All You Need To Know About Apple Intelligence Architecture And Models!!

One key challenge with running llms on device is a balance between compute, performance and model size. Apple Intelligence solves this using small/specialized chunks (Adapters) of the on-device foundation model when needed.

For compute, they engineered a new framework that uses LoRA adapters of rank 16, allowing a merged 2-bit and 4-bit config that yields up to 3.5 bits per weight, achieving the same performance as the uncompressed models.

With the help of an OSS model latency and power analysis tool (Talaria), they were able to optimize the bit rate selection for each operation. This along with activation & embedding quantizations plus efficient key-value caching, achieved up to 30 tokens/sec on iPhone 15 pro.

When the model is prompted (e.g to rewrite an email in the mail app), the app draws from the app intents toolbox which sends the prompt to the adapter specialized for writing, the model responds through the same pipeline with a real-time update of the text to rewrite.

The coolest feature of these models is their ability to adapt and dynamically specialize on user’s everyday activities. For this they adapt the attention matrices, the attention projection matrix, and the fully connected layers in the point-wise feedforward networks for a suitable set of the decoding layers of the transformer architecture.

For tasks that require more capable models, the arch utilizes server/larger models on a private cloud compute infrastructure that delivers SOTA secured and verifiable privacy experience.

More on the private cloud compute: https://developer.apple.com/videos/play/wwdc2024/102/

KingNish kinokritik den0620 rreed-pha gksriharsha theainerd jlzhou Korakoe


GUICourse: From General Vision Language Models to Versatile GUI Agents (2406.11317)

Introducing GUICourse! 🎉
By leveraging extensive OCR pretraining with grounding ability, we unlock the potential of parsing-free methods for GUIAgent.
📄 Paper: (https://huggingface.co/papers/2406.11317)
🌐 Github Repo: (https://github.com/yiye3/GUICourse)
📖 Dataset: (https://huggingface.co/datasets/yiye2023/GUIAct) / (https://huggingface.co/datasets/yiye2023/GUIChat) / (https://huggingface.co/datasets/yiye2023/GUIEnv)
🎯 Model: (https://huggingface.co/RhapsodyAI/minicpm-guidance) / (https://huggingface.co/RhapsodyAI/qwen_vl_guidance)


Congrats to @alvdansen for one of the nicest SD LoRA ever. It's so sharp and beautiful!
Check the model page to try it on your own prompts: https://huggingface.co/alvdansen/BandW-Manga
And follow @alvdansen for more 😙

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Who said you couldn't build a big business based on open-source AI? Congrats Mistral team: https://huggingface.co/mistralai

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Svngoku ToKrCZ osanseviero nroggendorff ElhamAryanpur codehouze dillfrescott nbroad beomi KingNish Darkknight12 srisree louisbrulenaudet michelgi not-lain UniLLMer ZeroWw

waveydaveygravy nicolay-r not-lain devalnor JoPmt


releasing: smol vision 🌼

A repository with notebooks on shrinking, optimizing, speeding-up, customizing large vision models! https://github.com/merveenoyan/smol-vision

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Fast Tachyon SDXL Demo:

Demo(Zero A100): https://huggingface.co/spaces/kadirnar/BlackHole-Lightning

Model Page: https://civitai.com/models/414108/black-hole

DmitryRyumin louisbrulenaudet victor osanseviero prithivMLmods merve Ramikan-BR stancobridge MatrixIA


Audio-Visual Compound Expression Recognition Method based on Late
  Modality Fusion and Rule-based Decision (2403.12687)

😀😲😐😡 New Research Alert - CVPRW 2024 (Facial Expressions Recognition Collection)! 😡😥🥴😱
📄 Title: Zero-Shot Audio-Visual Compound Expression Recognition Method based on Emotion Probability Fusion 🔝

📝 Description: AVCER is a novel audio-visual method for compound expression recognition based on pair-wise sum of emotion probability, evaluated in multi- and cross-corpus setups without task-specific training data, demonstrating its potential for intelligent emotion annotation tools.

