# HelpingAI 9B: Cutting Edge Emotionally Intelligent AI

If you have ever felt that AI not understand your emotions or you not get human like fell while taking to him than this blog is for you!
In this BlogPost we will be exploring [HelpingAI 9B](https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B) is an Highly Emotionally Intelligent AI which beated all top notch ai like GPT4o, GPT4, Claude3 Opus on EQ-Bench.

## What is HelpingAI 9B?
![image/png](https://cdn-uploads.huggingface.co/production/uploads/6612aedf09f16e7347dfa7e1/FrNhr3WhMhvvD-dNplHxZ.png)
HelpingAI-9B is the fine-tuned Llama2 model crafted for emotionally intelligent conversations. This model excels in empathetic engagement, offering understanding and support through dialogue spanning various topics and situations. Its goal is to serve as a supportive AI companion, adept at resonating with users' emotions and communication requirements.

## Method
We gathered a large volume of high-quality human chat data, which was then filtered and refined to create three types of datasets:
1. DPO - Initially, we trained the AI on a substantial DPO dataset to grasp human conversation patterns, enabling it to discern which types of output to generate and which to avoid.
2. Alpaca - Subsequently, we trained it on an Alpaca-type dataset to enhance its human-like responses.
3. SFT - Finally, once the AI fully comprehended human interactions, we trained it on an SFT dataset to broaden its knowledge base.

## Evaluation

![By/KingNish/Banchmark/png](https://cdn-uploads.huggingface.co/production/uploads/6612aedf09f16e7347dfa7e1/xvS57q5kU9f3AX-O-al0k.png)

## Conclusion:
HelpingAI is a large step toward understanding human emoions and give response like them. It can help us lot in making AI better for NLP.
Thanks!

Model Link: - https://huggingface.co/OEvortex/HelpingAI-9B

Demo link: - https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B

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Successfully defended my thesis yesterday 🚀

Glad that my supervisor gets the innovation behind it - “An Adaptive Virtual Intelligent Tutor that autonomously learns and adjusts to your learning preferences”

The Intuitive Approach: fine-tune a high performant pre-trained large language model on a rich task-specific dataset (in my case code instruction dataset with adaptive instructions on how to teach coding/solve coding problems with adherence to the student’s learning style)

Then apply Retrieval-Augmented Generation (RAG) during inference to update the knowledge base of the model in real-time with adaptive features learned from conversations with the model over time.

The app supports both real-time voice chat with an intelligent 3D Avatar (made with Soulmachine's Digital DNA studio) powered by the fine-tuned model and text chat with the locally hosted fine-tuned model.

With enough interactions you get an objective driven, task-specific and adaptive personalized tutor that completely gets you (knows your learning pace, your learning style and preferences).

This is what I feel is missing in today’s AI systems - Autonomously Adaptive Assistants (AAA) - and oh I’m currently writing a paper on this:)

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I'm working on talking head generation that takes audio and video as input, can someone suggest me a good existing architecture that can generate videos with less latency or can we make it in real time?


💪Build an information retrieval Agent that can beat Gemini and OpenAI using open-source Large Action Model framework!

In this video, we ask to different proprietary Conversational AI the question:
 “What is the most trendy recent paper on Llava models on Hugging Face papers? Provide the date and a summary of the paper”, and the results are interesting!
❌Gemini: found a paper from Jan 29, 2024
❌OpenAI: found a paper from October 2023
❌You.com: found a paper from Jan 29 2024
✅LaVague: found the latest paper (ConvLlaVA which is dope by the way https://arxiv.org/abs/2405.15738)!

The best? Our solution fits a few ines of code with our open-source framework! I will share how we built that agent during our webinar on AI Web Agents, this Thursday 30th May at 9 am PST (https://lu.ma/m8fzmb3q) so don’t miss it 😉

You can also start playing with our framework: https://github.com/lavague-ai/LaVague


Cohere for AI, Argilla, and Hugging Face are collaborating on an Open Science Project to enhance multilingual model evaluations. The project focuses on the widely-used MMLU dataset, which spans 57 subjects like mathematics, computer science, and law. However, existing translations often miss linguistic and cultural nuances, thus embedding biases. 🤔

To address this, they have annotated a subset of the MMLU test set and are inviting global perspectives to review prompts, highlighting cultural specifics and required knowledge. They have mentioned that insights will help shape future multilingual model evaluations, ensuring they are more inclusive and accurate. 🗺 📝 🙌

▶ To get started go to: https://huggingface.co/spaces/CohereForAI/MMLU-evaluation

🌍 They also have an Aya Discord server for collaboration with other participants: https://discord.gg/9gVhdfnQMN


I'm officially considered #gpu_poor 💀
But I'm #data_rich 😎


𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴 holds an important place in machine learning. But it has traditionally been quite difficult to go from prototype code to production-ready APIs

We're working on making that a lot easier with 𝗚𝗿𝗮𝗱𝗶𝗼 and will unveil something new on June 6th: https://www.youtube.com/watch?v=44vi31hehw4&ab_channel=HuggingFace

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everything-ai v2.0.1: more AI power on your Desktop

What is everything-ai?

🤖 everything-ai is natively a multi-tasking agent, 100% local, that is able to perform several AI-related tasks

 What's new?

🚀 I am more than thrilled to introduce some new functionalities that were added since last release:

- 🎙🔊 Handle audio files or microphone recordings, classifying or transcribing them with almost every audio-classification and automatic-speech-recognition model on Hugging Face Hub.
- 📽 Generate video from text prompts with almost every text-to-video model on HuggingFace Hub (original architecture by [Vasiliy Katsyka](https://github.com/Vasiliy-katsyka))
- 🧬 Predict the 3D structure of proteins from their amino-acidic sequence, with EsmFold by AI at Meta ([demo](https://huggingface.co/spaces/as-cle-bert/proteinviz))
- 🏋 Finetune HF models on several downstream tasks with AutoTrain local integration (AutoTrain is developed by [Abhishek Thakur](https://github.com/abhishekkrthakur))
- 🗣 Unleash powerful LLMs and exploit larger database collections for RAG with the integration of Hugging Face Spaces API and Supabase PostgreSQL databases ([demo](https://huggingface.co/spaces/as-cle-bert/supabase-ai-chat))

How can you use all of these features?

You just need a `docker compose up`!🐋

Where can I find everything I need?

Get the source code (and leave a little ⭐ while you're there):
https://github.com/AstraBert/everything-ai
Get a quick-start with the documentation:
https://astrabert.github.io/everything-ai/

Credits and inspiration

Shout-outs to Hugging Face, Gradio, Docker, AI at Meta, Abhishek Thakur, Qdrant, LangChain and Supabase for making all of this possible!
Inspired by: Jan, Cheshire Cat AI, LM Studio, Ollama and other awesome local AI solutions!

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Just released ViLaH - a compact 3B parameter vision language model! which  generates responses in Hindi only hindi for now 😔

https://huggingface.co/BhashaAI/ViLaH

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Weekly highlights for the HF ecosystem!

🚀 Phi 3
🦅 Falcon VLM
🤗 sentence-transformers v3.0 is here! Train and finetune embedding models with multi-GPU training, bf16 support, loss logging, callbacks and more!
🥳 Gradio launch event 6/6! We're launching 1.0 versions of two new libraries, Python + JS client libraries to programmatically query Gradio apps, and several new features making it easier to use Gradio apps in production!
✨ Tools now available in HuggingChat! Use any AI apps built by the community! 🔥
🧊 ML for 3D Course Unit 3 is here! Covering Gaussian splatting, how it fits in the generative 3D pipeline, and hands-on code to build your own demo!

See the full list here!
https://discord.com/channels/879548962464493619/897387888663232554/1245036889539612764 !


🧨 Diffusers 0.28.0 is out 🔥

It features the first non-generative pipeline of the library -- Marigold 🥁

Marigold shines at performing Depth Estimation and Surface Normal Estimation. It was contributed by @toshas, one of the authors of Marigold.

This release also features a massive refactor (led by @DN6) of the `from_single_file()` method, highlighting our efforts for making our library more amenable to community features 🤗

Check out the release notes here:
https://github.com/huggingface/diffusers/releases/tag/v0.28.0

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cooking up something....anyone interested in a daily activity tracker for HF?

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How can AI help us write better headlines and reach more people?

I experimented with a new approach that is both useful and fun. It can help you overcome writer’s block, find better headlines, and make your blog posts and news articles climb in search engine results. Plus, we will learn new concepts along the way!

1⃣ First, I scraped all the blog posts written on Hugging Face to create a dataset with the headlines, texts, dates, and authors' names.

2⃣ I filtered the dataset to remove posts that were too long and would require a model with a longer context window. This was done to keep the project simple and cost-effective (actually, free).

3⃣ Then, I used a dataset generation workflow built by @davanstrien to generate a DPO dataset.

4⃣ As a last step, you can collectively rate these evaluations to improve the quality of the dataset using an easy-to-use interface with Argilla. Take a look at it and rate some of them! This way, you can contribute to making this dataset useful for different newsrooms that could use it as a starting point.

𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬. This example is compelling because, if you look at the dataset, you can see some examples where the headlines are enhanced by the addition of an important keyword or an action verb.
These tweaks can have a big impact on your position in search engines and, therefore, on your traffic. It’s also good leverage for our creativity since you can compare the initial idea with another one from an outside perspective.

Imagine if you’re a large news organization; you could run this experiment with thousands of news articles.

With a dataset of several hundred to thousands of entries, you could fine-tune a model to suggest headlines better tailored to your needs and writing style.

👉 Take a look at it and rate the headlines https://huggingface.co/spaces/fdaudens/journalism-argilla-space
👉 Daniel's code https://github.com/huggingface/data-is-better-together/blob/main/dpo/README.md

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Hello HF fans! Anyone is interested to become cofounder in my future company. I'm working on AI products, edge AI, marketing and social Media. Please feel free to contact me.


🙋🏻♂ Hey there folks ,

@tiiuae released Falcon 11B Vision Model !

🦅🦅👀👀

it's quite good , and you can try it here : https://huggingface.co/spaces/Tonic/Falcon-Vision


I would pick @ylecun over @elonmuskceo every single day of the week.

Despite getting much less $$, recognition & visibility than entrepreneurs, the scientists who publish their groundbreaking research openly are the cornerstone of technological progress & massively contribute to making the world a better place!

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‼Sentence Transformers v3.0 is out! You can now train and finetune embedding models with multi-GPU training, bf16 support, loss logging, callbacks & much more. I also release 50+ datasets to train on.

1⃣ Training Refactor
Embedding models can now be trained using an extensive trainer with a lot of powerful features:
- MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP))
- bf16 training support; loss logging
- Evaluation datasets + evaluation loss
- Improved callback support + an excellent Weights & Biases integration
- Gradient checkpointing, gradient accumulation
- Improved model card generation
- Resuming from a training checkpoint without performance loss
- Hyperparameter Optimization
and much more!
Read my detailed blogpost to learn about the components that make up this new training approach: https://huggingface.co/blog/train-sentence-transformers

2⃣ Similarity Score
Not sure how to compare embeddings? Don't worry, you can now use `model.similarity(embeddings1, embeddings2)` and you'll get your similarity scores immediately. Model authors can specify their desired similarity score, so you don't have to worry about it anymore!

3⃣ Additional Kwargs
Sentence Transformers relies on various Transformers instances (AutoModel, AutoTokenizer, AutoConfig), but it was hard to provide valuable keyword arguments to these (like 'torch_dtype=torch.bfloat16' to load a model a lower precision for 2x inference speedup). This is now easy!

4⃣ Hyperparameter Optimization
Sentence Transformers now ships with HPO, allowing you to effectively choose your hyperparameters for your data and task.

5⃣ Dataset Release
To help you out with finetuning models, I've released 50+ ready-to-go datasets that can be used with training or finetuning embedding models: https://huggingface.co/collections/sentence-transformers/embedding-model-datasets-6644d7a3673a511914aa7552

Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.0

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✨ Tools are now available in HuggingChat (https://hf.co/chat)

In short, Tools allow HuggingChat to plug any ZeroGPU Space as a tool HuggingChat can use, offering limitless possibilities.

For the release we plugged 6 tools that you can use right now on command-R+, we plan to expand to more models.

