https://hf.co/chat/assistant/66591a605bfa3e96f8267a32

AI perfect hashtag generator
Create advanced hashtags using our smart AI system.


Octave-X releases their proprietary model Tenzin. For now the access will be given to a select few and will gradually open up. Our model is different from other models in the way it learns. It is not fed heaps of information but starts learning exactly like a human by first studying grammar patterns, then learning then number system, then learning to synthesize words and then sentences and so on. Patience is key with Tenzin. It keeps learning 24/7 with/without user-input. We have decided to keep our model closed-source given the novel algorithms integrated into it along with our novel ideas. Please expect our datacard soon which will be followed by our research paper. You can check us out at https://octave-x.com/


My 1st post on 🤗 I would love to discuss topics related to bias in LLMs:

1) Are researchers and enterprises concerned  about detecting and addressing social bias in the Gen AI applications? If so, what are the existing approaches?

2) Are there trusted and labeled datasets to evaluate bias in LLM generations?

lunarflu osanseviero egorvatulko Ryukijano Shivansh000


🦅 Falcon has landed... again!
And now it not just reads but sees as well 📖👀

Here is a summary of the Falcon-11B-VLM model:

Model Type: Causal decoder-only model 🔄.

Parameters: 11 billion 🌌.

Vision Integration: Uses the pretrained CLIP ViT-L/14 vision encoder with the recently released Falcon2-11B chat-finetuned model and trained with image-text data 🖼📚.

Training: Pretrained on over 5,000 billion tokens from RefinedWeb with curated corpora 📊.

Dynamic Encoding: Enhances perception of fine-grained details in images 🔍.

Training Hardware: 16 A100 80GB GPUs with ZeRO and Flash-Attention 2 🖥.

Tokenizer: Falcon-7B/11B tokenizer 🧩.

Languages Supported: 🌍 Primarily English, with capabilities in German 🇩🇪, Spanish 🇪🇸, French 🇫🇷, Italian 🇮🇹, Dutch 🇳🇱, Romanian 🇷🇴, Czech 🇨🇿, Swedish 🇸🇪, and more. 🗣🌐.

License: Open Source - TII Falcon License 2.0, based on Apache 2.0 📜.

Model: https://huggingface.co/tiiuae/falcon-11B-vlm

osanseviero KingNish louisbrulenaudet lunarflu


You can now train/finetune custom sentence transformer embedding models  using AutoTrain. Read blog: https://huggingface.co/blog/abhishek/finetune-custom-embeddings-autotrain

YaTharThShaRma999 abhishek osanseviero lunarflu MexIvanov not-lain


ChatGPT made Custom GPTs Free for Everyone.

Yes, you can use them but...
with limitations like
You can't use DallE 😥,
You can't make Custom GPTs
And chat limit also😥.
But...
We already have an open-source alternative like Hugging Chat, where you can create your custom assistant, generate, edit images, without any chat limit.

Try both of them from here:
https://chatgpt.com/gpts
https://huggingface.co/chat

and don't forget to Give your review here 👇:

pabloce GPT007 KingNish NHLOCAL lunarflu Darkknight12 nbroad


I ran 580 experiments (yes, 580 🤯) to check if we can quantify data drift's impact on model performance using only drift metrics.

For these experiments, I built a technique that relies on drift signals to estimate model performance. I compared its results against the current SoTA performance estimation methods and checked which technique performs best.

The plot below summarizes the general results. It measures the quality of performance estimation versus the absolute performance change. (The lower, the better).

Full experiment: https://www.nannyml.com/blog/data-drift-estimate-model-performance

In it, I describe the setup, datasets, models, benchmarking methods, and the code used in the project.

KingNish GPT007 umuthopeyildirim lunarflu NePe


Jamba GGUF!

Finally, thanks to the awesome work of the brilliant mind of Github user compilade (https://github.com/compilade) Jamba is now beginning to be supported in llama.cpp (just CPU inference at the moment). So far there are a few different versions I have been able to convert, mainly the Jamba-Bagel, Jamba-Claude, 900M Jamba-Small and a 1B Jamba

https://huggingface.co/collections/Severian/jamba-gguf-665884eb2ceef24c1a0547e0

SashimiSaketoro osanseviero JackCloudman lunarflu


We explore extremely low-weight merger as an alternative to fine-tuning; e.g., weight 1e-4. Merge formula details here:
https://huggingface.co/grimjim/kukulemon-v3-soul_mix-32k-7B

Aurangzeb746 Lewdiculous osanseviero lunarflu


Started a new AI Session: The AI Paper Talk Show 🧠🤖💥

In this episode we went through AnthropicAI's recent interpretability paper "Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet" in which they applied Sparse Dictionary Learning on a larger model (Claude 3 Sonnet) - wherein they match patterns of neuron activations (named Features) to human interpretable meanings.