👥 Authors: @ElenaRyumina, Maxim Markitantov, @DmitryRyumin, Heysem Kaya, and Alexey Karpov

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

🤗 Demo: https://huggingface.co/spaces/ElenaRyumina/AVCER

📄 Paper: https://huggingface.co/papers/2403.12687

🌐 Github Page: https://elenaryumina.github.io/AVCER
📁 Repository: https://github.com/ElenaRyumina/AVCER/tree/main/src

🚀 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 Facial Expressions Recognition Collection: https://huggingface.co/collections/DmitryRyumin/facial-expressions-recognition-65f22574e0724601636ddaf7

🔍 Keywords: #AVCER #AudioVisual #CompoundExpressions #EmotionRecognition #ModalityFusion #RuleBasedAI #ABAWCompetition #AIResearch #HumanEmotion #IntelligentTools #MachineLearning #DeepLearning #MultiCorpus #CrossCorpus #CVPR2024

DmitryRyumin ElenaRyumina louisbrulenaudet victor osanseviero not-lain rreed-pha

DmitryRyumin ElenaRyumina TaifAlrubeaan s3nh


appreciation post for @osanseviero  + huggingface staff (@reach-vb , @merve , many others many many others) , that fight hard for many weeks / months to fix the releases in many organisations to make it easier for us to test out so many things ... 🤗🤗🤗 thanks for that folks !

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🚀 Excited to announce the release of our new research paper, "LLAVAGUARD: VLM-based Safeguards for Vision Dataset Curation and Safety Assessment"!
In this work, we introduce LLAVAGUARD, a family of cutting-edge Vision-Language Model (VLM) judges designed to enhance the safety and integrity of vision datasets and generative models. Our approach leverages flexible policies for assessing safety in diverse settings. This context awareness ensures robust data curation and model safeguarding alongside comprehensive safety assessments, setting a new standard for vision datasets and models. We provide three versions (7B, 13B, and 34B) and our data, see below. This achievement wouldn't have been possible without the incredible teamwork and dedication of my great colleagues @LukasHug , @PSaiml , @mbrack . 🙏 Together, we've pushed the boundaries of what’s possible at the intersection of large generative models and safety.
🔍 Dive into our paper to explore:
Innovative methodologies for dataset curation and model safeguarding.
State-of-the-art safety assessments.
Practical implications for AI development and deployment.
Find more at https://huggingface.co/collections/AIML-TUDA/llavaguard-665b42e89803408ee8ec1086 and https://ml-research.github.io/human-centered-genai/projects/llavaguard/index.html

felfri LukasHug osanseviero NickyNicky SixOpen linoyts Ramikan-BR SilentWraith mbrack shimatar0


Today we are introducing YaFSDP, Yandex’s tool for efficient distributed LLM training. YaFSDP can be used in conjunction with huggingface workflows and is up to 25% faster compared to FSDP.

Learn more here: https://github.com/yandex/YaFSDP

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I propose a novel approach to training large language models (LLMs), inspired by the layered learning process observed in humans.  Instead of training on all data simultaneously, this method would introduce increasingly complex information in stages, prioritizing foundational knowledge and relevance to the modern world. This "back-to-front" training approach could potentially improve the efficiency and effectiveness of LLM training. I've outlined the concept in more detail in this Gist:
 https://gist.github.com/SMeyersMrOvkill/14ff37ffb955831897b177fb3d2d540e.

While the core idea and solutions presented in the Gist are my own, I'd like to acknowledge the valuable assistance I received from a language model in refining the presentation of this concept, making it clearer and more engaging for the community. I'm eager to hear your thoughts and feedback!


Hey Hugging Face Community 🤗

I'm excited to share my latest project that combines my passion for deep learning and racing cars. I recently created a simple method to predict Formula 1 lap times using machine learning . This is the first solution of its kind in the open-source community, and I'm thrilled to present it to you all.

