Text generation with Mistral
The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2.

Mistral-7B-v0.2 has the following changes compared to Mistral-7B-v0.1

32k context window (vs 8k context in v0.1)
Rope-theta = 1e6
No Sliding-Window Attention
For full details of this model please read our paper and release blog post.
https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2

huggingface.co

mistralai/Mistral-7B-Instruct-v0.2 · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Masked word completion with BERT
BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English.

Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team.
https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France

huggingface.co

google-bert/bert-base-uncased · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Hugging Face Diffusion Models Course
In this free course, you will:

👩‍🎓 Study the theory behind diffusion models
🧨 Learn how to generate images and audio with the popular 🤗 Diffusers library
🏋️‍♂️ Train your own diffusion models from scratch
📻 Fine-tune existing diffusion models on new datasets
🗺 Explore conditional generation and guidance
🧑‍🔬 Create your own custom diffusion model pipelines
Register via the signup form and then join us on Discord to get the conversations started. Instructions on how to join specific categories/channels are here.
https://github.com/huggingface/diffusion-models-class

GitHub

GitHub - huggingface/diffusion-models-class: Materials for the Hugging Face Diffusion Models Course

Materials for the Hugging Face Diffusion Models Course - huggingface/diffusion-models-class

Text-to-Image
Generates images from input text. These models can be used to generate and modify images based on text prompts.
About Text-to-Image
Use Cases
Data Generation
Businesses can generate data for their their use cases by inputting text and getting image outputs.

Immersive Conversational Chatbots
Chatbots can be made more immersive if they provide contextual images based on the input provided by the user.

Creative Ideas for Fashion Industry
Different patterns can be generated to obtain unique pieces of fashion. Text-to-image models make creations easier for designers to conceptualize their design before actually implementing it.
https://huggingface.co/tasks/text-to-image

huggingface.co

What is Text-to-Image? - Hugging Face

Learn about Text-to-Image using Machine Learning

PublicPrompts/All-In-One-Pixel-Model

Stable Diffusion model trained using dreambooth to create pixel art, in 2 styles the sprite art can be used with the trigger word "pixelsprite" the scene art can be used with the trigger word "16bitscene"

the art is not pixel perfect, but it can be fixed with pixelating tools like https://pinetools.com/pixelate-effect-image (they also have bulk pixelation)

some example generations
https://huggingface.co/PublicPrompts/All-In-One-Pixel-Model

PineTools

Pixelate effect online

Pixelate photo effect

Fine-tuned DistilRoBERTa-base for Emotion Classification
Model Description
DistilRoBERTa-base is a transformer model that performs sentiment analysis. I fine-tuned the model on transcripts from the Friends show with the goal of classifying emotions from text data, specifically dialogue from Netflix shows or movies. The model predicts 6 Ekman emotions and a neutral class. These emotions include anger, disgust, fear, joy, neutrality, sadness, and surprise.

The model is a fine-tuned version of Emotion English DistilRoBERTa-base and DistilRoBERTa-base. This model was initially trained on the following table from Emotion English DistilRoBERTa-base:
https://huggingface.co/michellejieli/emotion_text_classifier

HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition
HeBERT is a Hebrew pre-trained language model. It is based on Google's BERT architecture and it is BERT-Base config (Devlin et al. 2018).

HeBert was trained on three datasets:

A Hebrew version of OSCAR (Ortiz, 2019): ~9.8 GB of data, including 1 billion words and over 20.8 million sentences.
A Hebrew dump of Wikipedia: ~650 MB of data, including over 63 million words and 3.8 million sentences
Emotion UGC data was collected for the purpose of this study. (described below) We evaluated the model on emotion recognition and sentiment analysis, for downstream tasks.
https://huggingface.co/avichr/heBERT_sentiment_analysis

huggingface.co

avichr/heBERT_sentiment_analysis · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

DistilBERT base uncased finetuned SST-2
Model Description: This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2. This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7).