Check full video here: https://youtu.be/uNz-Ww3_LrU?si=HUm2TWV-rSJ3X4UX

Read More:
https://transformer-circuits.pub/2024/scaling-monosemanticity/

You can also find me:
Twitter: https://x.com/jaykef_
Github: https://github.com/Jaykef


I will be delivering an introductory coding session this Sunday 7Pm gmt+1 time about huggingface, if you are new to HF and don't know where to begin, you are welcome to join us 🤗
📌Place: huggingface discord server
🔗Link :  https://discord.gg/hugging-face-879548962464493619?event=1245406127668203541

lunarflu victor Ramikan-BR KingNish osanseviero Abhinay45 xianbao


Do you need a high-quality dataset to train a custom sentence transformer model? Look no further! I've developed a pipeline that leverages an LLM to create a synthetic dataset of negative and positive sentence pairs based on domain-specific anchors.

Here's what the pipeline offers:
- **Dataset Generation**: Automatically create synthetic sentence pairs
- **Mine hard negatives**: Use an existing embedding model to mine hard negatives
- **Model Training**: Train a model using the latest release of Sentence Transformers.

Check out this collection (https://huggingface.co/collections/davanstrien/sentence-transformers-from-synthetic-data-66571a6133480d1b70066b70) to see an example of what you can achieve with this pipeline. It features a sentence transformer model to detect coding prompt similarities in a @bigcode dataset.

Excited to get started? Find a tutorial here: https://github.com/davanstrien/awesome-synthetic-datasets/tree/main/examples/embedding-datasets.

lunarflu louisbrulenaudet nickprock KingNish GPT007 osanseviero


Do we fully leverage ViT encoders in vision language models?

A new paper (by @HuanjinYao et al) built a dense connector that does it better! https://huggingface.co/spaces/HuanjinYao/DenseConnector-v1.5-8B
https://huggingface.co/collections/HuanjinYao/denseconnector-66500e173fc8c9f05dc98dea

VLMs consist of an image encoder block, a projection layer that projects image embeddings to text embedding space and then a text decoder sequentially connected 📖
This paper explores using intermediate states of image encoder and not a single output 🤩
The authors explore three different ways of instantiating dense connector: sparse token integration, sparse channel integration and dense channel integration. (see paper on how they do it https://huggingface.co/papers/2405.13800)

They explore all three of them integrated to LLaVA 1.5 and found out each of the new models are superior to the original LLaVA 1.5 🥹 I tried the model and it seems to work very well. As part of the release, the authors have released various ckpts based on different decoders (Vicuna 7/13B and Llama 3-8B) that you can find in the collection 🤗


WorkerSafetyQAEval: A new benchmark to evaluate worker safety domain question and answering

Happy to share a new benchmark on question and answers for worker safety domain. The benchmark and leaderboard is available at
https://huggingface.co/datasets/codelion/worker-safety-qa-eval

We evaluate popular generic chatbots like ChatGPT and HuggingChat on WorkerSafetyQAEval and compare it with a domain specific RAG bot called Securade.ai Safety Copilot - https://huggingface.co/spaces/codelion/safety-copilot  It highlights the importance of having domain specific knowledge for critical domains like worker safety that require high accuracy. Securade.ai Safety Copilot achieves ~97% on the benchmark setting a new SOTA.

You can read more about the Safety Copilot on https://securade.ai/blog/how-securade-ai-safety-copilot-transforms-worker-safety.html


Phased Consistency Model

https://huggingface.co/papers/2405.18407

The consistency model (CM) has recently made significant progress in accelerating the generation of diffusion models. However, its application to high-resolution, text-conditioned image generation in the latent space (a.k.a., LCM) remains unsatisfactory. In this paper, we identify three key flaws in the current design of LCM. We investigate the reasons behind these limitations and propose the Phased Consistency Model (PCM), which generalizes the design space and addresses all identified limitations. Our evaluations demonstrate that PCM significantly outperforms LCM across 1--16 step generation settings. While PCM is specifically designed for multi-step refinement, it achieves even superior or comparable 1-step generation results to previously state-of-the-art specifically designed 1-step methods. Furthermore, we show that PCM's methodology is versatile and applicable to video generation, enabling us to train the state-of-the-art few-step text-to-video generator.