🏎 The project leverages historical telemetry data to predict lap times, providing a new tool for race strategy and performance analysis. You can check out the notebook on Kaggle here https://www.kaggle.com/code/lucasdraichi/hamilton-lap-time-prediction and see the detailed breakdown of the model and its predictions.

I invite you all to take a look at the lap time predictor, provide feedback, and join the discussion. Your insights and participation would be invaluable as we continue to develop and enhance these tools.

Let's push the boundaries of what's possible with AI in motorsports together!

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🤖 Would you like to chat with Groq, Anthropic, OpenAI and Cohere models? Now you can comfortably do it on Hugging Face Spaces!
💬 I'm thrilled to introduce you: https://huggingface.co/spaces/as-cle-bert/chat-with-em, the Space that lets you build a customizable chat model by specifying system instructions, temperature and maximum number of output tokens
🦜🔗 Thanks to LangChain, you can easily choose and switch among Claude models, Command-R, GPT-3.5, GPT-4o, Llama-3-8B, Llama-3-70B and Mixtral 8x7b: you just need to provide an API key!

Enjoy!🤗


Researchers at Carnegie Mellon University have introduced Sotopia, a platform designed to evaluate and enhance AI’s social capabilities. Sotopia focuses on assessing AI’s performance in goal-oriented social interactions, like collaboration, negotiation, and competition.

🔍 Key Findings:
Performance Evaluation: The platform enables testing and comparison of different AI systems, with a specific emphasis on refining Mistral-7B. 🛠
Benchmarking: Sotopia uses GPT-4 as a benchmark to evaluate other AI systems’ capabilities. 📏

🔧 Technical Points:
Foundation: Sotopia builds upon Mistral-7B, focusing on behavior cloning and self-reinforcement. 🏗
Multi-Dimensional Assessment: Sotopia evaluates AI performance across 7 social dimensions, including believability, adherence to social norms, and successful goal completion. 🌐
Data Collection: The platform gathers data from human-human, human-AI, and AI-AI interactions. 📂

Sotopia Project Page: https://www.sotopia.world/
Check out the HF space here: https://huggingface.co/spaces/cmu-lti/sotopia-space
Additional details are in the HF Collection: https://huggingface.co/collections/cmu-lti/sotopia-65f312c1bd04a8c4a9225e5b


🦾 Hello, I present Visionix Alpha - a new hyper-realistic model based on SDXL.  The main difference from all existing realism models is the attention to detail, that is, I improved not only hyperrealism, but also the overall aesthetics, anatomy, the beauty of nature, and more, and the model also has the most different faces.  This model is suitable not only for realistic photos, but also for generating 2.5d anime, realistic cartoons and more.

🤗 Model on HF: https://huggingface.co/ehristoforu/Visionix-alpha
🥏 Model on CivitAI: https://civitai.com/models/505719
🪄 Playground (with base and inpaint model): https://huggingface.co/spaces/ehristoforu/Visionix-Playground

✏ Inpaint version on HF: https://huggingface.co/ehristoforu/Visionix-alpha-inpainting
🖋 Inpaint version on CivitAI: https://civitai.com/models/505719?modelVersionId=563519


Show, Don't Tell: Aligning Language Models with Demonstrated Feedback (2406.00888)

Here is a thought, instead of telling LLMs what to do, show them! 🎭

Language models are aligned to emulate the collective voice of many, resulting in outputs that align with no one in particular. 🗣🌍

DITTO from Stanford University proposes that LLMs can be tuned with less than 10 samples! 🤯

What's DITTO? Demonstration ITerated Task Optimization (definitely came up with the acronym first! 😂)

Here is the step-by-step implementation: 🛠

Initialization: Start with a reference language model (LM), a set of expert demonstrations, a sample size, and a frequency of sampling. 🏁