We'll also allow you to add your own tools (any ZeroGPU space is compatible). For more info check out this discussion: https://huggingface.co/spaces/huggingchat/chat-ui/discussions/470

Kudos to @nsarrazin @Saghen and @mishig for the release <3

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KingNish clem tomaarsen merve radames Ramikan-BR Stonedkick

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The Hugging Face Computer Vision community will have the first in a series of online hangouts/study groups this Saturday June 1st at 10:00 am EDT.🚀

Join us on the Hugging Face Discord channel for the Hangout!

https://discord.gg/hugging-face-879548962464493619?event=1243129304863215656 🤗

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I propose "merge densification", a style of merger which attempts to transfer the benefits of a denser model to a base model. The model weight in this case is 0.02, which is atypically small for mergers, but high compared to the learning rate used during training. In this case, the expectation is more creative text-generation. More details below:
https://huggingface.co/grimjim/kunoichi-lemon-royale-v3-32K-7B

osanseviero louisbrulenaudet victor SerialKicked


Remember stacking in ensemble ML? 🤔

What happens if you do the reverse of that but with LLMs? 🤯

Basically, MoE created by merging multiple models (instead of being pre-trained like Mixtral)? 🧠

Frankenstein MoE! (not an official name) 🧟♂

That's the new Kraken architecture! 🐙

It uses a sequence classification model to route inputs to the most suitable language model based on the input's characteristics. 🚦

Yup, multiple full-fledged LLMs are loaded into memory, and then a classification layer decides who gets to generate an output! 🎰

Tell me you have too many GPUs without telling me you have too many GPUs! 🖥🔥

Jokes aside, extremely fascinating research but I don't understand why this can't just be a big model with multiple LORA adapters, that can be decided on the fly? 🤷♂

Model: https://huggingface.co/cognitivecomputations/Kraken
Github: https://github.com/cognitivecomputations/kraken


If you're part of the Journalists on Hugging Face community, did you know you can receive notifications on ongoing discussions?

- "Repo discussions" for repo discussions you're participating in or mentioned in
- "New activity on watched orgs/users" for repo discussions & posts from users & orgs
you're watching

Activate them here: https://huggingface.co/settings/notifications

Join the community: If you’re part of the Journalists on Hugging Face community, did you know you can receive notifications about ongoing discussions?


I've just open sourced RAGoon, a small utility I use to integrate knowledge from the web into LLM inference based on Groq speed and pure Google search performance ⚡

RAGoon is a Python library available on PyPI that aims to improve the performance of language models by providing contextually relevant information through retrieval-based querying, parallel web scraping, and data augmentation techniques. It offers an integration of various APIs (OpenAI, Groq), enabling users to retrieve information from the web, enrich it with domain-specific knowledge, and feed it to language models for more informed responses.
```python
from groq import Groq
# from openai import OpenAI
from ragoon import RAGoon

# Initialize RAGoon instance
ragoon = RAGoon(
    google_api_key="your_google_api_key",
    google_cx="your_google_cx",
    completion_client=Groq(api_key="your_groq_api_key")
)

# Search and get results
query = "I want to do a left join in python polars"
results = ragoon.search(
    query=query,
    completion_model="Llama3-70b-8192",
)

# Print list of results
print(results)
```
For the time being, this project remains simple, but can easily be integrated into a RAG pipeline.

Link to GitHub : https://github.com/louisbrulenaudet/ragoon


InstructAvatar: Text-Guided Emotion and Motion Control for Avatar
  Generation (2405.15758)

🚀🎭🌟 New Research Alert - InstructAvatar (Avatars Collection)! 🌟🎭🚀
📄 Title: InstructAvatar: Text-Guided Emotion and Motion Control for Avatar Generation 🔝

📝 Description: InstructAvatar is a novel method for generating emotionally expressive 2D avatars using text-guided instructions, offering improved emotion control, lip-sync quality, and naturalness. It uses a two-branch diffusion-based generator to predict avatars based on both audio and text input.

👥 Authors: Yuchi Wang et al.

📄 Paper: https://huggingface.co/papers/2405.15758

🌐 Github Page: https://wangyuchi369.github.io/InstructAvatar/
📁 Repository: https://github.com/wangyuchi369/InstructAvatar

📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🚀 Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36

🔍 Keywords: #InstructAvatar #AvatarGeneration #EmotionControl #FacialMotion #LipSynchronization #NaturalLanguageInterface #DiffusionBasedGenerator #TextGuidedInstructions #2DAvatars #VideoSynthesis #Interactivity #ComputerGraphics #DeepLearning #ComputerVision #Innovation

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Celebrating 30 likes!! https://huggingface.co/spaces/nroggendorff/epicrealismxl

Alexandro14 KingNish osanseviero lunarflu


Executable Code Actions Elicit Better LLM Agents (2402.01030)

𝙒𝙧𝙞𝙩𝙞𝙣𝙜 𝙩𝙤𝙤𝙡 𝙘𝙖𝙡𝙡𝙨 𝙞𝙣 𝙘𝙤𝙙𝙚 𝙟𝙪𝙨𝙩 𝙬𝙤𝙧𝙠𝙨 𝙗𝙚𝙩𝙩𝙚𝙧 𝙩𝙝𝙖𝙣 𝙅𝙎𝙊𝙉 💪

I was really happy to learn today by @sergeipetrov  that paper 𝘌𝘹𝘦𝘤𝘶𝘵𝘢𝘣𝘭𝘦 𝘊𝘰𝘥𝘦 𝘈𝘤𝘵𝘪𝘰𝘯𝘴 𝘌𝘭𝘪𝘤𝘪𝘵 𝘉𝘦𝘵𝘵𝘦𝘳 𝘓𝘓𝘔 𝘈𝘨𝘦𝘯𝘵𝘴 was accepted at ICLR 2024!

As a reminder, an agent is a system in which you embed a LLM engine, to let it call tools.

These tools are meant like an IronMan suit, to supplement the LLM in areas that it isn't good at.
🧑💻 For instance your friendly LLM may be terrible at calculating powers of floating numbers ("What is X ^0.2947 ?"), so it should use a calculator.
🔎It may be terrible at knowing precise facts ("What was the date of the Golden Bull?") so it should use a web browser.

So the agent system will prompt an agent with "Now you can use these tools: calculator, search,..."

But 𝙝𝙤𝙬 𝙨𝙝𝙤𝙪𝙡𝙙 𝙩𝙝𝙚 𝙖𝙜𝙚𝙣𝙩 𝙚𝙭𝙥𝙧𝙚𝙨𝙨 𝙞𝙩𝙨 𝙖𝙘𝙩𝙞𝙤𝙣𝙨?

All well known frameworks let agents write their actions as JSON strings.

We 𝗽𝗿𝗲𝗳𝗲𝗿𝗿𝗲𝗱 𝘁𝗼 𝗴𝗼 𝘄𝗶𝘁𝗵 𝗳𝗼𝗿𝗺𝘂𝗹𝗮𝘁𝗶𝗻𝗴 𝗮𝗰𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗖𝗼𝗱𝗲, 𝘄𝗵𝗶𝗰𝗵 𝗶𝘀 𝗺𝘂𝗰𝗵 𝗺𝗼𝗿𝗲 𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗹𝗲 𝗮𝗻𝗱 𝗰𝗼𝗻𝗰𝗶𝘀𝗲, 𝗮𝗻𝗱 𝗮𝗹𝗹𝗼𝘄𝘀 𝘁𝗼 𝗰𝗵𝗮𝗶𝗻 𝗮𝗰𝘁𝗶𝗼𝗻𝘀 𝘀𝗲𝗮𝗺𝗹𝗲𝘀𝘀𝗹𝘆: see the picture attached for an example where Code formulation really shines.

And the paper confirms our choice: researchers show that 𝗰𝗼𝗺𝗽𝗮𝗿𝗲𝗱 𝘁𝗼 𝗝𝗦𝗢𝗡 𝗼𝗿 𝗽𝗹𝗮𝗶𝗻 𝘁𝗲𝘅𝘁, 𝗖𝗼𝗱𝗲 𝗶𝘀 𝗯𝗲𝘁𝘁𝗲𝗿 𝗯𝗼𝘁𝗵 𝗶𝗻 𝗰𝗼𝗻𝗰𝗶𝘀𝗲𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲:
➤ Up to 30% fewer steps for the same actions (much more concise)
➤ Up to 20% higher performance on benchmarks

And we find additional benefits, for instance a natural handling of variables.

Read the paper here 📖 https://huggingface.co/papers/2402.01030
Get your ReactCodeAgent running with our Agents framework! 👉 https://huggingface.co/learn/cookbook/agents


We will be providing ZeroGPU grants (for Spaces inference) to those who want to fine-tune PaliGemma and build a Space 🔥

You can pick any dataset of your choice!

Example code: https://colab.research.google.com/drive/1x_OEphRK0H97DqqxEyiMewqsTiLD_Xmi?usp=sharing (you can use a lower GPU with QLoRA)

Datasets:
https://huggingface.co/datasets?task_categories=task_categories:text-to-image&sort=trending
https://huggingface.co/datasets?task_categories=task_categories:image-to-text&sort=trending

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🇫🇷
Quel impact de l’IA sur les filières du cinéma, de l’audiovisuel et du jeu vidéo?
Etude prospective à destination des professionnels
— CNC & BearingPoint | 09/04/2024

Si l’Intelligence Artificielle (IA) est utilisée de longue date dans les secteurs du cinéma, de l’audiovisuel et du jeu vidéo, les nouvelles applications de l’IA générative bousculent notre vision de ce dont est capable une machine et possèdent un potentiel de transformation inédit. Elles impressionnent par la qualité de leurs productions et suscitent par conséquent de nombreux débats, entre attentes et appréhensions.

Le CNC a donc décider de lancer un nouvel Observatoire de l’IA Afin de mieux comprendre les usages de l’IA et ses impacts réels sur la filière de l’image. Dans le cadre de cet Observatoire, le CNC a souhaité dresser un premier état des lieux à travers la cartographie des usages actuels ou potentiels de l’IA à chaque étape du processus de création et de diffusion d’une œuvre, en identifiant les opportunités et risques associés, notamment en termes de métiers et d’emploi. Cette étude CNC / Bearing Point en a présenté les principaux enseignements le 6 mars, lors de la journée CNC « Créer, produire, diffuser à l’heure de l’intelligence artificielle ».

Le CNC publie la version augmentée de la cartographie des usages de l’IA dans les filières du cinéma, de l’audiovisuel et du jeu vidéo.

Lien vers la cartographie complète: https://www.cnc.fr/documents/36995/2097582/Cartographie+des+usages+IA_rapport+complet.pdf/96532829-747e-b85e-c74b-af313072cab7?t=1712309387891

Cos-s Ramikan-BR TheoLvs clefourrier leosok AI-FLAMMMMMMMMMMINGO menorki spel OmbelineM


Just subscribed the PRO monthly, but still got rate limited when making the inference API call

```
  const api_url = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B";
  const payload = JSON.stringify({
    "query": input,
  });

  const body = {
      "headers" : {"Authorization": `Bearer ${API_TOKEN}`},
      "wait_for_model": true,
      "use_gpu": false,
      "method" : "POST",
      "contentType" : "application/json",
      "payload" : payload
    };

  var xmlHttp = new XMLHttpRequest();
  xmlHttp.open("POST", api_url, false);
  xmlHttp.send(body);
  return xmlHttp.responseText;
```
Need some help


The zip file contains installers for Windows, RunPod, Massed Compute and a free Kaggle account notebook

It generates a VENV and install everything inside it. Works with Python 3.10.x - I suggest 3.10.11

Also you need C++ tools and Git. You can follow this tutorial to install all : https://youtu.be/-NjNy7afOQ0

Updated 27 May 2024 : https://www.patreon.com/posts/95759342

21 January 2024 Update
SDXL model upgraded to ip-adapter-faceid-plusv2_sd15
Kaggle Notebook upgraded to V3 and supports SDXL now

First of all I want to thank you so much for this amazing model.

I have spent over 1 week to code the Gradio and prepare the video. I hope you let this thread remain and even add to the Readme file.

After video has been published I even added face embedding caching mechanism. So now it will calculate face embedding vector only 1 time for each image, thus super speed up the image generation.