lunarflu DominusZeikyu1998 AtAndDev nfrumkin


You are happy that @mistralai is releasing a new model 😊

You become even more happy to see it's a completely new coding model 😄

Then you become sad because the model is licensed under MNLP 😔

Before we talk about MNLP, here is the gist of the model:

🏷Name: Codestral (Code + Mistral 😂)

🚀 22B parameters

🌐 Supports 80 programming languages (including Python, Java, C, C++, bash, swift, and more)

🏆 Outperforms Llama 3 70B and Code Llama 70B on HumanEval and MBPP

🏆 Outperforms DeepSeek Coder 33B on HumanEval

📜 32K context window (longer than Llama 3, DeepSeek, or Code Llama)

🤖 Supports both code assistant and code completion use cases

More details: https://mistral.ai/news/codestral/

https://huggingface.co/mistralai/Codestral-22B-v0.1

Now what's MNLP? It's a non-commercial license for Mistral models that Codestral is released under! More here: https://mistral.ai/news/mistral-ai-non-production-license-mnpl/

Don't be sad... 😃 There is another model that's open source and actually gives better performance on HumanEval: https://huggingface.co/Bin12345/AutoCoder


What We Learned from a Year of Building with LLMs

It's a nice perspective outlined in here.

“When a measure becomes a target, it ceases to be a good measure.”

— Goodhart’s Law

https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/


https://huggingface.co/spaces/nroggendorff/epicrealismxl is growing so fast! thanks guys! 🤗


😐 Hello, there are a couple of interesting things. The first is that I will soon release several pretty cool SDXL models, the second is a little sad, I conducted long-term tests of training and merging of XL models and realized that XL will not improve soon, the architecture will not allow us to continue pushing realism and other interesting things into it, the entire community has brought XL closer to the maximum ideal on its architecture.

dreamdrop-art YaTharThShaRma999 monsoon-nlp yuri1 lunarflu louisbrulenaudet osanseviero TerraNull ehristoforu


Relightable Gaussian Codec Avatars (2312.03704)

🚀🎭🌟 New Research Alert - CVPR 2024 (Avatars Collection)! 🌟🎭🚀
📄 Title: Relightable Gaussian Codec Avatars 🔝

📝 Description: Relightable Gaussian Codec Avatars is a method for creating highly detailed and relightable 3D head avatars that can animate expressions in real time and support complex features such as hair and skin with efficient rendering suitable for VR.

👥 Authors: @psyth, @GBielXONE02, Tomas Simon, Junxuan Li, and @giljoonam

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

📄 Paper: https://huggingface.co/papers/2312.03704

🌐 GitHub Page: https://shunsukesaito.github.io/rgca/

🚀 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

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

🔍 Keywords: #3DAvatars #RealTimeRendering #RelightableAvatars  #3DModeling #VirtualReality #CVPR2024 #DeepLearning #ComputerGraphics #ComputerVision #Innovation #VR

DmitryRyumin songperformer SiDReD lunarflu osanseviero adamelliotfields


Introducing Image Generator Pro
https://huggingface.co/spaces/KingNish/Image-Gen-Pro

It is Expert in Text to Image generation, Sequential Image generation or Image Editing.

Examples:

victor pabloce lunarflu KingNish osanseviero Abhinay45


# 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

KingNish Abhaykoul lunarflu oproprioaquino Abhinay45 Aarifkhan


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:)

ThatTechChick dillfrescott lunarflu Despina louisbrulenaudet afrideva not-lain

KrisBcn GPT007 dillfrescott lunarflu not-lain Abhinay45

umair894 lunarflu JackMM KingNish not-lain


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

rwightman YaTharThShaRma999 osanseviero Bagus lunarflu Norod78 clem mohres


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!

Ramikan-BR lunarflu Joseph717171 Tonic KingNish Abhinay45


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

https://huggingface.co/BhashaAI/ViLaH

lunarflu osanseviero victor restufiqih apehex


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

toshas osanseviero lunarflu DmitryRyumin r3gm radames taufiqdp OzzyGT dn6 GPT007 jasstionzyf louisbrulenaudet

toshas osanseviero lunarflu DmitryRyumin radames

toshas osanseviero lunarflu radames OzzyGT


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

sayakpaul drbh not-lain damerajee KingNish thliang01 fdaudens DmitryRyumin radames poiqazwsx 3thn Elizezen Jofthomas boapps teowu JaCrispy35 beta3 clem sergiopaniego andrewrreed taufiqdp nbroad dominguezdaniel dillfrescott ZanMax ArthurZ louisbrulenaudet regisss BramVanroy MaziyarPanahi pcuenq TokenBender codelion Norm evijit VictorSanh maxsonderby alielfilali01 Abhinay45 TechxGenus emanuelevivoli Forbu14 Nymbo