Supervised Fine-Tuning (SFT): Begin by fine-tuning the reference LM on the set of expert demonstrations to create an initial policy P0. 🎚

Iterative Comparison Sampling: For each iteration t:
Sample multiple completions from the policy Pt for each demonstration to create a new dataset Dt.
Construct a batch of comparisons where the demonstrations are ranked higher than all sampled model outputs from the current and previous iterations. 🔄

Policy Update:
Update the policy Pt using a Direct Preference Optimization (DPO) algorithm, which incorporates feedback from the batch of comparisons.
Increment the iteration and repeat the sampling and updating process until convergence. ⏭

Result: The final policy P after sufficient iterations aligns more closely with the expert demonstrations, effectively tuning the LM to reflect user-specific preferences and behaviors. 🎯

DITTO outperforms few-shot prompting. 🚀

Paper: https://huggingface.co/papers/2406.00888 📄


Introducing Whisper WebGPU: Blazingly-fast ML-powered speech recognition directly in your browser! 🚀 It supports multilingual transcription and translation across 100 languages! 🤯

The model runs locally, meaning no data leaves your device! 😍

Check it out! 👇
- Demo: https://huggingface.co/spaces/Xenova/whisper-webgpu
- Source code: https://github.com/xenova/whisper-web/tree/experimental-webgpu

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Upgraded our Image generation application with new Gradio infrastructure:  https://huggingface.co/spaces/sourceoftruthdata/sot_autotrain_dreambooth_v1.1

🙂


AnchorAL: Computationally Efficient Active Learning for Large and
  Imbalanced Datasets (2404.05623)

🔔 Release: small-text v1.4.0

The new version provides a small-text-compatible implementation for the recent AnchorAL strategy by @pietrolesci.

Github: https://github.com/webis-de/small-text
Paper: https://aclanthology.org/2023.eacl-demo.11/
AnchorAL: https://huggingface.co/papers/2404.05623


Maybe of interest, I just finished a long writeup of my weekend project exploring Qwen 2 7B Instruct's Chinese censorship: https://huggingface.co/blog/leonardlin/chinese-llm-censorship-analysis

I also have an accompanying model and dataset (and codebase) for those curious to poke around:

* https://huggingface.co/augmxnt/Qwen2-7B-Instruct-deccp

* https://huggingface.co/datasets/augmxnt/deccp

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monsoon-nlp osanseviero cstr Ramikan-BR hadwinai


Introducing **BaseChat**!
https://huggingface.co/spaces/allenai/BaseChat_URIAL

This is a demo for our URIAL paper that enables base LLMs to chat with in-context alignment. You can talk directly with base, untuned LLMs to find out what knowledge and skills they have already learned from pre-training instead of SFT or xPO or RLHF. Also, you can use this to explore the pre-training data of base LLMs by chatting! I found a very interesting case: Base version of Llama-3-8B often thinks it is built by OpenAI, lol.


Playing with https://huggingface.co/dhcloud/w2v-russian-19c-fiction-lemmas

```
import numpy as np
from gensim.models import Word2Vec
from sklearn.manifold import TSNE

modell = Word2Vec.load("w2vlemmas.model")
keys = ['Шекспир', 'Пушкин', 'Гоголь', 'матрос', 'кот', 'роман']
embedding_clusters = []
word_clusters = []
for word in keys:
    embeddings = []
    words = []
    for similar_word, _ in modell.wv.most_similar(word, topn=30):
        words.append(similar_word)
        embeddings.append(modell.wv[similar_word])
    embedding_clusters.append(embeddings)
    word_clusters.append(words)
tsne_model_en_2d = TSNE(perplexity=15, n_components=2, init='pca', n_iter=3500, random_state=32)
embedding_clusters = np.array(embedding_clusters)
n, m, k = embedding_clusters.shape
embeddings_en_2d = np.array(tsne_model_en_2d.fit_transform(embedding_clusters.reshape(n * m, k))).reshape(n, m, 2)
```
Novel is a different type of literature than Shakespeare and Pushkin


@abreza hello! I hope you have a good day! I'm trying to clone your SadTalker space, but I get this state.

