Every time a new model is released that is topping 10+ leaderboards on 50+ benchmarks... 🚀

My brain goes... I will wait for the LMSYS Chatbot Arena results! 🤔

User-facing evaluation, such as Chatbot Arena, provides reliable signals but is costly and slow. 🐢

Now we have MixEval, a new open benchmark with a 96% correlation to LMSYS Chatbot Arena and Human preferences. 🎯

It comes with MixEval (4k samples) and MixEval Hard (1k samples) 📊

Can use GPT-3.5-Turbo or any other open-source models as Parser/Judge 🤖

It takes less than 6% of the time and cost of MMLU 💸

As expected:
In open models: Qwen2 72B >> Llama 3 70B >> Mixtral 8x7B 🔝
In Closed Models: GPT-4o >> Claude 3 Opus >> Gemini Pro 🔒

Leaderboard: https://mixeval.github.io/  📈


I just published Sentence Transformers v3.0.1: the first patch release since v3 from last week. It introduces gradient checkpointing, pushing model checkpoints to Hugging Face while training, model card improvements and fixes. Details:

1⃣ Gradient checkpointing allows for much less memory usage at a cost of ~20% training speed. Seems to allow for higher batch sizes, which is quite important for loss functions with in-batch negatives.
2⃣ You can specify `args.push_to_hub=True` and `args.hub_model_id` to upload your model checkpoints to Hugging Face while training. It also uploads your emissions (if codecarbon is installed) and your Tensorboard logs (if tensorboard is installed)
3⃣ Model card improvements: improved automatic widget examples, better tags, and the default of "sentence_transformers_model_id" now gets replaced when possible.
4⃣ Several evaluator fixes, see release notes for details.
5⃣ Fixed a bug with MatryoshkaLoss throwing an error if the supplied Matryoshka dimensions are ascending instead of descending.
6⃣ Full Safetensors support; even the uncommon modules can now save and load "model.safetensors" files: no more pickle risks.

Check out the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.1

And let me know what kind of features you'd like to see next! I have some plans already (ONNX, Sparse models, ColBERT, PEFT), but I don't yet know how I should prioritize everything.


Evaluate RAG using Open Source from HuggingFace using BeyondLLM

```
# pip install beyondllm
# pip install huggingface_hub
# pip install llama-index-embeddings-fastembed

from beyondllm.source import fit
from beyondllm.embeddings import FastEmbedEmbeddings
from beyondllm.retrieve import auto_retriever
from beyondllm.llms import HuggingFaceHubModel
from beyondllm.generator import Generate

import os
from getpass import getpass
os.environ['HUGGINGFACE_ACCESS_TOKEN'] = getpass("Enter your HF API token:")

data = fit("RedHenLab_GSoC_Tarun.pdf",dtype="pdf")
embed_model = FastEmbedEmbeddings()
retriever = auto_retriever(data=data,embed_model=embed_model,type="normal",top_k=3)
llm = HuggingFaceHubModel(model="mistralai/Mistral-7B-Instruct-v0.2")
pipeline = Generate(question="what models has Tarun fine-tuned?",llm=llm,retriever=retriever)

print(pipeline.call()) # Return the AI response
print(pipeline.get_rag_triad_evals())
```

GitHub: https://github.com/aiplanethub/beyondllm

Don't forget to ⭐ the repo

fdaudens radames orkut simkjels umutphp victor thesven sadra-barikbin


📢 Releasing the Chain-of-Thought (CoT)-tuned 🔥 FlanT5-xl (3B) for Target Sentiment Analysis (TSA) on english texts.
💡 The main reason for adopting this model or smaller version (large and base) are as follows:
✅ 1. Reasoning in sentiment-analysis in zero-shot-learning mode significantly underperforms the fine-tuned FlanT5.
✅ 2. This model showcases top 1 🏆 on the RuSentNE-2023 competitions: https://codalab.lisn.upsaclay.fr/competitions/9538
✅ 3. Easy colab for frameworkless lauch and experiments 🧪 https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/FlanT5_Finetuned_Model_Usage.ipynb

You may find more on the model card, while the fine-tuning statistics per each model size is shown in attachment.

Model: https://huggingface.co/nicolay-r/flan-t5-tsa-thor-xl
Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101


Hello!

I am studying PyTorch, and I made something that converged really well for something this simplistic. It isn't masterful, but i'd welcome feedback, improvements, suggestions, anything. Tell me it sucks and to take it down, I will, just wanted to share what i've spent the last 2 days crying to figure out.

https://colab.research.google.com/gist/SMeyersMrOvkill/625371e1816afb2163bdc4194ba74e93/scratchpad.ipynb


I made Tenzin public. One use-case at least to predict stock market prices for high-frequency trading. Would love to see the response as well as feedback you have for us. Please understand that this only represents 5% of the codebase of Tenzin 1.0. We will share more models and use-cases based on the feedback we receive along with keeping in mind AI safety and ethics.

Have fun and go and make some money :)


They: you need ground truth to measure performance! 😠

NannyML: hold my beer...


🤗 Hello, I have great news! FluentlyXL Final is finally here, the final release of the FluentlyXL model series. We've improved the overall aesthetics, lighting, and more.

🦾 Model on HF: https://huggingface.co/fluently/Fluently-XL-Final
🥏 Model on CivitAI: https://civitai.com/models/324891
🎆 Playground: https://huggingface.co/spaces/fluently/Fluently-Playground

ehristoforu lunarflu YaTharThShaRma999 LucasFlorentino taufiqdp radames ywlee88 XuehangCang victor louisbrulenaudet jaydip-tss KingNish erickdp rocca ajibawa-2023 dillfrescott Tanvir1337 faisalbsl21 dreamdrop-art ijohn07

lunarflu YaTharThShaRma999 ywlee88 XuehangCang ehristoforu KingNish Ramikan-BR rocca GPT007 dreamdrop-art


ChemLLM: A Chemical Large Language Model (2402.06852)

The First Multimodal Language Model dedicated for Chemistry.
Demo: https://v.chemllm.org/
Finetune based on ChemLLM-20B and InterViT-6B on MMChemExam and ChemOCR Datasets (coming soon...)
https://huggingface.co/AI4Chem/ChemVLM-26B
https://huggingface.co/papers/2402.06852


Hugging Face in your spreadsheet?

Because spreadsheets can be incredibly useful for journalists, I created this little project yesterday evening. Handy for prompting, extraction, classification, translation...

Ping me if you’re interested in trying it out!


⚙ Prompt Optimization with Haystack and DSPy

Experimental notebook: 🧪📓 https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/prompt_optimization_with_dspy.ipynb

When building applications with LLMs, writing effective prompts is a long process of trial and error. 🔄
Often, if you switch models, you also have to change the prompt. 😩
What if you could automate this process?

💡 That's where DSPy comes in - a framework designed to algorithmically optimize prompts for Language Models.
By applying classical machine learning concepts (training and evaluation data, metrics, optimization), DSPy generates better prompts for a given model and task.

Recently, I explored combining DSPy with the robustness of Haystack Pipelines.