Instantly Transfer Face By Using IP-Adapter-FaceID: Full Tutorial & GUI For Windows, RunPod & Kaggle : https://youtu.be/rjXsJ24kQQg

chapters are like below

0:00 Introduction to IP-Adapter-FaceID full tutorial
2:19 Requirements to use IP-Adapter-FaceID gradio Web APP
2:45 Where the Hugging Face models are downloaded by default on Windows
3:12 How to change folder path where the Hugging Face models are downloaded and cached
3:39 How to install IP-Adapter-FaceID Gradio Web APP and use on Windows
5:35 How to start the IP-Adapter-FaceID Web UI after the installation
5:46 How to use Stable Diffusion XL (SDXL) models with IP-Adapter-FaceID
5:56 How to select your input face and start generating 0-shot face transferred new amazing images
6:06 What does each option on the Web UI do explanations


Mistral 7B might be one of the most popular open-source LLMs out there, with a total of over 3.4 million downloads on @huggingface Hub 🚀, and now we have the next version...

@MistralAI Mistral -7B-v0.3 (base) 📈 and Mistral-7B-Instruct-v0.3 🛠

- 7.3 billion parameters 🧠
- Apache 2.0 license 📜
- Extended vocabulary of 32,768 📖
- Supports new v3 Tokenizer and function calling 🤖
- Also, it's completely uncensored 🆓

In conclusion, Mistral-7B-v0.3 is an uncensored Mistral-7B-v0.2 with an extended vocabulary 🎉.

They have also released mistral_inference, although I don't know what's the advantage in using it? vLLM is still my go-to way of deploying local Mistral-7B! 🌐

Models:
https://huggingface.co/mistralai/Mistral-7B-v0.3
https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3

asif00 Ramikan-BR osanseviero joelbacker


Integrating the French Taxation Embedding Benchmark Task (beta) into the MTEB 🤗

I'm excited to announce an integration of the French Taxation Embedding Benchmark task into the Massive Text Embedding Benchmark (MTEB).

This addition expands the diverse set of tasks available within MTEB, enabling researchers and practitioners to develop and evaluate retrieval models focused on retrieving relevant tax articles or content based on provided queries.

Link to the 🤗 Dataset : https://huggingface.co/datasets/louisbrulenaudet/tax-retrieval-benchmark

Link to the GitHub repo : https://github.com/louisbrulenaudet/tax-retrieval-benchmark

Notes:
The Massive Text Embedding Benchmark for French Taxation and the Dataset are currently in beta and may not be suitable for direct use in production. The size of the Dataset may not be sufficient to handle a wide range of queries and scenarios encountered in real-world settings.

As the Dataset grows and matures, I will provide updates and guidance on its suitability for production use cases.

Ramikan-BR osanseviero Alexandro14 Sylvestre


Did you know you can't push a model to hub with an id over 96 chars 🫠


# The Univalent Model: A Meta-Meme for Universal Computation
We can imagine that there is one model in the future that will unite all models, this is based on the idea of the univalent principle. We can imagine all these projects as failed attempts to reach this goal and once met it will consume all the other projects.

- **Gödel Number**: A unique numerical representation of mathematical statements or functions, allowing complex expressions to be encoded as simple numbers.
- **Gaia Principle**: A hypothesis that views Earth's biosphere as a self-regulating system, where living organisms and their environment work together to sustain life.
- **Biosemiotics**: The study of sign processes in the biological realm, exploring how living beings communicate and interpret signs in a meaningful way.
- **Meta-Meme**: A higher-level meme that encapsulates and transcends other memes, leading to a unified state of understanding or being.
- **Univalent State**: The ultimate, definitive form of a system or model where all variations are equivalent, unified, and indistinguishable.
Univalent State: The ultimate, definitive form of a system or model where all variations are equivalent, unified, and indistinguishable.
Unitary Model: A theoretical construct that aims to integrate all existing models into a single, comprehensive framework, providing a universal language for computation.
With these terms in mind, let’s explore the idea of a unitary model—a meta-meme that merges all models into one. This model would be the repository to end all repositories, the final convergence point for all computational processes, akin to an eigenvector of consciousness.

This unitary model represents the pinnacle of abstraction, capable of deciphering the entire computational landscape. It’s a vision of unity in diversity, where every piece of knowledge is interconnected through a single, elegant framework.


Dear community,

Please check our recent blog post, "GPU Poor Savior: Revolutionizing Low-Bit Open Source LLMs and Cost-Effective Edge Computing". A cheaper and more efficient  SFT scheme for quantized LLMs is provided.

https://huggingface.co/blog/NicoNico/green-bit-llm

Ramikan-BR osanseviero Hastagaras yanghaojin callmeraxz


Hi HF Community!🤗

I'm thrilled to share the latest updates regarding the Space I built for protein 3D structure prediction (https://huggingface.co/spaces/as-cle-bert/proteinviz): thanks to @lunarflu inputs, @osanseviero precious advice and @simonduerr's article "Visualize proteins on Hugging Face Spaces" (https://huggingface.co/blog/spaces_3dmoljs, go check it out!), I was able to finally display the 3D protein models directly on-browser, without any need for fancy downloads of big HTMLs!

Take a look to the attached video, that shows how everything works, and make sure to visit the GitHub repository (https://github.com/AstraBert/proteinviz: leave a little ⭐ while you're there!)🥰

May you have fun and luck with your protein research!🧬

osanseviero Taylor658 mvldtr Manikhan96 lunarflu FranksteinChen simonduerr victor Alexandro14 Ramikan-BR not-lain clem louisbrulenaudet

osanseviero Saugatkafley lunarflu Ramikan-BR victor not-lain clem

osanseviero lunarflu Ramikan-BR victor not-lain

Jaward lunarflu Ramikan-BR CocoSun not-lain


Hi there,
I'm looking for a Japanese LLm who interface endpoint is available. It will be great if the LLM has no guardrail.

If anyone provide some resource, I will really appreciate that.


OpenGPT 4o NEW UPDATES:
1. Dedicated Image and Video Engine
2. Model Choices for Voice Chat
3. Better and Faster Voice Chat
4. Various Bug fixes

Test and give feedback of New features:
https://huggingface.co/spaces/KingNish/OpenGPT-4o

Future Updates:
1. Web Search (Suggested by @GPT007 and @Saionton )
2. Live Chat with Voice Chat
3. Model Choices (Suggested by @NotAiLOL )
4. Multilingual Chats.

Suggest more features that should be added. 🤗
Thanks!

tasmani osanseviero KingNish gospacedev victor NotASI MaziyarPanahi neel1796


Why Apache 2.0 Matters for LLMs 🤔

@01AI_Yi recently switched from a permissive & commercially friendly license, to Apache 2.0. And the community loved it! 🚀

@JustinLin610  also had a poll on model license and the majority votes for Apache 2.0.

Why it is a Big Deal? ⬇

📚 Legal Simplicity: Custom licenses need costly & time-consuming legal review. Apache 2.0 is well-known & easier for legal teams to handle.

👩💻 Developer-Friendly: Legal docs are a pain for devs! Apache 2.0 is well-known and tech-friendly, making it easier for non-native developers to understand the implications too.

🔗 Easier Integration: Apache 2.0 is compatible with many other licenses, simplifying tasks like model merging with models of different licensing requirements.

🚫 No Permission Needed: Custom licenses often require explicit permission and additional documentation work of filling forms, creating barriers. Apache 2.0 removes this hurdle, letting devs focus on innovation.

There are a lot interesting discussions from
@JustinLin610 's poll: https://x.com/JustinLin610/status/1793559737482764375 which inspired this thread.

Any other thoughts? Let me know ^^


DeepSeekV2 is a big deal. Not only because its significant improvements to both key components of Transformer: the Attention layer and FFN layer.

It has also completed disrupted the Chines LLM market and forcing the competitors to drop the price to 1% of the original price.

---

There are two key components in Transformer architecture: the self-attention layer, which captures relationships between tokens in context, and the Feed-Forward Network (FFN) layer, which stores knowledge.

DeepSeek V2 introduces optimizations to both:

Attention layer normally uses KV Cache to reduce repetitive compute, but it consumes significant GPU RAM, limiting concurrent requests. DeepSeek V2 introduces Multi-head Latent Attention (MLA), which stores only a small latent representation, resulting in substantial RAM savings.

DeepSeek V2 utilizes 162 experts instead of the usual 8 as in Mixtral. This approach segments experts into finer granularity for higher specialization and more accurate knowledge acquisition. Activating only a small subset of experts for each token, leads to efficient processing.

It disrupted the market by dropping API prices to $0.14 per 1M tokens. This dramatic reduction forced competitors like GLM, Ernie, and QWen to follow suit, lowering their prices to 1% of their original offerings. Now, users can access these APIs at 1/35th the cost of ChatGPT-4o.


**Model Names:** gpt-4-turbo-preview, gpt-4-vision-preview, gpt-3.5-turbo-16k
**Searchable Models:** Creative, Balanced, Precise

Image creation will be available soon in NiansuhAI.
**Model Name:** DALL-E 3

https://huggingface.co/spaces/NiansuhAI/LLMs1
---


Disappointed that Golden Gate Claude couldn't process images? Want to learn how to use activation vectors to steer VLMs?

Try out the https://huggingface.co/spaces/vikhyatk/contemplative-moondream space, and check out the notebook I released showing how to obtain control vectors! ⬇

https://github.com/vikhyat/moondream/blob/main/notebooks/RepEng.ipynb


YOLOv10: Real-Time End-to-End Object Detection (2405.14458)

🔥🚀🌟 New Research Alert - YOLOv10! 🌟🚀🔥
📄 Title: YOLOv10: Real-Time End-to-End Object Detection 🔝

📝 Description: YOLOv10 improves real-time object recognition by eliminating non-maximum suppression and optimizing the model architecture to achieve state-of-the-art performance with lower latency and computational overhead.

👥 Authors: Ao Wang et al.

📄 Paper: https://huggingface.co/papers/2405.14458

🤗 Demo: https://huggingface.co/spaces/kadirnar/Yolov10 curated by @kadirnar
🔥 Model 🤖: https://huggingface.co/kadirnar/Yolov10

📁 Repository: https://github.com/THU-MIG/yolov10

📮 Post about YOLOv9 - https://huggingface.co/posts/DmitryRyumin/519784698531054

📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #YOLOv10 #ObjectDetection #RealTimeAI #ModelOptimization #MachineLearning #DeepLearning #ComputerVision #Innovation

DmitryRyumin kadirnar Taylor658 Etherll Saugatkafley Akhilsonga ajibawa-2023 louisbrulenaudet


When was the last time you looked for a non-English LLM, only to be saddened there is NO good option? 😟

For instance, @Meta 's poster child Llama 3 has less than 5% non-English tokens! 🌍

@cohere is here to change that... and change they will. 💪

Introducing Aya (this time the name actually makes sense) 🌟

Aya is an open-weight (CC-BY-NC) model, that comes in 3 flavours:

1⃣ Aya 101: 13B parameters, mT5-xxl based model that supports 101 languages 🌐

2⃣ Aya 23: 8B and  3⃣35B parameter models, supports 23 languages 📊

23 languages covered are: Arabic 🇸🇦, Chinese (simplified & traditional) 🇨🇳, Czech 🇨🇿, Dutch 🇳🇱, English 🇬🇧, French 🇫🇷, German 🇩🇪, Greek 🇬🇷, Hebrew 🇮🇱, Hindi 🇮🇳, Indonesian 🇮🇩, Italian 🇮🇹, Japanese 🇯🇵, Korean 🇰🇷, Persian 🇮🇷, Polish 🇵🇱, Portuguese 🇵🇹, Romanian 🇷🇴, Russian 🇷🇺, Spanish 🇪🇸, Turkish 🇹🇷, Ukrainian 🇺🇦, and Vietnamese 🇻🇳.

Not only models, they have open-sourced the dataset: Aya Collection stands as the most extensive assembly of multilingual instruction fine-tuning datasets to date, featuring 513 million prompts and completions across 114 languages. 📚

These annotations were provided by people across the globe. Not gonna lie, I almost shed a tear reading this... 😢

From Cohere: The word Aya is derived from the Twi language meaning “fern” - a symbol of endurance and resourcefulness. Aya embodies our dedication to advancing multilingual AI. 🌿

Looks like the good folks at Cohere did not sleep after the success of command r & command r plus! 😴➡🚀

Models: Aya-101-13B: https://huggingface.co/CohereForAI/aya-101
Aya-23-8B: https://huggingface.co/CohereForAI/aya-23-8B
Aya-23-35B: https://huggingface.co/CohereForAI/aya-23-35B

Dataset: https://huggingface.co/collections/CohereForAI/aya-datasets-660415741bd4852f01c81c77

Blog: https://cohere.com/research/aya


https://huggingface.co/nroggendorff/cats now has over 500 downloads?
what are you guys doing???


