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No more magic comments (#1554)
* no more magic comments * Also replace h1 by actual markdown * nit: remove extra space * Fix remaining <h1>s * handling complex h1 heading * Update README.md cc @mishig25 --------- Co-authored-by: Mishig Davaadorj <[email protected]>
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‎1b-sentence-embeddings.md

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# Train a Sentence Embedding Model with 1 Billion Training Pairs
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**Sentence embedding** is a method that maps sentences to vectors of real numbers. Ideally, these vectors would capture the semantic of a sentence and be highly generic. Such representations could then be used for many downstream applications such as clustering, text mining, or question answering.
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‎3d-assets.md

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# Practical 3D Asset Generation: A Step-by-Step Guide
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## Introduction
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‎4bit-transformers-bitsandbytes.md

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# Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA
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LLMs are known to be large, and running or training them in consumer hardware is a huge challenge for users and accessibility.
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Our [LLM.int8 blogpost](https://huggingface.co/blog/hf-bitsandbytes-integration) showed how the techniques in the [LLM.int8 paper](https://arxiv.org/abs/2208.07339) were integrated in transformers using the `bitsandbytes` library.

‎Llama2-for-non-engineers.md

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# Non-engineers guide: Train a LLaMA 2 chatbot
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‎README.md

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# Train your first Decision Transformer
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Your content here [...]
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```
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The blog_metadata and authors HTML comments are meant to mark where in the file will be inserted the following UI elements:
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When published, the Hub will insert the following UI elements right after the blogpost's main header (i.e. the line that starts with a single `#`, aka. the `<h1>`):
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- "Published on [date]"
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- "Update on GitHub" button
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- avatars of the authors that were listed in authors.
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⚠️ Please keep the blog_metadata and authors comments exactly equal to those strings otherwise they won't be replaced.
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5️⃣ Then, you can add your content. It's markdown system so if you wrote your text on notion just control shift v to copy/paste as markdown.
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6️⃣ Modify `_blog.yml` to add your blogpost.

‎accelerate-deepspeed.md

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<h1>Accelerate Large Model Training using DeepSpeed</h1>
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# Accelerate Large Model Training using DeepSpeed
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In this post we will look at how we can leverage the **[Accelerate](https://github.com/huggingface/accelerate)** library for training large models which enables users to leverage the ZeRO features of **[DeeSpeed](https://www.deepspeed.ai)**.
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‎accelerate-large-models.md

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# How 🤗 Accelerate runs very large models thanks to PyTorch
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## Load and run large models
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‎accelerate-library.md

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# Introducing 🤗 Accelerate
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## 🤗 Accelerate
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‎accelerate-transformers-with-inferentia2.md

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# Accelerating Hugging Face Transformers with AWS Inferentia2
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‎accelerated-inference.md

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<h1>How we sped up transformer inference 100x for 🤗 API customers</h1>
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# How we sped up transformer inference 100x for 🤗 API customers
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🤗 Transformers has become the default library for data scientists all around the world to explore state of the art NLP models and build new NLP features. With over 5,000 pre-trained and fine-tuned models available, in over 250 languages, it is a rich playground, easily accessible whichever framework you are working in.
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‎accelerating-pytorch.md

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# Accelerating PyTorch distributed fine-tuning with Intel technologies
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For all their amazing performance, state of the art deep learning models often take a long time to train. In order to speed up training jobs, engineering teams rely on distributed training, a divide-and-conquer technique where clustered servers each keep a copy of the model, train it on a subset of the training set, and exchange results to converge to a final model.
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‎agents-js.md

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# Introducing Agents.js: Give tools to your LLMs using JavaScript
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We have recently been working on Agents.js at [huggingface.js](https://github.com/huggingface/huggingface.js/blob/main/packages/agents/README.md). It's a new library for giving tool access to LLMs from JavaScript in either the browser or the server. It ships with a few multi-modal tools out of the box and can easily be extended with your own tools and language models.
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‎ai-comic-factory.md

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# Deploying the AI Comic Factory using the Inference API
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We recently announced [Inference for PROs](https://huggingface.co/blog/inference-pro), our new offering that makes larger models accessible to a broader audience. This opportunity opens up new possibilities for running end-user applications using Hugging Face as a platform.
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‎ai-residency.md

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# Announcing the 🤗 AI Research Residency Program 🎉 🎉 🎉
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The 🤗 Research Residency Program is a 9-month opportunity to launch or advance your career in machine learning research 🚀. The goal of the residency is to help you grow into an impactful AI researcher. Residents will work alongside Researchers from our Science Team. Together, you will pick a research problem and then develop new machine learning techniques to solve it in an open & collaborative way, with the hope of ultimately publishing your work and making it visible to a wide audience.