Here's how it works:
▶ Start from a Haystack RAG pipeline with a basic prompt
🎯 Define a goal (in this case, get correct and concise answers)
📊 Create a DSPy program, define data and metrics
✨ Optimize and evaluate -> improved prompt
🚀 Build a refined Haystack RAG pipeline using the optimized prompt

victor Jofthomas lunarflu aplrat ijohn07 ajibawa-2023 oneiroid


The application of  Phi-3-small-8k-instruct  for reasoning  in  Target Sentiment Analysis (TSA), in a  zero-shot-learning mode.  Comparing with the other 7B vendors, the key takeaways are as follows:
✅ 1.  At the moment this model on the top 🎉 of the 7B sized versions for texts translated in English (🇺🇸) by surpassing Mistral-7B-v0.3 and LLaMA-3-8B 🔥 (Figure 1)
✅ 2. It remains similar to 7B alternatives in original non-english texts (🇷🇺),  however show confidence in sentiment presence among other 7B alternatives (checkout F1(PN0) results in Figure 2)

In comparison with its mini (3B) brother  Phi-3-mini, the small (7B) version showcases a huge step in reasoning capabilities 🔥

Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Model: https://huggingface.co/microsoft/Phi-3-small-8k-instruct
Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101


🔬 This paper introduces Fusion Intelligence (FI), a novel approach integrating the adaptive behaviors of natural organisms 🐝(Bees!)🐝 with AI's computational power.

Paper:
Fusion Intelligence: Confluence of Natural and Artificial Intelligence for Enhanced Problem-Solving Efficiency (2405.09763)
https://arxiv.org/pdf/2405.09763

Key Takeaways:
*    Fusion Intelligence (FI): Combines natural organism efficiency with AI's power. 🌟
*   Hybrid Approach: Integrates natural abilities with AI for better problem-solving. 🧠🤖
*   Agricultural Applications: Shows a 50% improvement in pollination efficiency. 🐝🌼
*   Energy Efficiency: Consumes only 29.5-50.2 mW per bee, much lower than traditional methods. ⚡
*   Scalability: Applicable to fields like environmental monitoring and search and rescue. 🌍🔍
*   Non-Invasive: Eliminates the need for invasive modifications to biological entities. 🌱

This research offers a new approach for those interested in sustainable AI solutions. By merging biology with AI, (FI) aims to create solutions for a variety of challenges.


V-Express: 1-Click AI Avatar Talking Heads Video Animation Generator - D-ID Alike - Free Open Source

Full Windows YouTube Tutorial : https://youtu.be/xLqDTVWUSec

Ever wished your static images could talk like magic? Meet V-Express, the groundbreaking open-source and free tool that breathes life into your photos! Whether you have an audio clip or a video, V-Express animates your images to create stunning talking avatars. Just like the acclaimed D-ID Avatar, Wav2Lip, and Avatarify, V-Express turns your still photos into dynamic, speaking personas, but with a twist—it's completely open-source and free to use! With seamless audio integration and the ability to mimic video expressions, V-Express offers an unparalleled experience without any cost or restrictions. Experience the future of digital avatars today—let's dive into how you can get started with V-Express and watch your images come alive!

1-Click V-Express Installers Scripts ⤵
https://www.patreon.com/posts/105251204

Requirements Step by Step Tutorial ⤵
https://youtu.be/-NjNy7afOQ0

Official Rope GitHub Repository Free To Install and Use ⤵
https://github.com/tencent-ailab/V-Express

SECourses Discord Channel to Get Full Support ⤵
https://discord.com/servers/software-engineering-courses-secourses-772774097734074388


The Coachella of Computer Vision, CVPR, is right around the corner. In anticipation of the conference, I curated a dataset of the papers.

I'll have a technical blog post out tomorrow doing some analysis on the dataset, but I'm so hyped that I wanted to get it out to the community ASAP.

The dataset consists of the following fields:

 - An image of the first page of the paper
 - `title`: The title of the paper
 - `authors_list`: The list of authors
 - `abstract`: The abstract of the paper
 - `arxiv_link`: Link to the paper on arXiv
 - `other_link`: Link to the project page, if found
 - `category_name`: The primary category this paper according to [arXiv taxonomy](https://arxiv.org/category_taxonomy)
 - `all_categories`: All categories this paper falls into, according to arXiv taxonomy
 - `keywords`: Extracted using GPT-4o

Here's how I created the dataset 👇🏼

Generic code for building this dataset can be found [here](https://github.com/harpreetsahota204/CVPR-2024-Papers).

This dataset was built using the following steps:

- Scrape the CVPR 2024 website for accepted papers
- Use DuckDuckGo to search for a link to the paper's abstract on arXiv
- Use arXiv.py (python wrapper for the arXiv API) to extract the abstract and categories, and download the pdf for each paper
- Use pdf2image to save the image of paper's first page
- Use GPT-4o to extract keywords from the abstract

https://huggingface.co/datasets/Voxel51/CVPR_2024_Papers


🌍 As we all know, Planet Earth is undergoing an unprecedented climate crisis, almost totally due to human activities: we haven't got much time left before it's too late to take action, and one of the key fields where we need to urgently operate are climate-aware financial investments...
🤖 ... And that's where AI comes into the play: we can indeed try to leverage, tweak and expand its knowledge in the field to extract valuable climate-aware solutions.
🤗 I tried to make something alike: exploiting https://huggingface.co/datasets/climatebert/tcfd_recommendations as knowledge base, Qdrant Cloud as vector store service and https://huggingface.co/microsoft/Phi-3-mini-128k-instruct as LLM (provided via API from https://huggingface.co/spaces/eswardivi/Phi-3-mini-128k-instruct by @eswardivi), I built an AI assistant to help you find climate-oriented solutions for your investments, companies, or simply for your everyday life🎒.
Find it here: https://huggingface.co/spaces/as-cle-bert/cLLiMateChat

GitHub: https://github.com/AstraBert/qdrant-ai-chat
Website: https://astrabert.github.io/qdrant-ai-chat/

Be kind to our Planet, we only got one💚

(Shout-outs to @JohnSmith9982 whose https://huggingface.co/spaces/JohnSmith9982/small_and_pretty Gradio theme was used to build my application🚀)

PS: 🌱Curious of knowing what is your carbon footprint? Head over to this ML-backed HF Space I built to discover it: https://huggingface.co/spaces/as-cle-bert/carbon-footprint-predictor

Taylor658 lunarflu neovalle LucasFlorentino Severian Ramikan-BR louisbrulenaudet


THUDM has released GLM-4V-9B and it's.. chatty! 😂
I asked it to describe my favorite Howl's Moving Castle scene and here's how it went 👇🏻

joke aside it seems to outperform the previous VLMs. however the license isn't open-source 📈
model repo: https://huggingface.co/THUDM/glm-4v-9b
a community member has built a demo: https://huggingface.co/spaces/vilarin/VL-Chatbox


I decided to play around with FluentlyXL v4 😉

👉 Model: https://huggingface.co/fluently/Fluently-XL-v4
✨ Playground: https://huggingface.co/spaces/fluently/Fluently-Playground

ehristoforu dreamdrop-art S1m0neAI lunarflu LucasFlorentino TravelingMan s3nh victor Ramikan-BR cbensimon louisbrulenaudet ifmain

lunarflu Ramikan-BR cbensimon Westis dreamdrop-art


📢 The most recent Mistral-7B-Instruct-v0.3 release showcases more robust advances in zero-shot-mode mode on Target Sentiment Analysis.
🧪  We experiment with the original texts (🇷🇺 ) and their translated version into English (🇺🇸).
💡 The key takeaways on the expectation from this model are as follows:
✔ 1. On translated texts into English (🇺🇸), it surpasses LLaMA-3 and and nearly touches MOE Mixtral 8x7B versions being quite precise by F1 across all the classes by F1(PN)
✔2. On orignal texts (🇷🇺) It slightly surpasses LLaMA-3 by F1(PN) by being less tolerant in neutral (F1(PN0)).  Using larger versions (Mixtral) are still preferable choice for reasoning 🧠 in non-eng texts.
✔3. You can clearly see the difference between 7B version and MOE (figure 3) by F1(PN0)
Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Model: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3
Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101

victor kristaller486 osanseviero lunarflu


A great vision language benchmark: MM-UPD evaluates how model responds to unsolvable problems 🤓
LLaVA 1.6 is outperforming proprietary VLMs, making it a very robust choice for production!