Developed by: Hugging Face
Model Type: Text Classification
Language(s): English
License: Apache-2.0
Parent Model: For more details about DistilBERT, we encourage users to check out this model card.
Resources for more information:
Model Documentation
DistilBERT paper
https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english

huggingface.co

distilbert/distilbert-base-uncased-finetuned-sst-2-english · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Facebook/fasttext-language-identification-HF HUB
fastText (Language Identification)
fastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. It was introduced in this paper. The official website can be found here.

This LID (Language IDentification) model is used to predict the language of the input text, and the hosted version (lid218e) was released as part of the NLLB project and can detect 217 languages. You can find older versions (ones that can identify 157 languages) on the official fastText website.https://huggingface.co/facebook/fasttext-language-identification

huggingface.co

facebook/fasttext-language-identification · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Rynmurdock/searchsearch
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]
https://huggingface.co/rynmurdock/searchsearch

huggingface.co

rynmurdock/searchsearch · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Whisper Medusa
Whisper is an advanced encoder-decoder model for speech transcription and translation, processing audio through encoding and decoding stages. Given its large size and slow inference speed, various optimization strategies like Faster-Whisper and Speculative Decoding have been proposed to enhance performance. Our Medusa model builds on Whisper by predicting multiple tokens per iteration, which significantly improves speed with small degradation in WER. We train and evaluate our model on the LibriSpeech dataset, demonstrating speed improvements.https://huggingface.co/datasets/openslr/librispeech_asr

huggingface.co

openslr/librispeech_asr · Datasets at Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

THUDM/CogVideoX-2b
CogVideoX is an open-source video generation model that shares the same origins as 清影. The table below provides a list of the video generation models we currently offer, along with their basic information.https://huggingface.co/THUDM/CogVideoX-2b

huggingface.co

THUDM/CogVideoX-2b · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Multimodalart/FLUX.1-merged
https://huggingface.co/spaces/multimodalart/FLUX.1-merged
accelerate
git+https://github.com/huggingface/diffusers.git
torch
transformers==4.42.4
xformers
sentencepiece

Sofalcon84/black-forest-labs-FLUX.1-dev
https://huggingface.co/spaces/sofalcon84/black-forest-labs-FLUX.1-dev

Model Cards
Introduction
Model cards are an important documentation framework for understanding, sharing, and improving machine learning models. When done well, a model card can serve as a boundary object, a single artefact that is accessible to people with different backgrounds and goals in understanding models - including developers, students, policymakers, ethicists, and those impacted by machine learning models.

Today, we launch a model card creation tool and a model card Guide Book, which details how to fill out model cards, user studies, and state of the art in ML documentation. This work, building from many other people and organizations, focuses on the inclusion of people with different backgrounds and roles. We hope it serves as a stepping stone in the path toward improved ML documentation.

In sum, today we announce the release of:

A Model Card Creator Tool, to ease card creation without needing to program, and to help teams share the work of different sections.

An updated model card template, released in the huggingface_hub library, drawing together model card work in academia and throughout the industry.

An Annotated Model Card Template, which details how to fill the card out.

A User Study on model card usage at Hugging Face.

MTEB: Massive Text Embedding Benchmark
MTEB is a massive benchmark for measuring the performance of text embedding models on diverse embedding tasks.

The 🥇 leaderboard provides a holistic view of the best text embedding models out there on a variety of tasks.

The 📝 paper gives background on the tasks and datasets in MTEB and analyzes leaderboard results!

The 💻 Github repo contains the code for benchmarking and submitting any model of your choice to the leaderboard.

TGI Multi-LoRA: Deploy Once, Serve 30 models
Are you tired of the complexity and expense of managing multiple AI models? What if you could deploy once and serve 30 models? In today's ML world, organizations looking to leverage the value of their data will likely end up in a fine-tuned world, building a multitude of models, each one highly specialized for a specific task. But how can you keep up with the hassle and cost of deploying a model for each use case? The answer is Multi-LoRA serving.

Motivation
As an organization, building a multitude of models via fine-tuning makes sense for multiple reasons.

Performance - There is compelling evidence that smaller, specialized models outperform their larger, general-purpose counterparts on the tasks that they were trained on. Predibase [5] showed that you can get better performance than GPT-4 using task-specific LoRAs with a base like mistralai/Mistral-7B-v0.1.