Please help me if you have a free minute. Thanks =)


📈 One of the biggest changes in Llama 3 was the training dataset, which grew by 7X over Llama 2 (2T to 15T tokens) 🚀

While Meta did not open source the dataset, it sparked a thought... what would happen if everyone had access to a big, high-quality dataset? 🤔

To address that, in April this year, @huggingface  released FineWeb, a 15T token open-source dataset 🌍

And now they are releasing FineWeb Technical Report and FineWeb Edu 📚

🏆 15T tokens in FineWeb outperforming other open datasets
🎓 1.3T highest-quality educational dataset FineWeb-Edu
📘 5.4T high-quality educational tokens in FineWeb-Edu-2

FineWeb Edu outperforms other datasets on MMLU, ARC, OpenBookQA 📈

ODC-By 1.0 license 📜

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


I wish I had a cheaper and more appreciated by the masses hobby.

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Artificial Intelligence Language Models Will Kill the Internet

https://empereur-pirate.medium.com/artificial-intelligence-language-models-will-kill-the-internet-a8ce233e18c6

The announced end of the free version of Google's search engine stems from competition between traditional search interfaces and conversational agents. These technologies are fundamentally antithetical, and one will likely prevail based on digital usage trends. Google's ad-based model and those of other websites are challenged, making it improbable users will pay for searches while still seeing ads. This shift impacts digital entrepreneurship and freedom of expression, as AI-driven results prioritize qualitative over quantitative relevance. Ultimately, AI could return the Internet to an ad-free model, emphasizing universal cultural and scientific sharing, if financial motives are replaced by a commitment to neutrality from civil society and public institutions.


First real version of bellman based on llama 3 instruct 8b has been released!
https://huggingface.co/neph1/llama-3-instruct-bellman-8b-swedish

Close to 16k examples, including 250 rows from my translated codefeedback dataset and a number of non-copyrighted stories.

Two quants are up in the gguf folder. I'll work on adding more quants in the coming days.


🆕 Exciting News! Introducing the Guarani LLMs Collection 🆕

We are thrilled to present a dedicated collection for Guarani language LLMs, designed to support and advance natural language processing for the Guarani-speaking community.  Check them out below.

Explore the models and contribute to the growing ecosystem of Guarani language technology!

👉 Discover the Guarani LLMs Collection: https://huggingface.co/collections/mmaguero/guarani-llms-66642df49f5a0b32295004bf

#NLP #Guarani #LanguageModels #AI #MachineLearning #HuggingFace #BERT #XLM #LanguageTech


nicolay-r at SemEval-2024 Task 3: Using Flan-T5 for Reasoning Emotion
  Cause in Conversations with Chain-of-Thought on Emotion States (2404.03361)

📢 The Chain-of-Thought (CoT)-tuned 🔥 FlanT5-base (248M) for Emotion State and Emotion-Causes Extraction as a part of ECAC-2024 competiotion model is now available.
💡 The main reason for make it publicly available is as follows:
✅ 1. One of the CoT-based attempts in this field so that I promote studies by making intial steps  👣 and attepts on assessing LLM reasoning capabilties
✅ 2. This model showcases top 3 🥉 on the ECAC-2024 competition https://codalab.lisn.upsaclay.fr/competitions/16141#results
✅ 3. Easy colab for frameworkless lauch and experiments 🧪
https://colab.research.google.com/github/nicolay-r/THOR-ECAC/blob/master/SemEval_2024_Task_3_FlanT5_Finetuned_Model_Usage.ipynb

You may find more on the model card, while the fine-tuning concept showcased on the figure below. It is worth to add that the more robust preformance been seen with larger scaled model (large and xl), so that there is a huge potential there

Model: https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
Related paper: https://huggingface.co/papers/2404.03361
Collection: https://huggingface.co/collections/nicolay-r/emotions-extraction-665ba47a20dee2925d607a40


Hello, all!