It is now hosted as a leaderboard https://huggingface.co/spaces/MM-UPD/MM-UPD_Leaderboard 🏆💕

victor Ramikan-BR osanseviero aryansinghtech jeremy-london lunarflu taufiqdp AtsuMiyai


Hello, Vision World!
https://huggingface.co/AI4Chem/ChemLLM-20B-Chat-DPO
https://huggingface.co/papers/2402.06852

qq8933 victor KingNish osanseviero Taylor658 louisbrulenaudet abdesBen lunarflu


Can anyone see my post on🤗?
Reply with 🤗

turiabu wang-mei-nuan dani-garcia CookieMaster qq8933 zzffss victor Abru BoredApeYachtClub KingNish Hev832 sa8 Clausss GPT007 osanseviero sergiopaniego EnesCMLK thesven Snail921 taewan2002 Noclou qhduan takeraparterer s3nh actuallyastarfish nicoboss shtefarn Rybens Samoed urchade EveryPizza mrdbourke leafspark qnixsynapse giodeleo


🧠 Have you ever heard of neurons running as a computer?

💻 If you are curious about the so-called "brainoware", a hardware built upon a brain organoid and used for AI and ML tasks, you may want to read my latest 🤗 article: https://huggingface.co/blog/as-cle-bert/brain-next-generation-neurons

💡Enjoy!

Taylor658 not-lain victor osanseviero lunarflu

Joseph717171 louisbrulenaudet KingNish adamelliotfields lunarflu


Remember when @Microsoft  released Phi-3 models... 🤔

Yup, the ones that had 🦙Llama 3 8B beat on MMLU using 3.8B parameters! 🏆

Now they are on the LMSYS Chatbot Arena Leaderboard! 📊📈

Medium(14B) ranks near GPT-3.5-Turbo-0613, but behind Llama 3 8B. 📉

Phi-3 Small(7B) is close to Llama-2-70B, and Mistral fine-tunes. 📊

What about the Phi-3 Mini(3.8B), that was giving Llama 3 8B a run for its money on MMLU? It gets an arena score of 1037 (#73) against 1153 (#22) of Llama 3 8B 🤼

Looks like there is a struggle here between perplexity and inherent knowledge! 🤔

And Microsoft picked knowledge with high perplexity 🧠

Now I am even more intrigued: what is @Meta feeding its 🦙 Llamas?🌾

🏆 Leaderboard: https://chat.lmsys.org/?leaderboard


One shot evaluations is hard. That is honestly what I learnt throughout the last couple of weeks trying to make imgsys.org data more and more relevant. There is just so much diversity in these models that saying one is better than other one even at a particular domain is impossible.

If you have any suggestions on how we can make the testing easier for one shot, single question image model testing; please give your suggestions under this thread so we can provide a more meaningful data point to the community!


> We introduced a new model designed for the Code generation task. Its test accuracy on the HumanEval base dataset surpasses that of GPT-4 Turbo (April 2024). (90.9% vs 90.2%).

@Bin12345 interested in a ZeroGPU Spaces for https://huggingface.co/Bin12345/AutoCoder


hf-daily-paper-newsletter-chinese，Using glm-4 agents, interpret hugging face's daily papers in simplified Chinese

https://github.com/2404589803/hf-daily-paper-newsletter-chinese


Crazy to see that my GroundingDino contribution to the transformers library got roughly 600k downloads combined in the checkpoints in the last month 🤯

The tiny checkpoint got almost 500k alone https://huggingface.co/IDEA-Research/grounding-dino-tiny


🙂 Hello! FluentlyXL is now on Venice.ai, you can try the model there right now.

👉 Venice.ai: https://venice.ai
✨ FluentlyXL v4: https://huggingface.co/fluently/Fluently-XL-v4

ehristoforu osanseviero julien-c Ramikan-BR lunarflu dreamdrop-art

Ramikan-BR lunarflu dreamdrop-art louisbrulenaudet

Ramikan-BR louisbrulenaudet lunarflu dreamdrop-art


✂ Uncensor any LLM with abliteration

I wrote an article about abliteration and how NeuralDaredevil-8B was created. Beyond removing alignment, I believe it's an interesting technique with a lot of potential. It's basically fine-tuning without retraining.

In this article, we see how it works, implement it in Google Colab, and heal the abliterated model to recover the performance drop due to this technique. The final model is an uncensored and high-quality model with the highest MMLU score on the Open LLM Leaderboard (8B category).

https://huggingface.co/blog/mlabonne/abliteration

fs-tom victor Ramikan-BR VlSav xi0v osanseviero AkimfromParis path-dev zzffss marksverdhei lunarflu carlosbaraza SixOpen Winnougan Joseph717171 Borcherding adolfo-ab ZeroCollabs nvhf shafire Goekdeniz-Guelmez John6666 nicoism AtAndDev marcushobbs

ijohn07 tariqamankhan jpacifico Borcherding Axmr koolhug AtAndDev marcushobbs

Ramikan-BR lunarflu Winnougan Joseph717171 Borcherding Goekdeniz-Guelmez AtAndDev

Ramikan-BR Joseph717171 lunarflu Winnougan Borcherding AtAndDev

mhollomey Joseph717171 lunarflu Winnougan Borcherding AtAndDev

path-dev lunarflu Winnougan snakeying Borcherding AtAndDev

Winnougan Borcherding bmorphism AtAndDev


It is with great pleasure I inform you that huggingface's ModelHubMixin reached 200+ models on the hub 🥳

ModelHubMixin is a class developed by HF to integrate AI models with the hub with ease and it comes with 3 methods :
* save_pretrained
* from_pretrained
* push_to_hub

Shoutout to @nielsr , @Wauplin and everyone else on HF for their awesome work 🤗

If you are not familiar with ModelHubMixin and you are looking for extra resources you might consider :
* docs: https://huggingface.co/docs/huggingface_hub/main/en/package_reference/mixins
🔗blog about training models with the trainer API  and using ModelHubMixin: https://huggingface.co/blog/not-lain/trainer-api-and-mixin-classes
🔗GitHub repo with pip integration: https://github.com/not-lain/PyTorchModelHubMixin-template
🔗basic guide: https://huggingface.co/posts/not-lain/884273241241808

Wauplin Ramikan-BR victor nielsr Hev832 sanbo1200 GPT007 osanseviero qgallouedec lunarflu beta3 louisbrulenaudet msheykhmousa


@victor unprompted feature request: I'd love to have a toggle for a HF collection to control whether new items are added to the top or to the bottom. At the moment everything gets added at the bottom, but it would be great to have newer elements on top to make fresh content easily accessible without having to scroll all the way!