‎ai-webtv.md

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# Building an AI WebTV
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The AI WebTV is an experimental demo to showcase the latest advancements in automatic video and music synthesis.
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‎aivsai.md

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# Introducing ⚔️ AI vs. AI ⚔️ a deep reinforcement learning multi-agents competition system
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<div align="center">
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/128_aivsai/thumbnail.png" alt="Thumbnail">

‎ambassadors.md

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# Student Ambassador Program’s call for applications is open!
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As an open-source company democratizing machine learning, Hugging Face believes it is essential to **[teach](https://huggingface.co/blog/education)** open-source ML to people from all backgrounds worldwide. **We aim to teach machine learning to 5 million people by 2023**.
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‎annotated-diffusion.md

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# The Annotated Diffusion Model
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‎arxiv.md

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# Hugging Face Machine Learning Demos on arXiv
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We’re very excited to announce that Hugging Face has collaborated with arXiv to make papers more accessible, discoverable, and fun! Starting today, [Hugging Face Spaces](https://huggingface.co/spaces) is integrated with arXivLabs through a Demo tab that includes links to demos created by the community or the authors themselves. By going to the Demos tab of your favorite paper, you can find links to open-source demos and try them out immediately 🔥
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‎asr-chunking.md

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# Making automatic speech recognition work on large files with Wav2Vec2 in 🤗 Transformers
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```
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Tl;dr: This post explains how to use the specificities of the Connectionist

‎assisted-generation.md

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# Assisted Generation: a new direction toward low-latency text generation
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Large language models are all the rage these days, with many companies investing significant resources to scale them up and unlock new capabilities. However, as humans with ever-decreasing attention spans, we also dislike their slow response times. Latency is critical for a good user experience, and smaller models are often used despite their lower quality (e.g. in [code completion](https://ai.googleblog.com/2022/07/ml-enhanced-code-completion-improves.html)).
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‎audio-datasets.md

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# A Complete Guide to Audio Datasets
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<!--- Note to reviewer: comments and TODOs are included in this format. --->
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‎audioldm2.md

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# AudioLDM 2, but faster ⚡️
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<a target="_blank" href="https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/AudioLDM-2.ipynb">
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>

‎autoformer.md

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# Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)
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‎autonlp-prodigy.md

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# Active Learning with AutoNLP and Prodigy
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Active learning in the context of Machine Learning is a process in which you iteratively add labeled data, retrain a model and serve it to the end user. It is an endless process and requires human interaction for labeling/creating the data. In this article, we will discuss how to use [AutoNLP](https://huggingface.co/autonlp) and [Prodigy](https://prodi.gy/) to build an active learning pipeline.
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‎autotrain-image-classification.md

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# Image Classification with AutoTrain
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‎aws-marketplace.md

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# Hugging Face Platform on the AWS Marketplace: Pay with your AWS Account
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The [Hugging Face Platform](https://aws.amazon.com/marketplace/pp/prodview-n6vsyhdjkfng2) has landed on the AWS Marketplace. Starting today, you can subscribe to the Hugging Face Platform through AWS Marketplace to pay for your Hugging Face usage directly with your AWS account. This new integrated billing method makes it easy to manage payment for usage of all our managed services by all members of your organization, including Inference Endpoints, Spaces Hardware Upgrades, and AutoTrain to easily train, test and deploy the most popular machine learning models like Llama 2, StarCoder, or BERT.
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‎aws-partnership.md

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# Hugging Face and AWS partner to make AI more accessible
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It’s time to make AI open and accessible to all. That’s the goal of this expanded long-term strategic partnership between Hugging Face and Amazon Web Services (AWS). Together, the two leaders aim to accelerate the availability of next-generation machine learning models by making them more accessible to the machine learning community and helping developers achieve the highest performance at the lowest cost.
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‎bert-101.md

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# BERT 101 🤗 State Of The Art NLP Model Explained
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‎bert-cpu-scaling-part-1.md

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‎bert-cpu-scaling-part-2.md

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# Scaling up BERT-like model Inference on modern CPU - Part 2
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‎bert-inferentia-sagemaker.md

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# Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia
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‎bertopic.md

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# Introducing BERTopic Integration with the Hugging Face Hub
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[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg 'open in colab')](https://colab.research.google.com/#fileId=https://huggingface.co/spaces/davanstrien/blog_notebooks/blob/main/BERTopic_hub_starter.ipynb)
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‎big-bird.md

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# Understanding BigBird's Block Sparse Attention
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‎blip-2.md

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that enables a suite of state-of-the-art visual-language models that are now available in [🤗 Transformers](https://huggingface.co/transformers).

‎bloom-inference-optimization.md

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inference server that powers [https://huggingface.co/bigscience/bloom]().

‎bloom-inference-pytorch-scripts.md

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# Incredibly Fast BLOOM Inference with DeepSpeed and Accelerate
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This article shows how to get an incredibly fast per token throughput when generating with the 176B parameter [BLOOM model](https://huggingface.co/bigscience/bloom).
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‎bloom-megatron-deepspeed.md

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# The Technology Behind BLOOM Training
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‎bloom.md

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<a href="https://huggingface.co/bigscience/bloom"><img style="middle" width="950" src="/blog/assets/86_bloom/thumbnail-2.png"></a>
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