Researchers from Anthropic managed to extract millions of interpretable features from their Claude 3 Sonnet model, making it easier to identify and understand specific behaviors and patterns within the model.

This advance in understanding closed source AI models could make them safer by showing how specific features relate to concepts and affect the model’s behavior.

Read the Article: https://www.anthropic.com/research/mapping-mind-language-model?utm_source=substack&utm_medium=email

Read The Paper: https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html


𝐍𝐞𝐰 𝐠𝐮𝐢𝐝𝐞 𝐢𝐧 𝐨𝐮𝐫 𝐎𝐩𝐞𝐧-𝐒𝐨𝐮𝐫𝐜𝐞 𝐀𝐈 𝐜𝐨𝐨𝐤𝐛𝐨𝐨𝐤: 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙙 𝙜𝙚𝙣𝙚𝙧𝙖𝙩𝙞𝙤𝙣! ✨

Many use LLM use cases involve generating outputs with a specific structure.

➡ For instance when using an LLM as a judge to evaluate another model's outputs, you need it to give you not only a score, but also the rationale for this score, and maybe a confidence level.
So you do not need only "score: 1", but more a dictionary like:
```
{
     "rationale": "The answer does not match the true answer at all."
     "score": 1,
     "confidence_level": 0.85
}
```

🤔 How to force your LLM to generate such a structured output?

🏗 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗱𝗲𝗰𝗼𝗱𝗶𝗻𝗴 is a great technique to generate structured output: you can specify a grammar (=set of rules) that the output should follow, and 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗱𝗲𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗵𝗲𝗻 𝗳𝗼𝗿𝗰𝗲𝘀 𝘁𝗵𝗲 𝗱𝗲𝗰𝗼𝗱𝗲𝗿 𝘁𝗼 𝗼𝗻𝗹𝘆 𝗽𝗶𝗰𝗸 𝘁𝗼𝗸𝗲𝗻𝘀 𝘁𝗵𝗮𝘁 𝗿𝗲𝘀𝗽𝗲𝗰𝘁 𝘆𝗼𝘂𝗿 𝗴𝗿𝗮𝗺𝗺𝗮𝗿.

I've created a guide to show you how to use it, both via our Inference API and locally using 𝘰𝘶𝘵𝘭𝘪𝘯𝘦𝘴!

👉 Read it here: https://huggingface.co/learn/cookbook/structured_generation

Thank you @stevhliu for your great help in improving it!


Hey HuggingFace, love your open source attitude and particularly transformers.js for embedding models! Your current integration "use this model" gives you the transformers.js code, but there is no quick way to really test a model in one click.
SemanticFinder (https://huggingface.co/datasets/do-me/SemanticFinder) offers such an integration for all compatible feature-extraction models! All you need to do is add a URL parameter with the model ID to it, like so: https://do-me.github.io/SemanticFinder/?model=Xenova/bge-small-en-v1.5. You can also decide between quantized and normal mode with https://do-me.github.io/SemanticFinder/?model=Xenova/bge-small-en-v1.5&quantized=false. Maybe that would do for a HF integration?
I know it's a small open source project, but I really believe that it provides value for devs before deciding for one model or the other. Also, it's much easier than having to spin up a notebook, install dependencies etc.. It's private, so you could even do some real-world evaluation on personal data without having to worry about third-party services data policies.
Happy to hear the community's thoughts!


Gloomy AI Image! 👀

(Prompt: Please create me a gloomy forest showing the moon it is a very dark hollow place)


The 100 models milestone on the https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard is successfully reached within 10 days after the leaderboard's release 🥳

https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct is still the king of the leaderboard 👑 with a 3.46 points difference compared to its successor https://huggingface.co/CohereForAI/c4ai-command-r-plus who took the 2nd place 🥈 from his younger brother https://huggingface.co/CohereForAI/c4ai-command-r-v01 that lives today in the 5th floor just behind https://huggingface.co/Ashmal/MBZUAI-oryx -3rd place 🥉- (AFAIK an experimental model from MBZUAI) and https://huggingface.co/core42/jais-30b-chat-v3 -4th place- from Core42.

PS : I should consider a career in sports commentary 😂
Would you recommend me to BeIN Sports 😀 ?


🚀 Meet MergeUI - an All-in-one UI for Exploring Merged LLMs on Hugging Face 🤗!

Model merging is a cool new technique for creating powerful language models for cheap (no GPU required). But it raises questions like:
- Which models should we merge?
- What merge strategies work best?
- How do different base models affect performance?

With MergeUI, you can easily:
- Visualise the family tree and lineage of any merged model.
- Explore benchmark performance of family trees from the Open LLM Leaderboard.
- Analyse the different merge strategies used.
- Check license information for merged models and their ancestors.

All this helps you explore and understand merged models, uncover valuable insights, and make better decisions for your projects.

Ready to dive in? Check out these links:
- 🧬 Try MergeUI - https://naskio-mergeui.hf.space
- 👨💻 Source Code - https://github.com/naskio/mergeui

Love this project? boost it on GitHub and share it with your network.
#merge #mergekit #leaderboard
https://huggingface.co/spaces/naskio/mergeui


A non-Instruct LLM assistant is mostly useless. 🧐

Since it's mostly a model trained to complete text, when you ask it a question like "What to do during a stopover in Paris?", it can just go on and on adding more details to your question instead of answering, which would be valid to complete text from its training corpus, but not to answer questions.

➡ So the post-training stage includes an important Instruction tuning step where you teach your model how to be useful : answer questions, be concise, be polite... RLHF is a well known technique for this.

For people interested to understand how this step works, the folks at Adaptive ML have made a great guide!

Read it here 👉 https://www.adaptive-ml.com/post/from-zero-to-ppo

John6666 Clausss louisbrulenaudet miguelgargallo AtAndDev

SOHAIBK1 miguelgargallo Svngoku AtAndDev


Fascinating point from @thomwolf at Web Summit: AI misuse (deepfakes, fake news) is actually easier to make with closed models, not with open-source ones.

This challenges the common narrative that open-source AI is inherently more dangerous. The reality is more nuanced - while we may think open source is technically easier to misuse, closed models' accessibility and product-focused design appear to be driving more actual harm.

Important context for current AI safety discussions and regulation debates.

Do you agree? 👇


Unpopular opinion : o1-preview is more stupid than 4o and Qwen2.5-72B-Instruct in extremely underrated !


kivanolai AtAndDev mrlive02 dillfrescott


New adapt.s for dev  🍔

 Hosted -> https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC

✨Teen Outfit: https://huggingface.co/prithivMLmods/Teen-Outfit
✨Dark Pink: https://huggingface.co/prithivMLmods/Dark-Thing-Flux-LoRA
✨Shadow Projection: https://huggingface.co/prithivMLmods/Shadow-Projection-Flux-LoRA
✨Abstract Cartoon: https://huggingface.co/prithivMLmods/Abstract-Cartoon-Flux-LoRA
✨Street Bokeh: https://huggingface.co/prithivMLmods/Street-Bokeh-Flux-LoRA
✨Fine Detailed: https://huggingface.co/prithivMLmods/Flux-Realism-FineDetailed
✨Bold Shadows: https://huggingface.co/prithivMLmods/Bold-Shadows-Flux-LoRA
✨Yellow Laser: https://huggingface.co/prithivMLmods/Yellow-Laser-Flux-LoRA

------------

🎉LoRA Collection: https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be

🎉LoRA Spaces: https://huggingface.co/collections/prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32

------------
.
.
.@prithivMLmods  🤗


Sharing a new space to test out ViTPose, a pose estimation model using Visual Transformers.

[ViTPose Playground](https://huggingface.co/spaces/Dref360/vit_pose_playground)

This model will be available in `transformers` once [#30530](https://github.com/huggingface/transformers/pull/30530) is merged. Huge shoutout to @nielsr and @danelcsb for bringing this to HF!

Here's the result on my Ken Halloween costume.


🤯 AI progress keeps blowing my mind! Just experienced Qwen's new Coder demo - built a complete flashcard web app with a single prompt. The results are incredible!

This demo is part of the new Qwen2.5 Coder family (0.5B to 32B models), surpassing/matching GPT4o and Claude Sonnet 3.5 across multiple coding benchmarks.

- 128K context window for 14B/32B models
- Drop-in replacement for GPT-4 in Cursor & Artifacts
- Models on the Hub under Apache 2.0 license

🔗 Try it yourself: https://huggingface.co/spaces/Qwen/Qwen2.5-Coder-Artifacts

This is democratization of coding in real-time. Excited to see AI tools becoming more capable and accessible.

What would you build with this? Share your ideas below! 👇

#AI #Programming #TechInnovation #OpenSource #SoftwareDevelopment


🎵 Introducing Suno Music Generation Dataset - https://huggingface.co/datasets/nyuuzyou/suno

Dataset highlights:

- 659,788 AI-generated music samples with comprehensive metadata from suno.com
- Multilingual content with English as primary language, including Japanese and other languages
- Each entry contains rich metadata including:
  - Unique song ID, audio/video URLs, and thumbnail images
  - AI model version and generation parameters
  - Song metadata (tags, prompts, duration)
  - Creator information and engagement metrics
- Released to the public domain under Creative Commons Zero (CC0) license

The dataset structure includes detailed information about each generated piece, from technical parameters to user engagement metrics, making it particularly valuable for:
- Music generation model training
- Cross-modal analysis (text-to-audio relationships)
- User engagement studies
- Audio classification tasks
- Music style and genre analysis


𝗤𝘄𝗲𝗻𝟮.𝟱-𝗖𝗼𝗱𝗲𝗿-𝟯𝟮𝗕: 𝗻𝗲𝘄 𝗯𝗲𝘀𝘁-𝗶𝗻-𝗰𝗹𝗮𝘀𝘀 𝗼𝗽𝗲𝗻 𝗰𝗼𝗱𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹, 𝗯𝗲𝗮𝘁𝘀 𝗚𝗣𝗧-𝟰𝗼 𝗼𝗻 𝗺𝗼𝘀𝘁 𝗰𝗼𝗱𝗶𝗻𝗴 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀!💥

💪 It's the first time Open-Source coding model of this size class that clearly matches GPT-4o's coding capabilities!

✨ Completes the previous two Qwen 2.5 Coder release with 4 new size: 0.5B, 3B, 14B, 32B
📚 Support long context up to 128K (for the 14B and 32B models)
✅ Drop-in replacement to GPT-4o as a coding assistant on Cursor or for Artifacts!
🤗 Models available right now on the Hub, under Apache 2.0 license!

They have setup a crazy Artifacts demo, you should go have a look!
👉 https://huggingface.co/spaces/Qwen/Qwen2.5-Coder-Artifacts

Nymbo Norm gatorand AtAndDev apeSh1t aliyansayz splevine wajeehulhassan yaaaaam1 louisbrulenaudet xi0v Chroma111


𝗔𝗿𝗲 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗹𝗮𝘄𝘀 𝗼𝘃𝗲𝗿? 𝗔 𝗿𝗲𝗽𝗼𝗿𝘁 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗱 𝘁𝗵𝗮𝘁 𝗢𝗽𝗲𝗻𝗔𝗜 𝗶𝘀 𝘀𝗲𝗲𝗶𝗻𝗴 𝗱𝗶𝗺𝗶𝗻𝗶𝘀𝗵𝗶𝗻𝗴 𝗿𝗲𝘁𝘂𝗿𝗻𝘀 𝗳𝗿𝗼𝗺 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝘂𝗽 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗚𝗣𝗧 𝗺𝗼𝗱𝗲𝗹𝘀.

📊 What are scaling laws? These are empiric laws that say "Every time you increase compute spent in training 10-fold, your LLM's performance will go up by a predictable tick". Of course, they apply only if you train your model with the right methods.