KingNish not-lain Taylor658 radames poiqazwsx 3thn clem dillfrescott ArthurZ regisss MaziyarPanahi pcuenq codelion alielfilali01

clem mrfakename Locutusque dillfrescott ArthurZ regisss moock super-cinnamon MaziyarPanahi pcuenq codelion evijit alielfilali01 kramp


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

davanstrien dvilasuero burtenshaw gabrielmbmb lunarflu victor osanseviero sanu-kurien louisbrulenaudet

dvilasuero lunarflu osanseviero VictorSanh


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!

xianbao not-lain LucasThil pabloce KingNish lunarflu AkimfromParis seanmiranda theainerd rsandee7 rbgo

pabloce lunarflu seanmiranda theainerd ProtonJuice mrprajesh adrivazo


‼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

osanseviero alvarobartt lunarflu bourdoiscatie philschmid mattmdjaga DmitryRyumin not-lain gabrielmbmb cschroeder Ali-Khaled wonderinos urchade nickprock SamuelNthoroane Rybens vaclavkosar Abhinay45

louisbrulenaudet alvarobartt lunarflu bourdoiscatie philschmid gabrielmbmb urchade MexIvanov


✨ 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

nsarrazin SivilTaram clem tomaarsen merve thliang01 radames 1ucky1uke Ramikan-BR samusenps

KingNish clem tomaarsen merve radames Ramikan-BR Stonedkick

ijohn07 merve radames Ramikan-BR Stonedkick samusenps


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 🤗

osanseviero lunarflu mattmdjaga taufiqdp kraviart


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

DmitryRyumin fdaudens KvrParaskevi ambientocclusion wyc osanseviero CharileYou seriousran katoernest HugoArraes


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

Tonic rishabh063 KingNish SixOpen m-aliabbas1 ajibawa-2023 KvrParaskevi seyf1elislam clem osanseviero louisbrulenaudet lunarflu mattmdjaga

Tonic thliang01 clem osanseviero lunarflu Taylor658


🇫🇷
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


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


I have been seeing a specific type of AI hype more and more, I call it, releasing research expecting that no one will ever reproduce your methods, then overhyping your results. I test the methodology of maybe 4-5 research papers per day. That is how I find a lot of my research. Usually, 3-4 of those experiments end up not being reproduceable for some reason. I am starting to think it is not accidental.

So, I am launching a new series where I specifically showcase a research paper by reproducing their methodology and highlighting the blatant flaws that show up when you actually do this. Here is Episode 1!

https://www.youtube.com/watch?v=JLa0cFWm1A4


New Droppings🥳

😶🌫Collection: https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be

🥳Demo Here: https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC  with more than 100+ Flux LoRA's

🪨Fluid Dramatic Neon: https://huggingface.co/prithivMLmods/Castor-Dramatic-Neon-Flux-LoRA
🪨Past & Present Blend: https://huggingface.co/prithivMLmods/Past-Present-Deep-Mix-Flux-LoRA
🪨Tarot Cards Refreshed Themes: https://huggingface.co/prithivMLmods/Ton618-Tarot-Cards-Flux-LoRA
🪨Amxtoon Character Mix Real-Anime: https://huggingface.co/prithivMLmods/Ton618-Amxtoon-Flux-LoRA
🪨Epic Realism Flux v1: https://huggingface.co/prithivMLmods/Ton618-Epic-Realism-Flux-LoRA
🪨Mock-up Textures: https://huggingface.co/prithivMLmods/Mockup-Texture-Flux-LoRA
.
.
.
@prithivMLmods 🤗

John6666 prithivMLmods benhaotang TuringsSolutions JackCloudman AtAndDev rdrede kimleang123 xumijiezi622 KingNish hypergod ai4life44 Rsln s3nh Ngrthm

Goekdeniz-Guelmez AtAndDev Aryanne rdrede kimleang123 darksfx hypergod s3nh Ngrthm

ijohn07 rdrede darksfx hypergod s3nh Ngrthm


Excited to see my weird https://huggingface.co/datasets/davanstrien/ufo-ColPali dataset featured in a video by @sabrinaesaquino!

The video covers using ColPali with Binary Quantization in Qdant to accelerate retrieval. 2x speed up with no performance drop in results 🛸

Video: https://youtu.be/_A90A-grwIc?si=oB3JAhJG8VQUZGLz
Blog post: https://danielvanstrien.xyz/posts/post-with-code/colpali-qdrant/2024-10-02_using_colpali_with_qdrant.html


NEW RELEASE! Shining Valiant 2 for Llama 3.1 70b is here!