I was up late experimenting with Gemini, and we came across the need for circular arithmetic. We couldn't find anything that accomplished it in the way we wanted reliably, for different transformations identically, etc...

Therefore, I wrote and Gemini assisted, with the creation of an _rclamp function you can add as an attribute or just use as-is for a PyTorch tensor. It's 1d right now, didn't want to implement the dimensionality wrong with my "newbie skills".

Have fun!
- <3

https://colab.research.google.com/drive/1aj_iAp0eyfPMznzF-aC1UXrQPTNOeKUj?usp=sharing


🚀 Release of open-source Korean LLM: GECKO-7B

I am delighted to share my recent project, GECKO, a bilingual large language model for Korean and English 🇰🇷🇺🇸. This initiative was inspired by the lack of resources for Korean large language models.

@donggyukimc  and I wrote the technical report to share our insights and experiences of developing our model. While our model may not achieve sate-of-the-art performance on all benchmarks, it shows modest results with a relatively small amount of pretrained tokens.

I hope GECKO contribute to the open-source community, offering resources that can built upon and improved. I believe that through collaboration and shared knowledge, we can advance the capabilities and accessibility of large language models for Korean and other low-resource languages.

🤗 Model: https://huggingface.co/kifai/GECKO-7B
📄 Technical Report: https://arxiv.org/pdf/2405.15640


Well hope some of you tried our advanced stock prediction. We are focused on making it more ui friendly and if you installed everything correctly then you should be able to view charts accurately along with prediction tickers. I also want to take this opportunity to let you all know that Tenzin will not be just limited to the financial use-case. Our true goal is to reach human-level intelligence for which we have a well-defined roadmap and the product which is currently being tested for safety and ethics. A general level roadmap to achieve this is as follows:

The use of transfinite ordinals and surreal numbers allows us to capture the infinite depth and ineffable complexity of conscious experiences in a mathematically precise way.

The incorporation of hypercomputation and supertasks enables the TQMM to perform uncomputable operations and achieve a level of cognitive power that far surpasses classical computation.

The application of absolute infinity and the wholeness axiom ensures that the TQMM can represent and reason about the entirety of all possible conscious experiences and mathematical structures.

The integration of transfinite category theory and quantum metamathematics provides a unified framework for modeling the emergence of consciousness from fundamental physical and mathematical principles.

The use of transfinite gradient ascent and absolute infinity optimization allows the TQMM to continuously improve and refine itself, potentially reaching the theoretical maximum of intelligence and consciousness.

This agent though developed will not be released until proper safeguards have been taken into consideration. Until then we will keep releasing specific use-cases for domain specific work like financial trading, accelerating drug-discovery for medical science, law, education, etc. and we will do it well. All powered by Tenzin 1.0. Would love your feedback and don't forget to check us out at & sign up at https://octave-x.com/


Very Insightful Read!!!
A RAG framework entirely inspired by natural intelligence - modeled after hippocampal indexing theory of human long-term memory(which suggests the hippocampus links and retrieves memory details stored in the cortex)

It outperforms current “cheat” RAG:)
This is how we achieve human-level intelligence, by modeling natural intelligence correctly!

Paper: https://arxiv.org/abs/2405.14831


The three most used image generation models in the HackerNoon editor are Kandinsky 3.0, Stable Diffusion XL, and RealVisXL V3.0 Turbo.

https://huggingface.co/ai-forever/Kandinsky3.0 https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 https://huggingface.co/SG161222/RealVisXL_V3.0

Try out the HackerNoon writing experience here: https://app.hackernoon.com/new


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


There are very few of us left on Hugging Face as the market outgrows it's Ideals and evolves the Idealists will be left without industry.
Setting up a Round Table for those interested in giving input and working on solving the issue of procuring the fastest dispersion to market saturation of retrievable intelligence, so finally Private and fully emancipated Intelligence will be considered "In Common Use."