New research model out ! I uploaded a new Branchy model based on Phi-2 for faster inference using Early Exit. Check it out : https://huggingface.co/valcore/Branchy-Phi-2.
I also uploaded a Hugging Face Space to try it out : https://huggingface.co/spaces/valcore/Branchy-phi-2, unfortunately inference is very slow on free tier. Let me know what you are thinking about it !


We are super happy to contribute to the GLiNER ecosystem by optimizing training code and releasing a multi-task, prompt-tunable model.

The model can be used for the following tasks:
* Named entity recognition (NER);
* Open information extraction;
* Question answering;
* Relation extraction;
* Summarization;

Model: https://huggingface.co/knowledgator/gliner-multitask-large-v0.5
Demo: https://huggingface.co/spaces/knowledgator/GLiNER_HandyLab
Repo: 👨💻 https://github.com/urchade/GLiNER

**How to use**
First of all, install gliner package.

```bash
pip install gliner
```
Then try the following code:
```python
from gliner import GLiNER

model = GLiNER.from_pretrained("knowledgator/gliner_small-v2.1")

prompt = """Find all positive aspects about the product:\n"""
text = """
I recently purchased the Sony WH-1000XM4 Wireless Noise-Canceling Headphones from Amazon and I must say, I'm thoroughly impressed. The package arrived in New York within 2 days, thanks to Amazon Prime's expedited shipping.

The headphones themselves are remarkable. The noise-canceling feature works like a charm in the bustling city environment, and the 30-hour battery life means I don't have to charge them every day. Connecting them to my Samsung Galaxy S21 was a breeze, and the sound quality is second to none.
I also appreciated the customer service from Amazon when I had a question about the warranty. They responded within an hour and provided all the information I needed.
However, the headphones did not come with a hard case, which was listed in the product description. I contacted Amazon, and they offered a 10% discount on my next purchase as an apology.
Overall, I'd give these headphones a 4.5/5 rating and highly recommend them to anyone looking for top-notch quality in both product and service.
"""
input_ = prompt+text

labels = ["match"]

matches = model.predict_entities(input_, labels)

for match in matches:
    print(match["text"], "=>", match["score"])
```


my 🤗huggingface activity for 2024 so far ...

dont tell my boss...

check yours too now, it's fun 🤗

lunarflu cjerzak TurnKeyLabs victor Hev832 Taylor658 cnmoro thifalnak


I’ve been working on a crazy theory for my first solo paper and I would appreciate some advice from leading researchers here:)

"Theory of Adaptive Learning"

Of all the deep learning algorithms at least to my knowledge, there’s none that fully covers the adaptive nature of intelligence. I believe it is a fundamental missing component of current AI governing laws.

I define it as a kind of learning wherein one person (say a student) adapts their framework of understanding to better suit that of what is being taught or said by another person/model (say a teacher). If we could measure the nature of this transfer learning. I believe it could help improve planning and reasoning capabilities of AI systems. If we look back at the theory evolution, adaptation is a fundamental component of human evolution. Today's so-called groundbreaking architectures or models, specifically large language models tend to have static parameters with constraints that are almost impossible to change or update in real-time after training. This fundamentally hinders their ability to reason, plan and accomplish objective-driven tasks as we humans do. Intelligence is dynamic.

Now this cannot be done with current autoregressive llms as their parameters are fixed with static constraints, even though RAG do help in updating model parameters in real-time but its basically cheating and doesn’t count  as intelligence. There’s a pressing need for a natively adaptive architecture - The Goal of This Paper


hello everyone, I've finished making a project for RVC Dataset Maker if you want to try the project you can try it below:
https://huggingface.co/spaces/Hev832/RVC-Dataset-Maker

if you find any erors you can create New discussion 👀

ASPPIBRA-DAO lunarflu luyulong TurnKeyLabs nevreal


Hi @coyotte508 , if you've got a minute, could you take a look at this huggingface.js discussion?
https://github.com/huggingface/huggingface.js/discussions/735


Just published a new article 😊

https://huggingface.co/blog/santiviquez/data-drift-estimate-model-performance


📢 Impressed with the application of the microsoft/Phi-3-mini-4k-instruct (3B) performance in zero-shot-learning (ZSL) mode reasoning 🧠 on Target Sentiment Analysis (TSA) problem.
💡 There are three major takeaways  out of this experiment 🧪 and they are as follows:
✅ 1. Phi-3 slightly outperforms Mistral-7B (official Mistral API, v0.1 or v0.2) on texts written in English
✅ 2. Performs similar to LLaMA-3-8B-Instruct on texts translated in English 🔥
☑ 3. Reasoning in non-english language (🇷🇺) is pretty decent but underperforms to the similar 7B sized models.

This is a huge step forward since release of Phi-2, especially because the predecessor (microsoft/phi-2) was not capable for performing reasoning in non-english texts (🇷🇺) at all!

Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Model: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101

victor Taylor658 ithonestman kristaller486 lunarflu hiauiarau


By popular demand, HF activity tracker v1.0 is here! 📊 let's build it together!🤗

Lots of things to improve, feel free to open PRs in the community tab!

good PR ideas:
- track more types of actions that include date+time
- bigger plot
- track discord activity too 🤯
- link github? ⚡

https://huggingface.co/spaces/huggingface-projects/LevelBot

lunarflu not-lain osanseviero santiviquez victor Taylor658 KingNish MatrixIA Goekdeniz-Guelmez MaziyarPanahi Hev832 damerajee Nymbo

lunarflu osanseviero KingNish Goekdeniz-Guelmez MaziyarPanahi damerajee Nymbo


Scaling Rectified Flow Transformers for High-Resolution Image Synthesis (2403.03206)

"Hold your pixels" 🚦... SD3 is here 🌟

🚀 Performance Enhancements: Stable Diffusion 3 surpasses other text-to-image models like DALL·E 3 in typography and prompt adherence.

🏗 New Architecture: Introduces the Multimodal Diffusion Transformer (MMDiT) that separately processes image and language data, enhancing text understanding and spelling.

⚡ Efficiency Improvements: Features a rectified flow formulation for more efficient image generation, fitting within the memory constraints of common GPUs.

📈 Scalability: Demonstrates scaling capabilities with models ranging up to 8 billion parameters, showing improvements in model performance without saturation.

🔧 Flexible Text Encoders: Offers a flexible approach to text encoding, maintaining performance even when the largest T5 text encoder is removed for less memory-intensive operations.

While they discuss experiments on 2B and 8B parameter models, no word on open weights 🤐

Paper: https://huggingface.co/papers/2403.03206
@StabilityAI


Impressed by the work of @guipenedo @hynky @loubnabnl @anton-l @craffel @lvwerra @thomwolf on FineWeb.

LLMs are only as good as the data they have been trained on, but the crucial aspect of pretraining data remains obscure. Our approach lifts the veil on building high-quality pretraining datasets by sharing every detail about this process to enable a wider community to build on top of it.

- The FineWeb-Edu dataset, which outperforms all openly accessible web datasets in a number of educational benchmarks. We built it by developing a quality classifier using annotations generated by an LLM.

