilled to announce the collaboration between Panel and Hugging Face! 🎉 We have integrated a Panel template in Hugging Face Spaces to help you get started building Panel apps and deploy them on Hugging Face effortlessly.
What does Panel offer?
Panel is an open-source Python library that lets you easily build powerful tools, dashboards and complex applications entirely in Python. It has a batteries-included philosophy, putting the PyData ecosystem, powerful data tables and much more at your fingertips. High-level reactive APIs and lower-level callback based APIs ensure you can quickly build exploratory applications, but you aren’t limited if you build complex, multi-page apps with rich interactivity. Panel is a member of the HoloViz ecosystem, your gateway into a connected ecosystem of data exploration tools. Panel, like the other HoloViz tools, is a NumFocus-sponsored project, with support from Anaconda and Blackstone.https://github.com/huggingface/blog/blob/main/panel-on-hugging-face.md
GitHub
blog/panel-on-hugging-face.md at main · huggingface/blog
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PatchTSMixer in HuggingFace - Getting Started
<script async defer src="https://unpkg.com/medium-zoom-element@0/dist/medium-zoom-element.min.js"></script> Open In Colab
PatchTSMixer is a lightweight time-series modeling approach based on the MLP-Mixer architecture. It is proposed in TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting by IBM Research authors Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong and Jayant Kalagnanam.
For effective mindshare and to promote open-sourcing - IBM Research joins hands with the HuggingFace team to release this model in the Transformers library.
In the Hugging Face implementation, we provide PatchTSMixer’s capabilities to effortlessly facilitate lightweight mixing across patches, channels, and hidden features for effective multivariate time-series modeling. It also supports various attention mechanisms starting from simple gated attention to more complex self-attention blocks that can be customized accordingly.
<script async defer src="https://unpkg.com/medium-zoom-element@0/dist/medium-zoom-element.min.js"></script> Open In Colab
PatchTSMixer is a lightweight time-series modeling approach based on the MLP-Mixer architecture. It is proposed in TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting by IBM Research authors Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong and Jayant Kalagnanam.
For effective mindshare and to promote open-sourcing - IBM Research joins hands with the HuggingFace team to release this model in the Transformers library.
In the Hugging Face implementation, we provide PatchTSMixer’s capabilities to effortlessly facilitate lightweight mixing across patches, channels, and hidden features for effective multivariate time-series modeling. It also supports various attention mechanisms starting from simple gated attention to more complex self-attention blocks that can be customized accordingly.