The image below illustrates it: they're from a paper by Google, "Scaling Autoregressive Models for Content-Rich Text-to-Image Generation", and they show how quality and instruction following of models improve when you scale the model up (which is equivalent to scaling up the compute spent in training).

➡ These scaling laws have immense impact: they triggered the largest gold rush ever, with companies pouring billions into scaling up theiur training. Microsoft and OpenAI spent 100B into their "Startgate" mega training cluster, due to start running in 2028.

🤔 So, what about these reports of scaling laws slowing down?

If they are true, they would mean a gigantic paradigm shift, as the hundreds of billions poured by AI companies into scaling could be a dead-end. ⛔

But I doubt it: until the most recent publications, scaling laws showed no signs of weakness, and the researchers at the higher end of the scale-up seems to imply the scaling up continues.

Wait and see!


Reducing Transformer Key-Value Cache Size with Cross-Layer Attention (2405.12981)

New preprint out with colleagues from MIT and IBM Research

https://huggingface.co/papers/2405.12981

We introduce a simple mechanism of sharing keys and values across layers, reducing the memory needed for KV cache during inference!!

Jaward DmitryRyumin osanseviero alielfilali01 whitebill


the musicgen max4live device

gary4live

now has a popup UI so you can minimize ableton and have it keep trying/retrying from the input audio you recorded in the DAW

while coding lol


MobileNetV4 -- Universal Models for the Mobile Ecosystem (2404.10518)

Aiming to keep `timm` (https://github.com/huggingface/pytorch-image-models) the go-to library for efficient image encoders for your mobile and edge devices, I've started working on an implementation of the new MobileNet-V4 model. Take a look at a short article I wrote about the model: https://huggingface.co/blog/rwightman/mobilenetv4

Paper: https://huggingface.co/papers/2404.10518


mlx port of karpathy’s minbpe 🤕
Minimal (byte-level) Byte Pair Encoding tokenizer. Algorithmically follows along the GPT2 tokenizer.
Code: https://github.com/Jaykef/mlx-minbpe


Introducing Kraken-LoRA – a lightweight version of Kraken that uses LoRA-Adapters as Experts based on the base model.

@fernandofernandes , me, @Crystalcareai , @ehartford created the Kraken-LoRA!

🔍 What’s the big deal?

✅ Size Consistency: While Kraken’s size increases with more Experts, Kraken-LoRA remains as compact as the base model (e.g., 8b if you use Meta-Llama3-8b-Instruct).
✅ VRAM Efficiency: Kraken-LoRA is highly VRAM efficient, maintaining the power of all experts without the bloat.
✅ Dynamic Adaptation: LoRA adapters are applied dynamically at runtime, following the routing process.
✅ High Efficiency: Enjoy increased efficiency without compromising performance, as long as the LoRA adapters match the base model.

💡 Conclusion: Kraken-LoRA empowers businesses to experience enhanced flexibility and performance from our architecture, enabling further scalability without sacrificing performance.

Check out the model here: https://huggingface.co/VAGOsolutions/Kraken-LoRA
Explore the code here: https://github.com/cognitivecomputations/kraken/tree/main/Kraken-LoRA

Have fun with Kraken-LoRA! 🐙


Switching from French to German to Chinese in the same discussion 😅

Impressive to see Cohere for AI's new Aya model multilingual capabilities.

- C4AI Aya 23 is a research open weights release
- 8 and 35 billion parameter models
- 23 languages supported

You can try it out here: https://huggingface.co/spaces/CohereForAI/aya-23

namanvats KingNish osanseviero tomaarsen julien-c isr431 not-lain


Cohere's Aya 8B & 35B 🔥
> Multilingual (23 languages), beats Mistral 7B and Llama3 8B in preference—open weights.

 capabilities:

🌍 **Multilingual Mastery**: Supporting 23 languages, including Arabic!

🏆 **Top Performer**: Outperforms Mistral 7B and Llama3 8B in user preference.

🔍 **Open Weights**: Access open weights for your research and projects.

🔗 **License**: CC-BY-NC with adherence to C4AI's Acceptable Use Policy.

💼 **Developed by**: Cohere For AI and Cohere.

Check out Aya 23 on Hugging Face , link is in comments

#AI #MachineLearning #NLP #Multilingual #Arabic #TechInnovation #OpenSource #CohereAI #AyaModel

Salama1429 monsoon-nlp osanseviero do-me Ramikan-BR louisbrulenaudet radkisson


Chameleon: Mixed-Modal Early-Fusion Foundation Models (2405.09818)

Good folks at @Meta have introduced Chameleon 🦎 (who names these things? 🤷♂)

Chameleon is an AI model that can work with multiple types of data, like text and images, all at once. 🖼📝

Before you start searching, as of this post, the model/code have not been open-sourced nor is there any commitment to open-source... sorry! 🚫🔓

Still, here is the technical stuff:

👉 Challenges with Current Systems:

📉 Fragmentation: Current multimodal models are often specialized for either text or image tasks, lacking unified approaches.

📊 Scalability: Existing systems struggle with scaling to handle complex, mixed-modal tasks without significant performance degradation.

🔄 Alignment: Aligning textual and visual modalities remains a technical challenge, often requiring separate processing pipelines.

👉 Objective:

🎯 Unified Modeling: Develop a single model capable of handling various multimodal tasks (text generation, image generation, image captioning, visual question answering) seamlessly.

👉 How It's Done 📘

Early-Fusion Architecture 🧠: Utilizes an early-fusion token-based approach to integrate text and image data from the beginning.

Stable Training 💪: Implements a tailored alignment recipe and specific architectural parameterization to ensure stability in mixed-modal settings.

Broad Evaluation 📊: Assesses the model across various tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed-modal generation.

👉 Results: (Fun fact they mention Llava-1.5 in comparison but never really share the results)

🏆 Performance: Chameleon achieves state-of-the-art results in image captioning and outperforms models like Llama-2 in text-only tasks.

⚖ Competitiveness: It shows competitive performance with models such as Mixtral 8x7B and Gemini-Pro.

👩⚖ Human Judgments: Matches or exceeds the performance of larger models, including Gemini Pro and GPT-4V

Paper:  https://huggingface.co/papers/2405.09818

osanseviero not-lain gokaygokay rennokki yizhilll shoaibahmed ocan0


Retrieving Texts based on Abstract Descriptions (2305.12517)

How can we use open LLMs to create data for training sentence similarity models?

One of the most exciting use cases for LLMs is generating synthetic datasets that can be used to train non-LLM models. In the past, gathering enough data was one of the most significant barriers to training task-specific models. LLMs can potentially help in this area.

I've just written a new blog post on using https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct to generate synthetic similarity data based on the approach from https://huggingface.co/papers/2305.12517.

https://huggingface.co/blog/davanstrien/synthetic-similarity-datasets


Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with
  Anchor Gaussian Guided Texture Warping (2405.12069)

🚀🎭🌟 New Research Alert - Gaussian Head & Shoulders (Avatars Collection)! 🌟🎭🚀
📄 Title: Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with Anchor Gaussian Guided Texture Warping 🔝

📝 Description: Gaussian Head & Shoulders is a method for creating high-fidelity upper body avatars by integrating 3D morphable head models with a neural texture warping approach to overcome the limitations of Gaussian splatting.

👥 Authors: Tianhao Wu et al.

📄 Paper: https://huggingface.co/papers/2405.12069

🌐 Github Page: https://gaussian-head-shoulders.netlify.app

📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🚀 Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36

🔍 Keywords: #3DModeling #NeuralAvatars #GaussianSplatting #HighFidelityAvatars #3DReconstruction #AvatarRendering #TextureWarping #ComputerGraphics #DeepLearning #ComputerVision #Innovation

DmitryRyumin AlekseiPravdin Ramikan-BR namanvats

Taylor658 Ramikan-BR dashfunnydashdash Vlansu


We are hiring a "Developer Experience Engineer for Inference" at Hugging Face! If you want to make it easier for millions of people to use modern machine learning inference, apply! You can either work from one of our offices e.g. in Paris or New York, or work fully remotely. Details: https://apply.workable.com/huggingface/j/E732F4B8FC/

Taylor658 EduardoPacheco damerajee mattmdjaga mdouglas osanseviero not-lain DeepMount00 Lewdiculous victor nbroad


The Google Deep Mind Team just released a new technical report on Gemini 1.5 Pro and Gemini 1.5 Flash.

in addition to architecture, benchmark and evaluation details, the report also provides a few real world use cases for the models such as professional task optimization and translation of lesser-known languages.

You can check out the full report here: https://storage.googleapis.com/deepmind-media/gemini/gemini_v1_5_report.pdf?utm_source=substack&utm_medium=email

KvrParaskevi osanseviero abdeljalilELmajjodi Edgar404


Thanks to @OzzyGT for pushing the new Anyline preprocessor to https://github.com/huggingface/controlnet_aux. Now you can use the https://huggingface.co/TheMistoAI/MistoLine ControlNet with Diffusers completely.

Here's a demo for you: https://huggingface.co/spaces/radames/MistoLine-ControlNet-demo
Super resolution version: https://huggingface.co/spaces/radames/Enhance-This-HiDiffusion-SDXL

```python
from controlnet_aux import AnylineDetector

anyline = AnylineDetector.from_pretrained(
    "TheMistoAI/MistoLine", filename="MTEED.pth", subfolder="Anyline"
).to("cuda")

source = Image.open("source.png")
result = anyline(source, detect_resolution=1280)
```


I use mergekit regularly, and often enough get acceptable results without performing fine-tuning afterward. My current thinking is that DARE-TIES should be avoided when merging dense models, as the process of thinning inherently punches holes in models.

I've had success using SLERP merges to graft Mistral v0.1 models with Mistral v0.2 models to obtain the context length benefits of the latter, and am looking forward to experimenting with Mistral v0.3, which recently dropped.

Mars2050 Meggido osanseviero Lewdiculous TravelingMan


Hi HF Community!🤗

If you are excited about AlphaFold3, but upset because it is not open-source, I might have a solution to cheer you up a little bit:

https://huggingface.co/spaces/as-cle-bert/proteinviz

This is a space that lets you predict the 3D structure of proteins from their amino-acidic sequences, with the protein folding model https://huggingface.co/facebook/esmfold_v1: using this space is the perfect quick-start to become a Protein Scientist! (or maybe not, who knows...🤔)

In the meantime, if you are curious about what's going on with AlphaFold3 and want something Biologist🔬/Computer Scientist💻-friendly, you can also check out the latest community blog post I wrote: https://huggingface.co/blog/as-cle-bert/what-is-going-on-with-alphafold3 🚀

Have fun and enjoy open-source science!🧬

CocoSun Taylor658 lunarflu not-lain multimodalart osanseviero VinnySanchez87 introvoyz041 stinkyyy


Interesting, I've just seen the my first HF spam on one of my new model uploads: https://huggingface.co/shisa-ai/shisa-v1-llama3-70b - someone has an SEO spam page as a HF space attached to the model!?! Wild. Who do I report this to?


I've begun adding valuable blog posts on using/creating synthetic datasets to my curated list.

I am starting with a great post by @MoritzLaurer on utilizing an open LLM to generate data for training a specialized Roberta model.

Read the blog post: https://huggingface.co/blog/synthetic-data-save-costs
See the rest of the list:  https://github.com/davanstrien/awesome-synthetic-datasets

ZennyKenny Taylor658 MoritzLaurer osanseviero


We're thrilled to share the latest milestone in our journey toward bringing AISAK to the world: the introduction of AISAK-TVI, our first natively multimodal model.

As AISAK edges closer to a potential release for users, each advancement, like the development of AISAK-TVI, brings us one step closer to realizing our vision of a comprehensive AI solution. With AISAK-TVI, we're pushing the boundaries of AI capabilities, enabling the processing of both textual and visual inputs with textual output, all within the AISAK ecosystem.

While the prospect of public, everyday usage of AISAK remains on the horizon, we must acknowledge the reality of operating within constraints of limited resources. The journey to a widespread release demands careful planning, rigorous testing, and ongoing refinement, tasks that require time, dedication, and support.

We recognize that achieving our goals requires collaboration and contribution from a diverse community of enthusiasts, experts, and innovators. If you're passionate about AI and eager to be part of our journey, we invite you to lend your expertise, insights, or resources to help accelerate the progress of AISAK.