- A new technical report explaining in detail how to create a large and high-quality web-scale dataset for LLM pretraining such as FineWeb

👉 https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1

osanseviero lunarflu YannisTevissen ZennyKenny


OpenGPT 4o now features WEB SEARCH

This feature enhances the capabilities of OpenGPT 4o, allowing it to fetch and integrate the latest information from the web directly into its responses.
Try Now: https://huggingface.co/spaces/KingNish/OpenGPT-4o

With WEB SEARCH, OpenGPT 4o becomes an even more versatile and dynamic AI, ready to assist with up-to-date data retrieval and analysis.

dreamdrop-art Taylor658 Yusufssss pabloce osanseviero victor KingNish elcrei lunarflu ubali UttamPanasala saadasif19 jackpesso Shreyas94 jordivcb RayBernard jiweiwuita abaasia SasniyParen OfferL 1ucky1uke louisbrulenaudet ConAim arunk7033


Wikimedia and Hugging Face seem kind of naturally complementary: Both are community-centred, value openness and consent. That's why I'd love to see more Wikipedia and other Wikimedia projects' datasets on Hugging Face to advance machine learning with diverse, community-curated data! See my new article on the Hugging Face hub for why and how to create more Wikimedia datasets on Hugging Face: https://huggingface.co/blog/frimelle/wikipedias-treasure-trove-ml-data

yjernite fdaudens Taylor658 clem osanseviero not-lain thomwolf lunarflu Felladrin adorkin

yjernite clem not-lain louisbrulenaudet thomwolf hynky lunarflu cpetrillo


Hey Guys !! 🧋

This is the time to Share the Collection of Prompts which have high parametric details to produce the most detailed flawless images.

🔗You can watch out the Collection on: https://huggingface.co/spaces/prithivMLmods/Top-Prompt-Collection

🔢More than 200+ High Detailed prompts have been used in the Spaces.
@prithivMLmods

Thank you for the read. !!

KingNish Katiyar48 RanthonyB Ramikan-BR UltraMarkoRJ


Fluently XL v4 took 4th place in the arena leaderboard imgsys.org, yay!

Model: https://huggingface.co/fluently/Fluently-XL-v4
Playground with this model: https://huggingface.co/spaces/fluently/Fluently-Playground

ehristoforu dreamdrop-art osanseviero KingNish Arakinas isidentical lunarflu Tonic louisbrulenaudet julien-c


Grand Thief Auto style

https://reface.ai/unboring/restyle-image


Jina CLIP: Your CLIP Model Is Also Your Text Retriever (2405.20204)

we are very proud to introduce https://huggingface.co/jinaai/jina-clip-v1, aka "jina-embeddings-multimodal".

The OpenAI CLIP https://huggingface.co/openai/clip-vit-base-patch32 have nice performance to align text and image modality, that user can perform cross-modal text image retrieval or image classification on top of it.  However, due to the training data and recipe, it can not:

1. model longer sequence of text inputs (77 token constraint).
2. align text representations (CLIP Text Tower is weak for text search).

In our latest publication, https://huggingface.co/papers/2405.20204 , we proposed a multi-task, multi-objective learning scheme. The produced CLIP model shows:

1. Stronger cross-modal performance against OpenAI sets, 2% and 6% improvement on cross-modal retrieval recall@5.
2. Text tower of the JinaCLIP is a strong text encoder, reach the same performance as https://huggingface.co/jinaai/jina-embeddings-v2-base-en, 165% improvement on MTEB[BEIR] recall@5.
3. Image tower of the JinaCLIP also shows strong performance in image-image search (CBIR), 12% recall improvement on Cifar100 test set.

If you are working on MuRAG (multimodal-retrieval argumented generation), try it out!

florianhoenicke florian-hoenicke zhenwang23 alaeddineabdessalem osanseviero alexcg2 nan capricareloaded Jofthomas

florianhoenicke florian-hoenicke alaeddineabdessalem osanseviero alexcg2 nan capricareloaded jaisanrobert


Proof that ablative educational dataset significantly enhances model capabilities (independent of model parameters or architecture) 🤩

Yesterday, FineWeb’s technical report was published. FYI FineWeb (by 🤗) is currently the best opensource text dataset that can scale up model performance up to that of GPT-3 level.

While proprietary datasets used in training models like GPT-4/Claude/LlaMA are crawled internally and never released, FineWeb builds on CommonCrawl (an open repo for crawled web data). They preprocessed the data using their custom built data preprocessing library datatrove (which they also opensourced), and then evaluate the data quality on lighteval by training small sized models “ablation models” using nanotron (a library for pretraining transformer models).

Of all versions of FineWeb, FineWeb-Edu outperforms all other subsets. This is thanks to a new filtering technique wherein they used synthetic data to develop classifiers for identifying educational contents.

Turned out “Education is All You Need”:)

Taylor658 osanseviero valentimarco Vlansu Hev832 neuralink hynky GPT007 victor Ramikan-BR jlzhou


Rope is the newest 1-Click, most easy to use, most advanced open source Deep Fake application. It has been just published few days ago. In below tutorials I show how to use Rope Pearl DeepFake application both on Windows and on a cloud machine (Massed Compute). Rope is way better than Roop, Roop Unleashed and FaceFusion. It supports multi-face Face Swapping and making amazing DeepFake videos so easily with 1-Click. Select video, select faces and generate your DeepFake 4K ultra-HD video.

1-Click Rope Installers Scripts (contains both Windows into an isolated Python VENV and Massed Compute — Cloud — No GPU)⤵

https://www.patreon.com/posts/most-advanced-1-105123768

Tutorials are made only for educational purposes. On cloud Massed Compute machine, you can run with staggering 20 threads and can FaceSwap entire movies. Fully supports face tracking and multiple face changes.

Mind-Blowing Deepfake Tutorial: Turn Anyone into Your Fav Movie Star! Better than Roop & Face Fusion ⤵
https://youtu.be/RdWKOUlenaY

Best Deepfake Open Source App ROPE — So Easy To Use Full HD Feceswap DeepFace, No GPU Required Cloud ⤵
https://youtu.be/HLWLSszHwEc


huggingface, SakanaAILabs and @arcee_ai are sponsoring a Model Merging Competition with really sweet 💰cash prizes💰 at the 2024 NeurIPSConf! (https://neurips.cc) 🎉

Submissions are now open and will remain open until September 2024. 🚀

🔗 Register here: https://llm-merging.github.io/
🗣 Join the Discord discussion: https://discord.com/invite/dPBHEVnV

Artples Tonic osanseviero KingNish mervenoyan taewan2002 not-lain


hi everyone!

i wanted to share an experiment i did with upcycling phi-3 mini into an moe recently.
while benchmarks are definitely within a margin of error and they performed similarly, i think it's an interesting base to try and see if you can improve phi's performance! (maybe looking into https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu could be interesting, i also left some other notes if anyone with more compute access wants to try it themselves)

check it out! https://huggingface.co/Fizzarolli/phi3-4x4b-v1


Haloooo, continue experimenting with a checkpoint version of Ghost Beta (small version) during training in stage 1 (trained progress: 41%).

Supported languages: 🇺🇸 English, 🇪🇸 Spanish, 🇵🇹 Portuguese, 🇫🇷 French, 🇮🇹 Italian, 🇩🇪 German, 🇻🇳 Vietnamese, 🇰🇷 Korean, 🇨🇳 Chinese, and !?