Adaptability - Models like Mistral or Llama are extremely versatile. You can pick one of them as your base model and build many specialized models, even when the downstream tasks are very different. Also, note that you aren't locked in as you can easily swap that base and fine-tune it with your data on another base (more on this later).

Independence - For each task that your organization cares about, different teams can work on different fine tunes, allowing for independence in data preparation, configurations, evaluation criteria, and cadence of model updates.

Privacy - Specialized models offer flexibility with training data segregation and access restrictions to different users based on data privacy requirements. Additionally, in cases where running models locally is important, a small model can be made highly capable for a specific task while keeping its size small enough to run on device.https://github.com/huggingface/blog/blob/main/multi-lora-serving.md

GitHub

blog/multi-lora-serving.md at main · huggingface/blog

Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.

Total noob’s intro to Hugging Face Transformers
Welcome to "A Total Noob’s Introduction to Hugging Face Transformers," a guide designed specifically for those looking to understand the bare basics of using open-source ML. Our goal is to demystify what Hugging Face Transformers is and how it works, not to turn you into a machine learning practitioner, but to enable better understanding of and collaboration with those who are. That being said, the best way to learn is by doing, so we'll walk through a simple worked example of running Microsoft’s Phi-2 LLM in a notebook on a Hugging Face space.

You might wonder, with the abundance of tutorials on Hugging Face already available, why create another? The answer lies in accessibility: most existing resources assume some technical background, including Python proficiency, which can prevent non-technical individuals from grasping ML fundamentals. As someone who came from the business side of AI, I recognize that the learning curve presents a barrier and wanted to offer a more approachable path for like-minded learners.

Therefore, this guide is tailored for a non-technical audience keen to better understand open-source machine learning without having to learn Python from scratch. We assume no prior knowledge and will explain concepts from the ground up to ensure clarity. If you're an engineer, you’ll find this guide a bit basic, but for beginners, it's an ideal starting point.

Let’s get stuck in… but first some context.https://github.com/huggingface/blog/blob/main/noob_intro_transformers.md

GitHub

blog/noob_intro_transformers.md at main · huggingface/blog

Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.

Jupyter X Hugging Face
We’re excited to announce improved support for Jupyter notebooks hosted on the Hugging Face Hub!

From serving as an essential learning resource to being a key tool used for model development, Jupyter notebooks have become a key component across many areas of machine learning. Notebooks' interactive and visual nature lets you get feedback quickly as you develop models, datasets, and demos. For many, their first exposure to training machine learning models is via a Jupyter notebook, and many practitioners use notebooks as a critical tool for developing and communicating their work.

Hugging Face is a collaborative Machine Learning platform in which the community has shared over 150,000 models, 25,000 datasets, and 30,000 ML apps. The Hub has model and dataset versioning tools, including model cards and client-side libraries to automate the versioning process. However, only including a model card with hyperparameters is not enough to provide the best reproducibility; this is where notebooks can help. Alongside these models, datasets, and demos, the Hub hosts over 7,000 notebooks. These notebooks often document the development process of a model or a dataset and can provide guidance and tutorials showing how others can use these resources. We’re therefore excited about our improved support for notebook hosting on the Hub.https://github.com/huggingface/blog/blob/main/notebooks-hub.md

GitHub

blog/notebooks-hub.md at main · huggingface/blog

Public repo for HF blog posts. Contribute to huggingface/blog development by creating an account on GitHub.

Introducing NPC-Playground, a 3D playground to interact with LLM-powered NPCs
Thumbnail

AI-powered NPCs (Non-Playable Characters) are one of the most important breakthroughs brought about by the use of LLMs in games.

LLMs, or Large Language Models, make it possible to design "intelligent" in-game characters that can engage in realistic conversations with the player, perform complex actions and follow instructions, dramatically enhancing the player's experience. AI-powered NPCs represent a huge advancement vs rule-based and heuristics systems.

Today, we are excited to introduce NPC-Playground, a demo created by Cubzh and Gigax where you can interact with LLM-powered NPCs and see for yourself what the future holds!

You can play with the demo directly on your browser 👉 here

In this 3D demo, you can interact with the NPCs and teach them new skills with just a few lines of Lua scripting!