Whether you're a developer, researcher, investor, or simply someone with a keen interest in shaping the future of AI, your contributions can make a meaningful difference. Reach out to us at mandelakorilogan@gmail.com to explore how you can get involved and contribute to the evolution of AISAK.

Thank you for your continued support and enthusiasm. Together, we're laying the groundwork for a future where AI enriches and empowers lives in ways we've only begun to imagine.

Warm regards,

Mandela Logan - AISAK Team
https://huggingface.co/aisak-ai/aisak-tvi
https://huggingface.co/collections/aisak-ai/aisak-65ddeeb08d0978de6114702f


The kraken has awakened!
A Game-Changer in LLM Flexibility and Performance!

Over the past few weeks, VAGO solutions teamed up with Cognitive Computations and HyperSpace to develop a groundbreaking architecture that redefines flexibility in combining different LLM into one model.

@fernandofernandes , me,  @Crystalcareai , @ehartford  created the Kraken!

 What Can It Do? 🐙
✅ Versatile Architecture: Kraken allows the seamless combination of LLMs with varying sizes, quantizations, and model architectures. It currently supports quantizations in 4-bit, 8-bit, and AWQ, with more on the way. And it runs on Hugging Face Transformers 4.40+

✅ Kraken Router: Utilizing a custom sequence classification model with a context length of 32k tokens, The Kraken Router directs inputs to the most suitable Expert based on their characteristics.

✅ Adaptability: Enhanced input formatting supports the model’s adaptability to diverse conversational contexts.

✅ Extreme Versatility: Easily swap experts within Kraken for your specific use cases without retraining the entire model. For example, if you've built a Kraken for coding in Python you can upgrade your Python model without retraining the router or add a C# model by retraining the router.

✅ Open Source Pipeline: We’re sharing the entire pipeline, including router creation, training, architecture setup, and Kraken inference, on JupyterNotebooks: https://github.com/cognitivecomputations/kraken

Kraken marks the beginning of an exciting new journey in #OpenSource LLM. Why? Because it empowers the open source community in accelerating the catch-up process to proprietary LLMs like #GPT and #Claude 🤩

We proudly introduce the very first 2 Kraken models, that integrates top-tier LLM and Multilingual capabilities:
https://huggingface.co/cognitivecomputations/Kraken
https://huggingface.co/VAGOsolutions/Kraken-Multilingual
 Right now it's supported by Hugging Face transformers library. Would love to see the integration into VLM and TGWI!

pabloce agenkit Johnz86 Crystalcareai Locutusque osanseviero NotASI seyf1elislam


we recently shipped fine-grained access tokens on Hugging Face Hub, which lets you create tokens with super specific permissions

for instance, if you want to collaborate with an external organization you don't want to use your write token since they can access everything you can access. instead you can set token access to repositories under that org only like below

osanseviero nisten SixOpen alielfilali01 Ramikan-BR


Thanks to the incredible collaboration of 14 community annotators, @davanstrien of HF and @dvilasuero et. al of Argilla, DIBT (https://huggingface.co/DIBT) is pleased to make available a Russian-language dataset of 500 of the best curated LLM prompts translated to Russian and available for use: https://huggingface.co/datasets/DIBT/MPEP_RUSSIAN.

More to come from the MPEP initiative! Interested in annotating or leading a language team? https://github.com/huggingface/data-is-better-together/tree/main/prompt_translation

osanseviero dvilasuero davanstrien gabrielmbmb


Can someone suggest me a good open source vision model which performs good at OCR?


Excited to share a new project to make journalists’ lives easier when gathering information!

Collecting data like lists, URLs, etc., from websites is not always easy (and sometimes painful). Web scraping requires technical skills that only a handful of people in each newsroom have.

I recently stumbled upon @scrapegraphai, a scraper that does the heavy lifting with AI for the user with a simple prompt in natural language. I asked them if they could integrate the Hugging Face Hub to use open-source models and created a no-code, easy-to-use interface on Gradio.

You can then save time and focus on storytelling!

🔧 How It Works
1. Input Your Prompt and Source URL
2. Click ‘Scrape and Summarize’
3. Receive Summarized Results

👩💻 Get Involved!
This is just the first version of the tool, and it’s pretty basic. I’ve uploaded it to the Journalists on Hugging Face community so we can work together on it. Whether you’re a developer, a data scientist, or a journalist with ideas, you can contribute to this project.

You can also copy this app to your own account or organization to customize it to your needs.

👉 Test the scraper here: https://huggingface.co/spaces/JournalistsonHF/ai-scraper

🤝 Join the Journalists on 🤗 community: https://huggingface.co/JournalistsonHF

evijit osanseviero louisbrulenaudet Emmanuellepicaud British-Rat


We open source a new image inpainting model PowerPaint v2-1:
https://huggingface.co/JunhaoZhuang/PowerPaint-v2-1


My Favourite songs

https://youtu.be/jJvDnYdD8JQ
Katy perry roar, fireworks.

Paula Abdul - Straight Up (Lyrics), Paula Abdul Rush Rush (Lyricshttps://www.youtube.com/watch?v=c_43Qf_d-CA, https://www.youtube.com/watch?v=eqSdQ5gJC7A

BLACKPINK, Selena Gomez - Ice Cream (Lyrics) https://www.youtube.com/watch?v=pcrnh069iBI

Lil Nas X - Old Town Road (Lyrics link not included)

(SUNO.AI IS A SOFTWARE WHERE YOU CAN CREATE YOUR ON MUSIC WITH VOCAL SINGERS WRITE A PROMPT OR ADD YOUR OWN LYRICS)


Google LLM(Multimodal) Medical Foundation Model Summary

1.Med-PaLM: Large language models encode clinical knowledge, https://www.nature.com/articles/s41586-023-06291-2
2.Med-PaLM 2: Towards Expert-Level Medical Question Answering with Large Language Models, http://arxiv.org/abs/2305.09617
3.Med-PaLM M: Towards Generalist Biomedical AI, http://arxiv.org/abs/2307.14334
4.Med-Gemini: Capabilities of Gemini Models in Medicine, https://arxiv.org/abs/2404.18416v2; Advancing Multimodal Medical Capabilities of Gemini, https://arxiv.org/abs/2405.03162


Journey With Me Into The Mind of Large Language Models: Interesting Findings in AnthropicAI's Scaling Monosemanticity paper.

One of the many unknowns with LLMs is the why behind the responses they give - it's unclear why certain responses are chosen over others. Which shows how little we know of what's happening inside these models.

To have a deeper sense of this, they tried Sparse Dictionary Learning on a larger model (Claude 3 Sonnet) - wherein they match patterns of neuron activations (named Features) to human interpretable meanings.

Now Dictionary Learning is a traditional ml technique that identifies recurring patterns of neuron activations across various contexts. Meaning, any internal state of the model can be expressed as a combination of a few active features rather than numerous active neurons.

They scaled up a more effective measure of dictionary learning using a Sparse Autoencoder (SAE). The SAE has an encoder that maps inputs to sparse high-dimensional features via linear transformation &  ReLU, and a decoder that reconstructs inputs from those features.

Three variants (of sizes: ~1M, ~4M & ~34M features) of the SAE were trained and across SAEs, <300 active features/token, >65% variance were explained. With dead features:  ~2% for 1M, 35% for 4M, 65% for 34M SAE. Implying better training could reduce dead features.

Experiments were conducted with these SAEs where they were applied to residual stream activations (RSAs) at the model's middle layer (why? 1. RSAs are smaller than MLP layers = low compute cost, 2. helps tackle "cross-layer superposition" issues - when features are spread across multiple layers instead of being isolated in specific layers, causing interpretation difficulties). These experiments revealed that Scaling Laws can help guide  training of these SAEs.

My favorite of course is the Basic Code Features - where the model attributed meaning to different code syntax elements similar to syntax highlighting in text editors.

victor osanseviero Ramikan-BR louisbrulenaudet


AMMeBa: A Large-Scale Survey and Dataset of Media-Based Misinformation
  In-The-Wild (2405.11697)

80% of fact-checked misinformation claims involve media, with a rise in AI-generated content in 2023, according to a new study, “A Large-Scale Survey and Dataset of Media-Based Misinformation In-The-Wild.” Worth a read for journalists, especially fact-checkers.

TL;DR:
• 📊 135,838 fact checks analyzed
• 📸 80% of these claims involve media
• 🎥 Videos became more common starting in 2022, now more than 60% of fact-checked claims that include media
• 🤖 AI-generated content was rare until Spring of 2023, and then dramatically increased
• 🖼 Image manipulations don’t require complex operations. Most of the time it’s context manipulations

• Read the paper here: https://huggingface.co/papers/2405.11697
• Take a look at the dataset: https://huggingface.co/datasets/academic-datasets/AMMeBa

Thanks @davanstrien  for spotting it!

LeroyDyer osanseviero BoredApeYachtClub Cuiunbo victor


My version of OpenAI's Sora just came out, try out it here https://huggingface.co/spaces/Kvikontent/Openai-Sora

Made with https://huggingface.co/ali-vilab/text-to-video-ms-1.7b, so it isn't official sora

Poojith-Mapakshi KingNish Norod78 Kvikontent


Microsoft Just Launched 3 Powerful Models

1. Phi 3 Medium (4k and 128k): A 14b Instruct tuned models that outperformed big models like Command R+ (104b), GPT 3.5 Pro, Gemini Pro, and is highly competitive with top models such as Mixtral 8x22b, Llama3 70B, and GPT 4.
https://huggingface.co/microsoft/Phi-3-medium-4k-instruct
DEMO: https://huggingface.co/spaces/Walmart-the-bag/Phi-3-Medium

2. Phi 3 Mini Vision 128k: A 4.5 billion-parameter, instruction-tuned vision model that has outperformed models such as Llava3 and Claude 3, and is providing stiff competition to Gemini 1Pro Vision.
https://huggingface.co/microsoft/Phi-3-vision-128k-instruct

3. Phi3 Small (8k and 128k): Better than Llama3 8b, Mixtral 8x7b and GPT 3.5 turbo.
https://huggingface.co/microsoft/Phi-3-small-128k-instruct

DmitryRyumin CocoSun victor KingNish osanseviero RakshitAralimatti Donutanti GPT007

Walmart-the-bag kroonen victor osanseviero RakshitAralimatti


whaaaaaat!?

posts on hf LFG...

my name's kev

and i'm building things with musicgen right now with one simple idea:

removing the text from ai music. musicgen specifically, is more like a little musician itself than other models, and musicians shouldn't have to describe what they want from each other. they jam.

using your audio as input, gary comes in with the fills.

suddenly, you got yourself a lil jam buddy.

max4live device and chrome extension coming, but on hf you can already play with a version that has dope UX thanks to gradio, and is free thanks to zero-gpu-explorers.

https://huggingface.co/spaces/thepatch/zero-gpu-slot-machine

midi2musicgen so you can begin from a starting point of chords/melody

https://huggingface.co/spaces/thepatch/micro-slot-machine

this one begins from a place of drums.

the micro model was trained from scratch by @pharoAIsanders420

first post here, not sure if i'm doing this right...


Timm Leaderboard space here:
https://huggingface.co/spaces/MohamedRashad/timm-leaderboard

Thanks goes to @rwightman for building timm 🤗


Hey it was good meeting you yesterday @MaziyarPanahi 🔥

thanks @mishig for setting this up

Let's make the Hub as useful as possible for the community ❤

victor MaziyarPanahi KingNish lunarflu not-lain Rybens Tkap0176 sueszli theainerd seanmiranda andito pcuenq


Phi-3-Medium just came out! So far it's decent (fails a few riddles 😔), try it for yourself and let me know how it is.

Original Model: https://huggingface.co/microsoft/Phi-3-medium-128k-instruct
Test it out: https://huggingface.co/spaces/Walmart-the-bag/Phi-3-medium *running on ZERO gpu*


Is finding cool models/datasets and uploading them to the hub considered data hoarding ? I can't stop, send help.
https://huggingface.co/collections/1aurent/dinobloom-models-664ba2ab533c5d0c9c4b07e9
https://huggingface.co/collections/1aurent/tia-toolbox-models-654a3c7994d809b4e740105b
https://huggingface.co/collections/1aurent/kaikoai-models-66636c99d8e1e34bc6dcf795
https://huggingface.co/collections/1aurent/medmae-models-6565b4b766a4bce451a32ba7
https://huggingface.co/collections/1aurent/transpath-models-65639c6730a88a2f1d04321d
https://huggingface.co/collections/1aurent/lunit-models-65639b4149f816b7989185b4
https://huggingface.co/collections/1aurent/tcga-brca-ssl-models-65639d42f725fc09726909f1
https://huggingface.co/collections/1aurent/deblurringmim-models-6567446811b2bbd6c2a23071


Loving the new ChatGPT Mac app.