Note that this is not a conclusion, this is just a sharing of the state of the model. If you find it interesting, please follow the project at:
* https://x.com/ghostx_ai
* https://ghost-x.org/
* https://huggingface.co/ghost-x

Ghost X is currently very open to invitations to cooperate, share and support.
🤯👇


🍷 FineWeb technical report is out and so is 📚 FineWeb-Edu, a 1.3 trillion tokens dataset that outperforms all other open web datasets, with remarkable improvements on educational benchmarks such as MMLU, ARC, and OpenBookQA.

Technical report: https://hf.co/spaces/HuggingFaceFW/blogpost-fineweb-v1
Dataset: https://hf.co/datasets/HuggingFaceFW/fineweb-edu

We used Llama 3 generations to train an educational quality classifier, filtering the 15 trillion tokens of FineWeb to select only those with high educational value (an approach also used in Llama 3 and Phi-3 training datasets). We're releasing both FineWeb-Edu and the classifier, along with a larger, less heavily filtered version containing 5.4 trillion tokens.

You can find more details about the dataset and the experiments we ran in the FineWeb technical report, It's a 45-minute read but it contains all the secret sauce for building high quality web datasets.

Enjoy!


[New crazy blog post alert] We are releasing an extensive blog post on the science of creating high quality web-scale datasets, detailing all the steps and learnings that came in our recent 15 trillion tokens 🍷FineWeb release

Inspired by the distill.pub interactive graphics papers, we settled to write the most extensive, enjoyable and in-depth tech report we could draft on so prepare for a 45-mmin read with interactive graphics and all.

And it's not all, in this article we also introduce 📚FineWeb-Edu a filtered subset of Common Crawl with 1.3T tokens containing only web pages with very high educational content. Up to our knowledge, FineWeb-Edu out-performs all openly release web-scale datasets by a significant margin on knowledge- and reasoning-intensive benchmarks like MMLU, ARC, and OpenBookQA

We also make a number of surprising observations on the "quality" of the internet it-self which may challenge some of the general assumptions on web data (not saying more, I'll let you draw your conclusions ;)

https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1

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loubnabnl mmhamdy mmoy KonradSzafer rreed-pha Joseph717171 SaylorTwift clem

afrideva Joseph717171 medmekk clem dadaddy


Yuan 2.0-M32: Mixture of Experts with Attention Router (2405.17976)

🔥 77.2% on MMLU with 3.7B parameters 🚀

... 3.7B active parameters, 40B in total parameters 📊

7.4 GFlops forward computation per token, 1/19 of Llama3-70B 📉

Exciting enough? 😲

That's Yuan2-M32 for you, released by IEIT-Yuan.
A new 40B Mixture of Experts using a new Attention Router mechanism 🧠

32 experts with 2 active in generation ✌

8,192 context length 📝

Trained on 2T tokens, using 9.25% of the compute required by the dense models 🛠.

Yuan 2.0-M32 employs fine-tuning techniques to adjust to longer sequence lengths, utilizing a modified base value in the Rotary Position Embedding to maintain performance over extended contexts 🔄.

Open-source - Apache 2.0 📜

Vocabulary size of 135,040 🗣

Outperforms Mixtral 8x7B (47B total parameters, 12.9B active parameters) on all benchmarks and almost gives Llama 3 70B run for its money 💸

Models: https://huggingface.co/IEITYuan 🌐
Paper: https://huggingface.co/papers/2405.17976 📄


Large Language Models in Targeted Sentiment Analysis (2404.12342)

The most recent LLaMA-3-70B Instruct showcases the beast performance in zero-shot-learning mode in Target-Sentiment-Analsys (TSA) 🔥🚀  In particular we experiment with sentence-level analysis, with sentences fetched from the WikiArticles that were formed into RuSentNE-2023 dataset.

The key takeaways out of LLaMA-3-70B performance on original (🇷🇺) texts and translated into English are as follows:
1. Outperforms all ChatGPT-4 and all predecessors on non-english-texts (🇷🇺)
2. Surpasses all ChatGPT-3.5 / nearly performs as good as ChatGPT-4 on english texts 🥳

Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
Model: https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct
Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
Related paper: https://huggingface.co/papers/2404.12342
Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101


all these GPU bourgeois tryna act cool like the GPU poor kids...
- what's your number for real ?
+ and did it work at parties for you ?


Get your hands on the new, best, most-performant tiny model on huggingface. With 32k context window, you can fine-tune it on larger datasets or your preferred rag functionality.

https://huggingface.co/appvoid/palmer-004


Introducing GlotCC: a new 2TB corpus based on an early 2024 CommonCrawl snapshot with data for 1000+ languages.

🤗 corpus v1: https://huggingface.co/datasets/cis-lmu/GlotCC-V1
🐱 pipeline v3: https://github.com/cisnlp/GlotCC

More details? Stay tuned for our upcoming paper.
More data? In the next version, we plan to include additional snapshots of CommonCrawl.

Limitation: Due to the lower frequency of low-resource languages compared to others, there are sometimes only a few sentences available for very low-resource languages. However, the data volume for English in this version stands at 750GB, and the top 200 languages still have a strong presence in our data (see plot attached; we write the index for every 20 languages, meaning the 10th index is the 200th language).

kargaranamir osanseviero eliebak yjernite ayymen


The InstructGPT paper mentions that they insert 10% pretraining data during SFT, which they find improves the effect of PPO (IIUC). Has anyone else done later ablations on this? I've only seen the inverse suggested, mixing in SFT data during pretraining.


📺 Introducing the YouTube-Commons Dataset 📺

🌐 Overview: The YouTube Commons Dataset is a comprehensive collection of 30 billion words from 15,112,121 original and automatically translated transcripts, drawn from 2,063,066 videos on YouTube.

🔗 License: All videos are shared under the CC-BY license, with the majority (71%) in English.

🤖 Applications: This dataset is ideal for training powerful AI models for converting speech to text (ASR) and translation models.

📊 Utilization: The text can be used for model training and is republishable for reproducibility purposes.

🤝 Collaboration: This dataset is the result of a collaboration between state start-up LANGU:IA, the French Ministry of Culture, and DINUM. It will be expanded in the coming months.

🔗 Explore the dataset here: https://lnkd.in/d_paWKFE

#YouTubeCommons #AIResearch #MachineLearning #OpenData #ArtificialIntelligence #NLP #Dataset #TechCollaboration #Innovation #DigitalTransformation

Salama1429 Ramikan-BR KingNish NHLOCAL Tonic jshuadvd Vladuzz rreed-pha KIRNEILL thomwolf hiauiarau dingo-actual

Salama1429 Ramikan-BR Tonic thesven jshuadvd GPT007 thomwolf

Salama1429 Ramikan-BR Tonic thesven jshuadvd dillfrescott thomwolf

Salama1429 Ramikan-BR Tonic jshuadvd GPT007 thomwolf

Salama1429 Ramikan-BR Tonic jshuadvd nataliaElv


Introducing UNA-ThePitbull Series

We are happy to announce the release of our latest model UNA-ThePitbull, the most powerful model below 70B in the industry. In this new generation, inspired on our previous Beagle series we curated a model that balance nicely EQ and IQ. It was trained with some of the latest datasets including:
* Replete-AI/code_bagel_hermes-2.5
* mlabonne/orpo-dpo-mix-40k
* jondurbin/py-dpo-v0.1
Available in the hub https://huggingface.co/fblgit/UNA-ThePitbull-21.4B-v2 and you can grab Quant versions sponsored by @bartowski at https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF fully compatible with Ollama, llama.cpp, etc.