- Trained on high quality science-instruct, complex queries, and general chat data!
- Uses our newest datasets, ALL open-sourced for everyone to use!

GET SV2 70B: https://huggingface.co/ValiantLabs/Llama3.1-70B-ShiningValiant2

- Find the SV datasets here, including the expanded version of our science-instruct dataset:
  - https://huggingface.co/datasets/sequelbox/Celestia
  - https://huggingface.co/datasets/sequelbox/Spurline
  - https://huggingface.co/datasets/sequelbox/Supernova
- SV2 8b and 3b will be updated with the new datasets soon!

Enjoy! :)


Cybertron is back:

We released today a newest version of Cybertron: V4 based on Qwen2.5 7B and trained on MagPie. Scoring #1 LLM on 7B & 8B class.

The model hasn't go thru DPO, so the weights are in good shape to welcome further training sessions and optimizations.
Enjoy it in the hub as usual:
https://huggingface.co/fblgit/cybertron-v4-qw7B-MGS


FLUX De-Distilled and Anti-Bleeding Fine-Tuning / DreamBooth & LoRA Training Experiments

Also Testing CFG Impact for Stylized Images on base FLUX DEV model

Full size grids and full details shared here : https://www.patreon.com/posts/114969137

The aim is finding a workflow that FLUX can be trained for multiple concepts / subjects without bleeding / mixing


⚡ LLMs do a good job at NER, but don't you want to do learn how to do more with less?

Go from 🐢 -> 🐇

If you want a small model to perform well on your problem, you need to fine-tune it.

Bootstrap with a teacher model.

Correct potential mistakes to get high-quality data.

Fine-tune your student model

Go more accurate and more efficient.

Free signup: https://lu.ma/zx2t7irs

prithivMLmods John6666 janinakeno NeuroSpaceX


🚀 Exploring Topic Modeling with BERTopic 🤖

When you come across an interesting dataset, you often wonder:
Which topics frequently appear in these documents? 🤔
What is this data really about? 📊

Topic modeling helps answer these questions by identifying recurring themes within a collection of documents. This process enables quick and efficient exploratory data analysis.

I’ve been working on an app that leverages BERTopic, a flexible framework designed for topic modeling. Its modularity makes BERTopic powerful, allowing you to switch components with your preferred algorithms. It also supports handling large datasets efficiently by merging models using the BERTopic.merge_models approach. 🔗

🔍 How do we make this work?
Here’s the stack we’re using:

📂 Data Source ➡ Hugging Face datasets with DuckDB for retrieval
🧠 Text Embeddings ➡ Sentence Transformers (all-MiniLM-L6-v2)
⚡ Dimensionality Reduction ➡ RAPIDS cuML UMAP for GPU-accelerated performance
🔍 Clustering ➡ RAPIDS cuML HDBSCAN for fast clustering
✂ Tokenization ➡ CountVectorizer
🔧 Representation Tuning ➡ KeyBERTInspired + Hugging Face Inference Client with Meta-Llama-3-8B-Instruct
🌍 Visualization ➡ Datamapplot library
Check out the space and see how you can quickly generate topics from your dataset: https://huggingface.co/spaces/datasets-topics/topics-generator

Powered by @MaartenGr  - BERTopic


🔍 NYT leveraged AI to investigate election interference by analyzing 400+ hours of recorded meetings - that's 5M words of data!

AI spotted patterns, humans verified facts. Every AI-flagged quote was manually verified against source recordings. Really appreciate that they published their full methodology - transparency matters when using AI in journalism.

A perfect blend of tech & journalism.

 The future of journalism isn't robots replacing reporters - it's AI helping humans process massive datasets more efficiently. Sometimes the most powerful tech solutions are the least flashy ones.

Read the article: https://www.nytimes.com/interactive/2024/10/28/us/politics/inside-the-movement-behind-trumps-election-lies.html?unlocked_article_code=1.Vk4.ucv9.dbHVquTQaf0G&smid=nytcore-ios-share

Aurelien-Morgan John6666 io2 OmbelineM wsuff KhalilGuetari owao


🚀 Exciting update! You can now compare multiple models side-by-side with the Hugging Face Open LLM Comparator! 📊

https://huggingface.co/spaces/open-llm-leaderboard/comparator

Dive into multi-model evaluations, pinpoint the best model for your needs, and explore insights across top open LLMs all in one place. Ready to level up your model comparison game?

awacke1 reach-vb Manel alielfilali01 octoplanet averoo


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


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