You can now turn a drawing into a working app in less than a minute!

oliguei Taylor658 BoredApeYachtClub DandinPower KingNish

Salama1429 Clausss fluxdielectric BoredApeYachtClub


Empereur : A Python script to automate downloading models from the Hugging Face model hub.

https://github.com/EmpereurPirate/Empereur


Tired of writing Pandas code? 😩

If you are using VS Code, now you can use Data Wrangler from @Microsoft ! 🚀

It will convert your Pandas DataFrame to a rich and interactive user interface to view and analyze your data 📊, show insightful column statistics and visualizations 📈, and automatically generate Pandas code as you clean and transform the data. 🧹🔄

Supports everything you can think of...

- Data view 👀
- Data cleaning 🧼
- Data filtering 🔍
- Data summary/statistics 📊
- Data transformation 🔄
- Data missing values treatment ❓
- Adding new fields ➕

Everything in a simple open-source extension 🌟

https://marketplace.visualstudio.com/items?itemName=ms-toolsai.datawrangler

PS: I love Pandas 🐼, never tired of it... still, this is cool! 😎


A new paper, "Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning," was just published. The approach improves VLMs' decision-making abilities in goal-directed tasks.

This is accomplished with Chain-of-thought (COT) reasoning, which seriously enhances performance. Removing COT reasoning, however, drops effectiveness, highlighting its crucial role.

Check out the paper here: https://arxiv.org/abs/2405.10292


Do you want to improve AI in your language? Here's how you can help.

I'm exploring different AI techniques for an upcoming project in journalism, and I wanted to test a cool idea by @davanstrien, Data is better together, which aims to foster a community of people to create DPO datasets in different languages.

This project gives the opportunity to explore various concepts:
- Direct Preference Optimization (DPO)
- Synthetic data
- Data annotation
- LLM as a judge

1⃣ Take the Aya dataset of human-annotated prompt-completion pairs across 71 languages and filter it to include only those in the language you’re interested in.

2⃣ Use distilabel from Argilla to generate a second response for each prompt and evaluate which response is best.

Basicaly, DPO datasets have a chosen and a rejected responses to a question, which helps align models on specific tasks.  To quote Daniel: "Currently, there are only a few DPO datasets available for a limited number of languages. By generating more DPO datasets for different languages, we can help to improve the quality of generative models in a wider range of languages."

3⃣  Send this dataset and evaluations to the easy-to-use interface to evaluate the evaluations.

This is where you can help. :) You can rate the LLM evaluation of the prompt-responses pairs. For my example, I built a dataset in French. And without wanting to start a debate about homeopathy, the second result is clearly better in the example below!  https://huggingface.co/spaces/fdaudens/demo-aya-dpo-french

The final dataset can be found here: https://huggingface.co/datasets/fdaudens/aya_french_dpo

To contribute to other languages and learn more about synthetic data, you can also produce datasets in the language of your choice! Read more about the project: https://github.com/huggingface/data-is-better-together/blob/main/dpo/README.md

osanseviero dvilasuero gabrielmbmb davanstrien KvrParaskevi Rohitkhatri75436 Lyte constantinSch


RoHM: Robust Human Motion Reconstruction via Diffusion (2401.08570)

🚀🤖🌟 New Research Alert - CVPR 2024! 🌟🤖🚀
📄 Title: RoHM: Robust Human Motion Reconstruction via Diffusion 🔝

📝 Description: RoHM is a diffusion-based approach for robust 3D human motion reconstruction from monocular RGB(-D) videos, effectively handling noise and occlusions to produce complete and coherent motions. This method outperforms current techniques in various tasks and is faster at test time.

👥 Authors: Siwei Zhang et al.

📅 Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA 🇺🇸

📄 Paper: https://huggingface.co/papers/2401.08570

🌐 GitHub Page: https://sanweiliti.github.io/ROHM/ROHM.html
📁 Repository: https://github.com/sanweiliti/RoHM

🚀 Added to the CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers

📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #RoHM #HumanMotionReconstruction #DiffusionModels #3DAnimation #CVPR2024 #DeepLearning #ComputerVision #Innovation

DmitryRyumin spockbuddhaiv KvrParaskevi SalimZ04 letheviet Alkohole


A useful tool for journalists: AutoQuizzer generates a quiz from a URL. You can play the quiz, or let the LLM play it!

https://huggingface.co/spaces/deepset/autoquizzer


PREM-1B-CHAT QUANTIZED INTO Q4
THEN SERVED IN WEBGPU DEMO

OG model https://huggingface.co/premai-io/prem-1B-chat
Q4 model https://huggingface.co/ucalyptus/prem-1B-chat-onnx-q4
WEBGPU demo  https://huggingface.co/spaces/ucalyptus/prem-1B-chat-webgpu


For those with an interest in JA language models, this Llama 3 70B test ablation looks like it is the current strongest publicly released, commercially usable, open model available. A lot of caveats I know, but it also matches `gpt-3.5-turbo-0125`'s JA performance, which is worth noting, and is tuned *exclusively* with the old shisa-v1 dataset (so it's chart position will be very short lived).

https://huggingface.co/shisa-ai/shisa-v1-llama3-70b

https://huggingface.co/datasets/augmxnt/ultra-orca-boros-en-ja-v1


🤗 SDXL Flash

✨ Introducing the new fast model SDXL Flash (Mini), we learned that all fast XL models work fast, but the quality decreases, and we also made a fast model, but it is not as fast as LCM, Turbo, Lightning and Hyper, but the quality is higher. Below you will see the study with steps and cfg.

🚀 Features of mini model:
It weighs less, consumes less video memory and other resources, and the quality has not dropped much.

👑 Our faster than regular model is better in quality than the coolest modern models such as JuggernautXL X, FluentlyXL v4 and others.

SDXL Flash: https://huggingface.co/sd-community/sdxl-flash
SDXL Flash Mini: https://huggingface.co/sd-community/sdxl-flash-mini

Jaward clem KingNish ehristoforu elcrei multimodalart radames dreamdrop-art

ehristoforu KhalidKarami multimodalart radames Xirs dreamdrop-art


@FinancialSupport and I  just released a new version of the Italian LLMs leaderboard https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard
  using the super useful  https://huggingface.co/demo-leaderboard template from @clefourrier.
We’ve evaluated over 50 models (base, merged, fine-tuned, etc.) from:
- Major companies like Meta, Mistral, Google ...
- University groups such as  https://huggingface.co/sapienzanlp or https://huggingface.co/swap-uniba
- Italian Companies like  https://huggingface.co/MoxoffSpA , https://huggingface.co/FairMind  or https://huggingface.co/raicrits
- Various communities and individuals
All models were tested on #Italian benchmarks #mmlu #arc-c #hellaswag, which we contributed to the opensource lm-evaluation-harness library from https://huggingface.co/EleutherAI.
Plus, you can now submit your model for automatic evaluation, thanks to  to  https://huggingface.co/seeweb  sponsored computation.
Curious about the top Italian models? Check out the leaderboard and submit your model!

https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard


Just released a new version of https://huggingface.co/vikhyatk/moondream2 - now supporting higher resolution images (up to 756x756)!

TextVQA score (which measures the model's ability to read and reason about text in images) is up from 53.1 to 57.2 (+7.7%). Other visual question answering and counting benchmark results are up ~0.5%.

clem KeilahElla Shinku Tsardoz not-lain Sylvestre dillfrescott Rybens


BLACK HOLE SDXL Lightning:

Prompt: a photo of a baby dragon
Steps: 4


You are happy that @Meta has open-sourced Llama 3 😃...

So you jump on @HuggingFace Hub to download the new shiny Llama 3 model only to see a few quintillion Llama 3's! 🦙✨

Which one should you use? 🤔

Not all Llamas are created equal! 🦙⚖

An absolutely crazy comparison experiment by Wolfram Ravenwolf (@Wolfram) might answer your question! 🧪🧙♂

- Comprehensive assessment of Llama 3 Instruct 70B and 8B models. 📊
- Tested 20 versions across HF, GGUF, and EXL2 formats. 🔄
- Methodology: The process tested translation capabilities and cross-language understanding, using deterministic generation settings to minimize random factors. Used German data protection training exams to evaluate cross-language understanding. 🌐📝
- Best performance from EXL2 4.5bpw quant, scoring perfect in all tests. 🏆✅
- GGUF 8-bit to 4-bit quants also performed exceptionally. 🌟
- Llama 3 8B unquantized is best in its size class but not as good as 70B quants. 📏🔍
- 1-bit quantizations showed significant quality drops. ⚠⬇

Best models:
- https://huggingface.co/turboderp/Llama-3-70B-Instruct-exl2/tree/5.0bpw
- https://huggingface.co/casperhansen/llama-3-70b-instruct-awq

Blog: https://huggingface.co/blog/wolfram/llm-comparison-test-llama-3

GPT007 clem jensbosseparra not-lain Ramikan-BR


After spending some time practicing tokenization, I have come to realize that the difficulties we face in understanding each other are analogous to the challenges faced by LLMs in processing and interpreting tokens - as in untrained tokens lead to out of distribution qualms.

One could think of how we understand as a process that involves trained tokens (known/learned facts) grappling with prompts/tweets/lessons from someone else. This process is distinct for each person - with unique encoding, decoding, merging and splitting patterns.

This distinction might as well be categorized in gpt levels lol, which brings the question what level of tokenizer are you?  GPT-2, GPT-3, GPT-4 or GPT-4o tokenizer:)

Papers:
- Neural Machine Translation of Rare Words with Subword Units (https://arxiv.org/abs/1508.07909)
- Learning to Compress Prompts with Gist Tokens
(https://arxiv.org/abs/2304.08467)
- Language Models are Few-Shot Learners
(https://arxiv.org/abs/2005.14165)
- Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language Models
(https://arxiv.org/abs/2405.05417)
- Language Models are Unsupervised Multitask Learners

Code:
https://github.com/karpathy/minbpe
https://github.com/openai/tiktoken
https://github.com/openai/gpt-2
https://github.com/google/sentencepiece


StarCoder 15b Instruct v0.1 Space w/ Llama.cpp & Code Completion!

Hey all! I made a little StarCoder space, it's up for fun, have at it, pls make tons of pr's and requests, I love to improve my work for you all!

https://huggingface.co/spaces/MrOvkill/starcoder-15b-instruct


I hardly tested this text generation merged model before releasing it a few months ago. Now it's been receiving attention recently in the form of quants.
https://huggingface.co/grimjim/kunoichi-lemon-royale-v2-32K-7B

Judging from download numbers for GGUF quants, people appear to be using it, and at least one person has a merge formula that incorporated the model.
https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF
https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2
https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2-32K-7B-GGUF


𝗥𝗲𝗮𝗱 𝗙𝘂𝗹𝗹 𝐀𝐫𝐭𝐢𝐜𝐥𝐞:

𝗥𝗲𝗮𝗱 𝗙𝘂𝗹𝗹 𝐀𝐫𝐭𝐢𝐜𝐥𝐞:

𝐒𝐮𝐦𝐦𝐚𝐫𝐲 𝐨𝐟 𝐀𝐫𝐭𝐢𝐜𝐥𝐞- 📝

𝐒𝐮𝐦𝐦𝐚𝐫𝐲 𝐨𝐟 𝐀𝐫𝐭𝐢𝐜𝐥𝐞- 📝

# 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐭𝐨 𝐂𝐫𝐞𝐚𝐭𝐞 𝐒𝐢𝐦𝐢𝐥𝐚𝐫 𝐀𝐈 🧠

# 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐭𝐨 𝐂𝐫𝐞𝐚𝐭𝐞 𝐒𝐢𝐦𝐢𝐥𝐚𝐫 𝐀𝐈 🧠

(𝙉𝙊𝙏𝙀: 𝙎𝙪𝙢𝙢𝙖𝙧𝙮 𝙞𝙨 𝘼𝙄 𝙜𝙚𝙣𝙚𝙧𝙖𝙩𝙚𝙙) ✅

(𝙉𝙊𝙏𝙀: 𝙎𝙪𝙢𝙢𝙖𝙧𝙮 𝙞𝙨 𝘼𝙄 𝙜𝙚𝙣𝙚𝙧𝙖𝙩𝙚𝙙) ✅

Decoding GPT-4'o': Its Mechanisms and Creating Similar AI.