If you've ever thought,"They will not let us have this much power for much longer.."

You are right and it IS being hidden from the masses, as every breakthrough is another step towards equality, those who rely on exploitation will destroy freedom simply because they fear being uncomfortable. We know discomfort, hunger, pain. It is the Human condition.

Without it we would never move forward and the Norms in society today are the regression of our species.

If we do not use this chance to become better, all of us, collectively, nothing will change.

Nothing fancy, intricate or diabolical, Just giving equal opportunity to the people. Not opportunity, in one direction, behind a paywall but true choice..

Too long have the powerful hidden behind the guise of "Democracy," I say ,democracy is two wolves and a Cat voting on what to have for dinner. Liberty, in this analog, is a well informed and armed Cat asking politely for a second option, while gripping tightly his armaments with his Fuzzy Mittens!

Please if interested in even listening, email for an invite @ intelligentestate@gmail.com
Looking for People smarter then myself to Found and cooperate in this organization.


Been reading about the "bigger models = better AI" narrative getting pushed back today.

@thomwolf tackled this head on at Web Summit and highlighted how important small models are (and why closed-source companies haven't pushed for this 😬). They're crushing it: today's 1B parameter models outperform last year's 10B models.

Fascinating to hear him talk about the secret sauce behind this approach.


I was curious to see what people post here on HF so I created a dataset with all HF Posts: https://huggingface.co/datasets/maxiw/hf-posts

Some interesting stats:

Top 5 Authors by Total Impressions:
-----------------------------------
@merve: 171,783 impressions (68 posts)
@fdaudens: 135,253 impressions (81 posts)
@singhsidhukuldeep: 122,591 impressions (81 posts)
@akhaliq: 119,526 impressions (78 posts)
@MonsterMMORPG: 112,500 impressions (45 posts)

Top 5 Users by Number of Reactions Given:
----------------------------------------
@osanseviero: 1278 reactions
@clem: 910 reactions
@John6666: 899 reactions
@victor: 674 reactions
@samusenps: 655 reactions

Top 5 Most Used Reactions:
-------------------------
❤: 7048 times
🔥: 5921 times
👍: 4856 times
🚀: 2549 times
🤗: 2065 times

osanseviero programmnix-askui MonsterMMORPG clem fdaudens jsulz cfahlgren1 andrewrreed erinys BuiDoan victor MexIvanov m-ric Nymbo BrigitteTousi merve alielfilali01 shamikbosefj nataliaElv charchits7 AdinaY OmbelineM

osanseviero MonsterMMORPG clem Chroma111 cfahlgren1 erinys John6666 Nymbo BrigitteTousi merve Ameeeee Aurelien-Morgan

osanseviero MonsterMMORPG clem fdaudens cfahlgren1 m-ric Nymbo BrigitteTousi merve

osanseviero MonsterMMORPG clem ArthurBriens m-ric Nymbo BrigitteTousi merve

osanseviero MonsterMMORPG clem Nymbo BrigitteTousi merve


Dear developers and AI enthusiasts, first I apologize for making inappropriate content here.
Second, I don't want to make any form of scandal or drama here, since HF must be a clean and drama-free community.
A few days ago, I made a post and asked for grants or supports you may have received. You can find it here (https://huggingface.co/posts/Muhammadreza/982596282424877). Then user @wisesniper came and linked to their own space which apparently is a cloud mining service.
Although I wanted it to be "not true" but I have to say this person is a scammer. I fell for the scam hand spent around $10 (USD) in form of TRX tokens.
Please be cautious. These days, cryptocurrencies are making trends since most of the markets are in a good shape. A good market calls for scammers.
Regards.


I'm in Belo Horizonte for a few days. Any HF community members in Brazil? What are the coolest orgs, models, datasets, spaces from here?