UNA
In this case we tried something new by alternating uniformity across layers of both MLP & Attention reducing computational requirements while keep a high performant result.

We trained him under these terms:
* ThePitbull-v1 as base: SFT maxLR 1e-4 minLR 5e-5 for 1 Epoch
* DPO maxLR 1e-4 minLR 5e-5 for 1 Epoch
You can continue the training by merely using 5e-5 maxLR and 0 warmup steps, it should minimize catastrophic forgetting of the model.

Remember if you do so, please include a Pitbull picture on your model and cite :) Have fun!


ChemLLM Multi-Modal version will coming soon!
Also Weights and Datasets!

YaTharThShaRma999 osanseviero Ramikan-BR taufiqdp whitebill Tonic Hev832 mrmuminov Yhyu13 louisbrulenaudet


Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts (2405.11273)

Remember Gemini, GPT-4o, all being true multimodal models 🌟.

Now we have a paper 📄 describing an architecture that might achieve that!

Uni-MoE: a native multimodal, Unified Mixture of Experts (MoE) architecture 🏗.

Uni-MoE integrates various modalities (text 📝, image 🖼, audio 🎵, video 📹, speech 🗣) using modality-specific encoders and connectors for a cohesive multimodal understanding.

Training Strategy:
1⃣ Training cross-modality alignment with diverse connectors 🔄.
2⃣ Training modality-specific experts using cross-modality instruction data 📊.
3⃣Tuning the Uni-MoE framework with Low-Rank Adaptation (LoRA) on mixed multimodal data 🔧.

Technical Details:

Modality-Specific Encoders: CLIP for images 🖼, Whisper for speech 🗣, BEATs for audio 🎵.

MoE-Based Blocks: Shared self-attention layers, feed-forward networks (FFN) based experts, and sparse routers for token-level expertise allocation 🚀.

Efficient Training: Utilizes LoRA for fine-tuning pre-trained experts and self-attention layers 🛠.

Uni-MoE outperforms traditional dense models on benchmarks like A-OKVQA, OK-VQA, VQAv2, MMBench, RACE-Audio, and English High School Listening Test 🏆.

The code is open-sourced as well: https://github.com/HITsz-TMG/UMOE-Scaling-Unified-Multimodal-LLMs/tree/master/Uni_MoE_v2

Paper: https://huggingface.co/papers/2405.11273


#Newer / Current Version
🚨Huggingface APK Update v0.0.4🚨
1. Fixed Pinch to Zoom Update .
2. Swipe Gestures.
3. Fixed Auto Rotate.
4. Updated app Indentifiers.

Download the app now !!
🚨Huggingface v0.0.4 Download,
⬇Link : https://drive.google.com/file/d/1xEiH7LMdP14fBG-xDuSqKje5TRLV1PuS/view?usp=sharing

Like 👍Share 🚀 Follow 🌠


I am pleased to announce 2 amazing AI demos:

1. Chat with Google Agent - This includes three AI models that allow you to converse with an AI, which provides answers by searching Google.
Demo Link: https://huggingface.co/spaces/poscye/google-go

2. HelpingAI 9B - A model that surpassed all top AIs with the highest EQ benchmark score of 89.23. It specializes in understanding human emotions and responding in human style.
Demo Link: https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B
Model Link: https://huggingface.co/OEvortex/HelpingAI-9B
Blog Link: https://huggingface.co/blog/KingNish/helpingai-9b


We are pleased to announce the new line of universal token classification models 🔥

https://huggingface.co/collections/knowledgator/universal-token-classification-65a3a5d3f266d20b2e05c34d

It can perform various information extraction tasks by analysing input prompts and recognizing parts of texts that satisfy prompts. In comparison with the first version, the second one is more general and can be recognised as entities, whole sentences, and even paragraphs.

The model can be used for the following tasks:
* Named entity recognition (NER);
* Open information extraction;
* Question answering;
* Relation extraction;
* Coreference resolution;
* Text cleaning;
* Summarization;

How to use:

```
from utca.core import (
    AddData,
    RenameAttribute,
    Flush
)
from utca.implementation.predictors import (
    TokenSearcherPredictor, TokenSearcherPredictorConfig
)
from utca.implementation.tasks import (
    TokenSearcherNER,
    TokenSearcherNERPostprocessor,
)
predictor = TokenSearcherPredictor(
    TokenSearcherPredictorConfig(
        device="cuda:0",
        model="knowledgator/UTC-DeBERTa-base-v2"
    )
)
ner_task = TokenSearcherNER(
    predictor=predictor,
    postprocess=[TokenSearcherNERPostprocessor(
        threshold=0.5
    )]
)

ner_task = TokenSearcherNER()

pipeline = (
    AddData({"labels": ["scientist", "university", "city"]})
    | ner_task
    | Flush(keys=["labels"])
    | RenameAttribute("output", "entities")
)
res = pipeline.run({
    "text": """Dr. Paul Hammond, a renowned neurologist at Johns Hopkins University, has recently published a paper in the prestigious journal "Nature Neuroscience". """
})
```


𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗮𝗻 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝘂𝘀𝗲 𝗶𝘁𝘀 𝗟𝗟𝗠 𝗲𝗻𝗴𝗶𝗻𝗲 𝘁𝗼 𝘀𝗼𝗹𝘃𝗲 𝘁𝗮𝘀𝗸𝘀?

➡ I made my first ever 𝘮𝘢𝘯𝘪𝘮 video to show just that:

𝗪𝗮𝘁𝗰𝗵 𝗯𝗲𝗹𝗼𝘄 𝗵𝗼𝘄 𝗮 𝗥𝗲𝗮𝗰𝘁 𝗔𝗴𝗲𝗻𝘁 𝘀𝗼𝗹𝘃𝗲𝘀 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝘁𝗮𝘀𝗸, by leveraging its memory to iterate on previous actions! 🎬👇

Read our blog post on Agents: https://huggingface.co/blog/agents


With the previous survey, Ghost Beta (small version) will support 9+ languages fluently. It is revealed that the model will be designed for 3 stages of training, showing a checkpoint to try at stage 1 (trained progress: 29%).

Supported languages: 🇺🇸 English, 🇪🇸 Spanish, 🇵🇹 Portuguese, 🇫🇷 French, 🇮🇹 Italian, 🇩🇪 German, 🇻🇳 Vietnamese, 🇰🇷 Korean, 🇨🇳 Chinese, and !?

Note that this is not a conclusion, this is just a sharing of the state of the model. If you find it interesting, please follow the project at:
* https://x.com/ghostx_ai
* https://ghost-x.org/
* https://huggingface.co/ghost-x

🤯👇


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

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

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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 👇:

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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.

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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

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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

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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

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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.

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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.

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Why is the Adam Optimizer so good? Simple, because it will never find the absolute most optimal solution. That is a design feature, not a flaw. This is why no other optimizer comes close in terms of generalizable use. Want to learn more about this entire process and exactly what I am talking about? I break all of this down in very simple terms in this video! https://youtu.be/B9lMONNngGM

https://youtu.be/B9lMONNngGM


Microsoft released a groundbreaking model that can be used for web automation, with MIT license 🔥 https://huggingface.co/microsoft/OmniParser

Interesting highlight for me was Mind2Web (a benchmark for web navigation) capabilities of the model, which unlocks agentic behavior for RPA agents.

no need for hefty web automation pipelines that get broken when the website/app design changes! Amazing work.