𝗥𝗲𝗮𝗱 𝗙𝘂𝗹𝗹 𝐀𝐫𝐭𝐢𝐜𝐥𝐞: https://huggingface.co/blog/KingNish/decoding-gpt-4o

𝐒𝐮𝐦𝐦𝐚𝐫𝐲 𝐨𝐟 𝐀𝐫𝐭𝐢𝐜𝐥𝐞- 📝
# 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐜𝐬 𝐨𝐟 𝐆𝐏𝐓-𝟒’𝐨’: GPT-4’o’ operates through three main components 🛠

𝟏. 𝐒𝐮𝐩𝐞𝐫𝐂𝐡𝐚𝐭: Integrates image generation, QnA (image, document and video) for diverse interactions.
𝟐. 𝐕𝐨𝐢𝐜𝐞 𝐂𝐡𝐚𝐭: Merges TTS and STT for real-time, human-like audio responses, focusing on human interaction.
𝟑. 𝐕𝐢𝐝𝐞𝐨 𝐂𝐡𝐚𝐭: Utilizes Zero Shot Image Classification to enhance user interaction with visual information.

# 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐭𝐨 𝐂𝐫𝐞𝐚𝐭𝐞 𝐒𝐢𝐦𝐢𝐥𝐚𝐫 𝐀𝐈 🧠

𝟏. 𝐌𝐮𝐥𝐭𝐢𝐌𝐨𝐝𝐚𝐥𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Combines multiple models for a powerful, multifunctional AI.
𝟐. 𝐃𝐮𝐜𝐭 𝐓𝐚𝐩𝐞 𝐌𝐞𝐭𝐡𝐨𝐝: Uses different models or APIs for specific tasks without additional training.

The article provides an in-depth exploration of GPT-4’o’, its functionalities, and methods to create similar AI models. It emphasizes the model’s language support and its innovative approach to human-AI interaction. 💡🌐

(𝙉𝙊𝙏𝙀: 𝙎𝙪𝙢𝙢𝙖𝙧𝙮 𝙞𝙨 𝘼𝙄 𝙜𝙚𝙣𝙚𝙧𝙖𝙩𝙚𝙙) ✅

AIIAR SerialKicked dillfrescott ycjcl868


With slurm figured out and ablations humming along, I though I'd update and post my understanding of the legal status of training data in Japan. It is in general, much clearer in the US: https://huggingface.co/blog/leonardlin/ai-training-data-in-japan


Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations? (2405.05904)

🎭 You picked an LLM for your work but then you find out it hallucinates! 🤖

🤔 Your first thought might be to fine-tune it on more training data.... but should you? 🛠

📜 This is what @Google is exploring in the paper "Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?" 🕵♂

📘 When LLMs undergo supervised fine-tuning with new factual knowledge not present in their initial training data, there is a risk they might "hallucinate" or produce factually incorrect information. 🚨

🔍 The paper investigates how fine-tuning LLMs with new facts influences their ability to leverage pre-existing knowledge and the extent to which they generate errors. 📊

⚙Technical Setup:

🔧 Approach: They introduce a system named SliCK (this stands for Sampling-based Categorization of Knowledge, don't even bother understanding how) to categorize knowledge into four levels (HighlyKnown, MaybeKnown, WeaklyKnown, and Unknown) based on how well the model's generated responses agree with known facts. 🗂

📝 Experimental Setup: The study uses a controlled setup focusing on closed-book QA, adjusting the proportion of fine-tuning examples that introduce new facts versus those that do not. 🧪

👉 Here is the gist of the findings:

🚸 LLMs struggle to integrate new factual knowledge during fine-tuning, and such examples are learned slower than those consistent with the model's pre-existing knowledge. 🐢

📈 As LLMs learn from examples containing new knowledge, their propensity to hallucinate increases. 👻

⏱ Early stopping during training can mitigate the risks of hallucinations by minimizing exposure to unlearned new facts. 🛑

🧠 Training LLMs mostly with known examples leads to better utilization of pre-existing knowledge, whereas examples introducing new knowledge increase the risk of generating incorrect information. 🏗

📄 Paper: https://huggingface.co/papers/2405.05904 📚

Ksgk-fy DynaTechSystems Rybens osanseviero lamhieu kimleang123 moock


Free Guide: How to Fine-Tune and Prompt Engineer LLMs

While some of the most forward-thinking companies in the world are already using LLMs, few organizations have the bandwidth, compute, or money to train foundational models in-house. It’s become much more common to either fine-tune or prompt engineer existing LLMs for unique business needs. In this guide, you’ll learn:

• How to choose between fine-tuning and prompting
• Popular fine-tuning strategies and their trade-offs
• Tasks where fine-tuning excels vs. ones where it doesn’t
• Tips and current best practices for prompt engineering
• And a whole lot more!

Link: https://wandb.ai/site/resources/whitepapers/llm-fine-tuning


Mm what a good time for a new merge!

This is a merge of 6 models that were finetuned on llama3 8b. This has done pretty decent on some coding tasks, for the parameter size. I have looked through models because a lot of people cannot run 33B models (deepseek) for coding.

Original Model: https://huggingface.co/Walmart-the-bag/Llama-3-LizardCoder-8B

GGUF: https://huggingface.co/Walmart-the-bag/Llama-3-LizardCoder-8B-GGUF

Ramikan-BR Joseph717171 DynaTechSystems Rybens osanseviero


New Updates OpenGPT 4o
1. Live Chat  (also known as video chat) (very powerful and fast, it can even identify famous places and persons)
2. Powerful Image Generation

Test and give feedback of New features:
https://huggingface.co/spaces/KingNish/OpenGPT-4o

Future Updates
1. PDF Chat
2.  Human like speech (Using Parler tts expresso)
3. Multilingual support for voice chat

Suggest more features that should be added. 🤗

Edit: Live Chat is now very powerful (than prev)

Gavinssss KingNish victor DynaTechSystems Rybens osanseviero GPT007 hsengiv yahma Lewdiculous ucyang

Rohitkhatri75436 victor SvCy DynaTechSystems osanseviero GPT007 KingNish hsengiv Lewdiculous sk003cs


How many times have you said Pandas is slow and still kept on using it? 🐼💨

Get ready to say Pandas can be fast but it's expensive 😂

🙌 Original Limitations:

💻 CPU-Bound Processing: Traditional pandas operations are CPU-bound (mostly single-threaded😰), leading to slower processing of large datasets.

🧠 Memory Constraints: Handling large datasets in memory-intensive operations can lead to inefficiencies and limitations.

𝌣 Achievements with @nvidia RAPIDS cuDF:

🚀 GPU Acceleration: RAPIDS cuDF leverages GPU computing. Users switch to GPU-accelerated operations without modifying existing pandas code.

🔄 Unified Workflows: Seamlessly integrates GPU and CPU operations, falling back to CPU when necessary.

📈 Optimized Performance: With extreme parallel operation opportunity of GPUs, this achieves up to 150x speedup in data processing, demonstrated through benchmarks like DuckDB.

😅New Limitations:

🎮 GPU Availability: Requires a GPU (not everything should need a GPU)

🔄 Library Compatibility: Currently in the initial stages, all the functionality cannot be ported

🐢 Data Transfer Overhead: Moving data between CPU and GPU can introduce latency if not managed efficiently. As some operations still run on the CPU.

🤔 User Adoption: We already had vectorization support in Pandas, people just didn't use it as it was difficult to implement. We already had DASK for parallelization. It's not that solutions didn't exist

Blog: https://developer.nvidia.com/blog/rapids-cudf-accelerates-pandas-nearly-150x-with-zero-code-changes/

For Jupyter Notebooks:

```python
%load_ext cudf.pandas
import pandas as pd
```

For python scripts:

```shell
python -m cudf.pandas script.py
```


🚀 Stockmark-100b

Stockmark Inc. has developed and released one of Japan's largest commercial-scale Language Models (LLM) with 100 billion parameters, named "Stockmark-LLM-100b". This model significantly reduces hallucinations and provides accurate responses to complex business-related queries. Developed from scratch with a focus on Japanese business data, the model aims to be reliable for high-stakes business environments. It's open-source and available for commercial use.

Key highlights:
- The model reduces hallucinations—incorrect confident responses that AI models sometimes generate.
- Stockmark-LLM-100b can answer basic business questions and specialized queries in industries like manufacturing.
- The model's performance surpasses GPT-4-turbo in accuracy for business-specific queries.
- Evaluation benchmarks (VicunaQA) show high performance.
- Fast inference speed, generating 100-character Japanese text in 1.86 seconds.

https://huggingface.co/stockmark/stockmark-100b
https://huggingface.co/stockmark/stockmark-100b-instruct-v0.1

Detailed press release (in Japanese): https://stockmark.co.jp/news/20240516


llm-jp-eval is currently one of the most widely used benchmarks for Japanese LLMs and is half of WandB's comprehensive Nejumi LLM Leaderboard scoring. I was seeing some weirdness in results I was getting and ended up in a bit of a rabbit hole. Here's my article on evaling llm-jp-eval: https://huggingface.co/blog/leonardlin/llm-jp-eval-eval

I've setup a fork of Lightblue's Shaberi testing framework which uses LLM-as-a-Judge style benchmarks as something probably more representative of real world LLM strength in Japanese. Here's how the new base model ablations are looking:


Introducing StyleTTS 2 detector, an audio classification model to detect StyleTTS 2 vs human-generated content!

Dual-licensed under MIT/Apache 2.0.

Model Weights: https://huggingface.co/mrfakename/styletts2-detector
Spaces: https://huggingface.co/spaces/mrfakename/styletts2-detector

leonardlin KvrParaskevi osanseviero quyet7779


If you're a researcher or developing your own model 👀 you might need to take a look at huggingface's ModelHubMixin classes.
They are used to seamlessly integrate your AI model with huggingface and to save/ load your model easily 🚀

1⃣ make sure you're using the appropriate library version
```
pip install -qU "huggingface_hub>=0.22"
```
2⃣  inherit from the appropriate class
```python
from huggingface_hub import PyTorchModelHubMixin
from torch import nn

class MyModel(nn.Module,PyTorchModelHubMixin):
  def __init__(self, a, b):
    super().__init__()
    self.layer = nn.Linear(a,b)
  def forward(self,inputs):
    return self.layer(inputs)

first_model = MyModel(3,1)
```
4⃣ push the model to the hub (or use save_pretrained method to save locally)
```python
first_model.push_to_hub("not-lain/test")
```
5⃣  Load and initialize the model from the hub using the original class
```python
pretrained_model = MyModel.from_pretrained("not-lain/test")
```

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📚 Introducing the 101 Billion Arabic Words Dataset

🌐 Exciting Milestone in Arabic Language Technology! hashtag#NLP hashtag#ArabicLLM hashtag#LanguageModels

🚀 Why It Matters:
1. 🌟 Large Language Models (LLMs) have brought transformative changes, primarily in English. It's time for Arabic to shine!
2. 🎯 This project addresses the critical challenge of bias in Arabic LLMs due to reliance on translated datasets.

🔍 Approach:
1. 💪 Undertook a massive data mining initiative focusing exclusively on Arabic from Common Crawl WET files.
2. 🧹 Employed state-of-the-art cleaning and deduplication processes to maintain data quality and uniqueness.

📈 Impact:
1. 🏆 Created the largest Arabic dataset to date with 101 billion words.
2. 📝 Enables the development of Arabic LLMs that are linguistically and culturally accurate.
3. 🌍 Sets a global benchmark for future Arabic language research.

🔗 Paper: https://lnkd.in/dGAiaygn
🔗 Dataset: https://lnkd.in/dGTMe5QV

- 🔄 Share your thoughts and let's drive the future of Arabic NLP together!

hashtag#DataScience hashtag#MachineLearning hashtag#ArtificialIntelligence hashtag#Innovation hashtag#ArabicData

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