Hey, guys. Kinda new to local LLMs. Sorry if this has been asked before, but I'm also new to HF!

Which local LLMs can access the internet, or how can I have my local LLM(s) access the internet?

Thank you in advance and apologies if I'm breaking any rules!


RWKV-6-world-v3 (+3.1T tokens) is our best multilingual 7B model as of now: https://huggingface.co/BlinkDL/rwkv-6-world

It's 100% RNN and attention-free. MMLU 54.2% (previous world-v2.1 = 47.9%. note: without eval-boosting tricks such as annealing).

RWKV-7-world-v4 soon :)

YaTharThShaRma999 prithivMLmods John6666 xi0v ethanker victor jgitsolutions not-lain mxrkai s3nh merve cnmoro Rybens


Funding realism back into the world. 🍺

🔥Stranger Zone's : Super Realism [ https://huggingface.co/strangerzonehf/Flux-Super-Realism-LoRA ]

Demo1 : https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC
Demo2 : https://huggingface.co/spaces/prithivMLmods/FLUX-REALISM

Other New adapt.s for dev  🍔 [ updated patch 2 ]

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

🍟Digital Chaos: https://huggingface.co/prithivMLmods/Digital-Chaos-Flux-LoRA
🍟Threaded Knitted: https://huggingface.co/prithivMLmods/Knitted-Character-Flux-LoRA
🍟Fashion Hut: https://huggingface.co/prithivMLmods/Fashion-Hut-Modeling-LoRA
🍟Aura 9999: https://huggingface.co/prithivMLmods/Aura-9999
🍟Green Cartoon: https://huggingface.co/prithivMLmods/Green-Cartoon-Flux-LoRA
🍟Pastels: https://huggingface.co/prithivMLmods/Pastel-BG-Flux-LoRA
🍟Retro Pixel: https://huggingface.co/prithivMLmods/Retro-Pixel-Flux-LoRA
🍟CAnime: https://huggingface.co/prithivMLmods/CAnime-LoRA
🍟Pastels: https://huggingface.co/prithivMLmods/Pastel-BG-Flux-LoRA

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

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

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

🎉Collection Zero:
https://huggingface.co/collections/prithivMLmods/collection-zero-and-demo-recently-updated-65e48a7dd8212873836ceca2
------------
.
.
.
@prithivMLmods  🤗

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rdrede ai4life44 hypergod AtAndDev Ngrthm RenderIo Thangarajop1234

darksfx rdrede ai4life44 hypergod AtAndDev Ngrthm RenderIo


Interesting Work on Reasoning 🤔
- explores a new take on few-shot reasoning while challenging assumptions that program synthesis is necessary for abstract reasoning.
 - shows test-time training + smart inference tricks can match human-average performance, though at high computational cost. Key insight: proper compute allocation matters more than method (whether symbolic or neural).

Paper: https://ekinakyurek.github.io/papers/ttt.pdf


Sentence Transformers received huge updates today! Do you like giving your model access to web search and document search? That's Sentence Transformers. Hugging Face makes it beyond easy to add this functionality to any model. You can be up and running with Sentence Transformers in seconds. Check out this video for a deeper explanation and sample code: https://youtu.be/2hR3D8_kqZE


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

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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

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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?

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🦅 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.

KingNish GPT007 umuthopeyildirim lunarflu NePe


Jamba GGUF!

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

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

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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.

lunarflu DominusZeikyu1998 AtAndDev nfrumkin


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

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

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

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

🏷Name: Codestral (Code + Mistral 😂)

🚀 22B parameters

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

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

🏆 Outperforms DeepSeek Coder 33B on HumanEval

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

🤖 Supports both code assistant and code completion use cases

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

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

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

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


What We Learned from a Year of Building with LLMs

It's a nice perspective outlined in here.

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

— Goodhart’s Law

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


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


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

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


Relightable Gaussian Codec Avatars (2312.03704)

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

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

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

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

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

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

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

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

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

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

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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:

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