Lastly, the authors also fine-tune this model on open-set detection for interactable regions and see if they can use it as a plug-in for VLMs and it actually outperforms off-the-shelf open-set detectors like GroundingDINO. 👏

OmniParser is a state-of-the-art UI parsing/understanding model that outperforms GPT4V in parsing.

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Parents in the 1990: Teach the kids to code
Parents now: Teach the kids to fix the code when it starts walking around 🤖✨

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Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis
  from Scratch (2410.18693)

📊 We present ScaleQuest-Math-1M, a mathematical reasoning dataset of 1 million high-quality question-answer pairs.
🔥 We propose ScaleQuest, a scalable and novel data synthesis method that utilizes small-size open-source models to generate questions from scratch.

Project Page: https://scalequest.github.io/
Dataset: https://huggingface.co/datasets/dyyyyyyyy/ScaleQuest-Math
Paper: https://huggingface.co/papers/2410.18693
HF Collection: https://huggingface.co/collections/dyyyyyyyy/scalequest-670a7dc2623c91990f28913b


🤯 Plot twist: Size isn't everything in AI! A lean 32B parameter model just showed up to the party and outperformed a 70B one. Efficiency > Scale? The AI world just got more interesting...

Cohere For AI released Aya Expanse, a new family of multilingual models (8B and 32B) spanning 23 popular languages.

Models: https://huggingface.co/collections/CohereForAI/c4ai-aya-expanse-671a83d6b2c07c692beab3c3
Blog post: https://huggingface.co/blog/aya-expanse
Demo: https://huggingface.co/spaces/CohereForAI/aya_expanse

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This is no Woodstock AI but will be fun nonetheless haha. I’ll be hosting a live workshop with team members next week about the Enterprise Hugging Face hub.

1,000 spots available first-come first serve with some surprises during the stream!

You can register and add to your calendar here: https://streamyard.com/watch/JS2jHsUP3NDM

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If you have ~300+ GB of V-RAM, you can run Mochi from @genmo

A SOTA model that dramatically closes the gap between closed and open video generation models.

Mochi 1 introduces revolutionary architecture featuring joint reasoning over 44,520 video tokens with full 3D attention. The model implements extended learnable rotary positional embeddings (RoPE) in three dimensions, with network-learned mixing frequencies for space and time axes.

The model incorporates cutting-edge improvements, including:
- SwiGLU feedforward layers
- Query-key normalization for enhanced stability
- Sandwich normalization for controlled internal activations

What is currently available?
The base model delivers impressive 480p video generation with exceptional motion quality and prompt adherence. Released under the Apache 2.0 license, it's freely available for both personal and commercial applications.

What's Coming?
Genmo has announced Mochi 1 HD, scheduled for release later this year, which will feature:
- Enhanced 720p resolution
- Improved motion fidelity
- Better handling of complex scene warping

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October version of Claude 3.5 lifts SOTA (set by its June version) by 7 points.
https://huggingface.co/spaces/onekq-ai/WebApp1K-models-leaderboard

Closed sourced models are widening the gap again.

Note: Our frontier leaderboard now uses double test scenarios because the single-scenario test suit has been saturated.


Cohere drops two new multilingual models!

https://huggingface.co/CohereForAI/aya-expanse-8b
https://huggingface.co/CohereForAI/aya-expanse-32b

Try them out here

https://huggingface.co/spaces/CohereForAI/aya_expanse

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🔄 𝗧𝗿𝗮𝗶𝗻 𝗼𝗻 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗱𝗮𝘁𝗮:

🔄 𝗧𝗿𝗮𝗶𝗻 𝗼𝗻 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗱𝗮𝘁𝗮:

🌟🌎 Cohere releases Aya 8B & 32B: SOTA multilingual models for 23 languages !

How did they manage to beat top contenders while also adding 23 languages?

🔄 𝗧𝗿𝗮𝗶𝗻 𝗼𝗻 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗱𝗮𝘁𝗮:
• Synthetic data has been said to cause model-collapse after too much training
• Cohere has introduced "data arbitrage" to prevent this by strategically sampling from a pool of several teacher models instead of one single teacher
• First train a model pool for each different groups of languages, and employ an internal Reward Model named "Arbiter" to evaluate and select the optimal generation. Then only the best generation is kept as the final completion for each prompt
➡ This process is particularly effective for multilingual setting, where no single teacher model performs in all languages : here "Multilingual Arbitrage" singlehandedly improves win rates of the 8B model vs Gemma-2-9B by 10 points!

🧩 𝗨𝘀𝗲 𝗺𝗼𝗱𝗲𝗹 𝗺𝗲𝗿𝗴𝗶𝗻𝗴: Rather than struggling to find the right mix of data in training a single model for multilingual use, just train language specific models then merge them!
• Maximize diversity between merged checkpoints by training each on different language families.
• Experimented fancy techniques (SLERP, TIES, DARE-TIES) but found out weighted averaging to be the most consistent!
➡ Merging had 3x more gains at high 35B scale vs the 8B scale - consistent with literature findings that merging is more effective at scale

⚡ 𝗚𝗿𝗲𝗮𝘁 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Automatic evaluations on Arena-Hard-Auto dataset:
➡ Aya Expanse 8B beats models from its weight class such as Gemma 2 9B, Llama 3.1 8B, and the recent Ministral 8B, with win rates ranging from 60.4% to 70.6%
➡ Aya Expanse 32B outperforms Gemma 2 27B, Mistral 8x22B, and Llama 3.1 70B (2x its size)
• ⚠ But this performance eval comes from only one benchmark! Let's wait for Open LLM leaderboard evals;

🔒 CC by NC license

Blog post here: https://huggingface.co/blog/aya-expanse


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.

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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

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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:)

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


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

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

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

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


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

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

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

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


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


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

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

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!

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

https://huggingface.co/BhashaAI/ViLaH

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Weekly highlights for the HF ecosystem!

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

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


🧨 Diffusers 0.28.0 is out 🔥

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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!

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Remember stacking in ensemble ML? 🤔

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

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

Frankenstein MoE! (not an official name) 🧟♂

That's the new Kraken architecture! 🐙

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

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

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

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

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


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

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

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

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


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

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

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

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

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

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


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

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

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

👥 Authors: Yuchi Wang et al.

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

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

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

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

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

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

Alexandro14 KingNish osanseviero lunarflu


Executable Code Actions Elicit Better LLM Agents (2402.01030)

𝙒𝙧𝙞𝙩𝙞𝙣𝙜 𝙩𝙤𝙤𝙡 𝙘𝙖𝙡𝙡𝙨 𝙞𝙣 𝙘𝙤𝙙𝙚 𝙟𝙪𝙨𝙩 𝙬𝙤𝙧𝙠𝙨 𝙗𝙚𝙩𝙩𝙚𝙧 𝙩𝙝𝙖𝙣 𝙅𝙎𝙊𝙉 💪

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

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

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

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

But 𝙝𝙤𝙬 𝙨𝙝𝙤𝙪𝙡𝙙 𝙩𝙝𝙚 𝙖𝙜𝙚𝙣𝙩 𝙚𝙭𝙥𝙧𝙚𝙨𝙨 𝙞𝙩𝙨 𝙖𝙘𝙩𝙞𝙤𝙣𝙨?

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

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

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

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

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


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

You can pick any dataset of your choice!

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

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

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

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

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

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

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

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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