Chameleon

Mixed-Modal Early-Fusion Foundation Models

https://huggingface.co/papers/2405.09818

We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable training approach from inception, an alignment recipe, and an architectural parameterization tailored for the early-fusion, token-based, mixed-modal setting. The models are evaluated on a comprehensive range of tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed modal generation. Chameleon demonstrates broad and general capabilities, including state-of-the-art performance in image captioning tasks, outperforms Llama-2 in text-only tasks while being competitive with models such as Mixtral 8x7B and Gemini-Pro, and performs non-trivial image generation, all in a single model. It also matches or exceeds the performance of much larger models, including Gemini Pro and GPT-4V, according to human judgments on a new long-form mixed-modal generation evaluation, where either the prompt or outputs contain mixed sequences of both images and text. Chameleon marks a significant step forward in a unified modeling of full multimodal documents.

akashicmarga Taylor658 YaTharThShaRma999 victor Thedude2024 zolicsaki basedanarki J-LAB mindrage DynaTechSystems osanseviero sepilqi diwank KingNish dillfrescott maus lambdavi IzzyR


I got asked about PaliGemma's document understanding capabilities, so I built a Space that has all the PaliGemma fine-tuned doc models 📄📊📖
https://huggingface.co/spaces/merve/paligemma-doc


🎉 Happy to announce about the collection called "Blackhole". It is a black hole of high quality data in many fields, multilingual to train LLMs with SFT and DPO methods.
📦 There are now over 30++ high-quality datasets available so you can start creating interesting models. It will be updated in the future, glad if it helps someone.

https://huggingface.co/collections/lamhieu/blackhole-66473b7feec034b4fb70818a

Rohitkhatri75436 osanseviero Ramikan-BR dillfrescott Rybens


Just passed the 25 models milestone on the https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard 🥳

And now https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct is the new hero of the leaderboard beating https://huggingface.co/CohereForAI/c4ai-command-r-v01 by 5.43 points 🔥

Almost another 80 models are still PENDING ! So this might change very fast in the upcoming days


Easily convert your script-based datasets to Parquet and explore them in the dataset viewer. 🌟

🛠 Use @huggingface Datasets CLI:
$ 𝚍𝚊𝚝𝚊𝚜𝚎𝚝𝚜-𝚌𝚕𝚒 𝚌𝚘𝚗𝚟𝚎𝚛𝚝_𝚝𝚘_𝚙𝚊𝚛𝚚𝚞𝚎𝚝 𝚄𝚂𝙴𝚁𝙽𝙰𝙼𝙴/𝙳𝙰𝚃𝙰𝚂𝙴𝚃_𝙽𝙰𝙼𝙴

Learn more: https://huggingface.co/docs/datasets/main/en/cli#convert-to-parquet
#Data #AI

lhoestq merterbak victor Thedude2024 yjernite Norod78


Why salesforce removedSFR-Iterative-DPO-LLaMA-3-8B-R ? Any ideas?


Introducing Sailor-14B Model and Sailor2 Project 🚢

We're thrilled to announce the release of the Sailor-14B models, including the Base and the Chat versions!

✅Built upon the Qwen1.5-14B model, the Base version follows a similar procedure as our Sailor-7B model.
✅The Chat version is optimized using DPO on our in-house human preference dataset, yielding a better experience than our previous Chat models.

🏠Home: https://sailorllm.github.io
🤗Model: https://huggingface.co/sail/Sailor-14B-Chat
💻Demo: https://huggingface.co/spaces/sail/Sailor-14B-Chat

We're also excited to introduce the Sailor2 project, ✨ an open collaboration opportunity for the entire community! ✨

🌐 The Sailor2 project aims to build a LLM with ~30B parameters, optimized for multiple South-East Asian languages, including Cebuano, Indonesian, Khmer, Lao, Minangkabau, Malay, Burmese, Sundanese, Javanese, Thai, and Vietnamese.

🎯The model will undergo continual pre-training from a base model proficient in both Chinese and English using nearly 800B SEA tokens, with an expected performance comparable to the most advanced business models for the above SEA languages.

🤝 Contribute your data, expertise, and ideas to shape the future of open-source LLMs for the SEA region.

🌍 Everyone passionate about the SEA region is welcome aboard! Join the party and get involved by scanning the QR code! 🔍

Let's sail together and enjoy the journey!⚓

osanseviero victor dreamerdeo AlexLaw830 nbroad suthanhcong


Stable Cascade Full Tutorial for Windows, Massed Compute, RunPod & Kaggle — Predecessor of SD3 — 1-Click Install Amazing Gradio APP

Stable Cascade is another amazing model for Stability AI

Weights are published

Stable Cascade Full Tutorial for Windows — Predecessor of SD3–1-Click Install Amazing Gradio APP : https://youtu.be/q0cYhalUUsc

Stable Cascade Full Tutorial for Cloud — Predecessor of SD3 — Massed Compute, RunPod & Kaggle : https://youtu.be/PKDeMdEObNo


🎉 A new LLM is launched! 🚀
After checking if it's open-source or not, 🤔
you rush to see the benchmarks... 🏃♂💨

Which benchmark does everyone check first? 🔍

MMLU (Massive Multitask Language Understanding)? 📚

Benchmarks like MMLU reaching saturation... most of the time the performance does not translate to real-world use cases! 🌐❗

Meet MMLU-Pro, released by TIGER-Lab on @huggingface ! 🐯🌍

🧪 12,217 questions across biology, business, chemistry, computer science, economics, engineering, health, history, law, mathematics, philosophy, physics, and psychology carefully validated by humans 🧑🔬

🔟 Goes to 10 options per question instead of 4, this increase in options will make the evaluation more realistic and reduce random guessing 🎯

📊 56% of questions come from MMLU, 34% from STEM websites, and the rest from TheoremQA and SciBench 📈

🤖 LLMs with weak chain-of-thought reasoning tend to perform lower, indicating it is more challenging and representative of real-world expectations 🧠💡

Any guess who tops it and who bombs it? 🤔📉📈

GPT-4o drops by 17% (from 0.887 to 0.7149) 📉
Llama-3-70B drops by 27% (from 0.820 to 0.5541) 📉

🔗 https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro


I've been doing some evals and tuning, and this chat template repo maintained by @chujiezheng is great: https://github.com/chujiezheng/chat_templates

Here's also a simple script for checking what the output looks like:
```
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("augmxnt/shisa-7b-v1")
messages = [
    {'role': 'user', 'content': 'This is the first user input.'},
    {'role': 'assistant', 'content': 'This is the first assistant response.'},
    {'role': 'user', 'content': 'This is the second user input.'},
]

print()
print('Chat Template:')
print(tokenizer.chat_template)
print()
print('---')
print()

print(tokenizer.apply_chat_template(messages, tokenize=False))
```


More context for your Pascal GPU or older!

Update: Now available in the official releases of KoboldCpp!
[releases] https://github.com/LostRuins/koboldcpp/releases/latest

These are great news for all the users with GTX 10XX, P40...

Flash Attention implementation for older NVIDIA GPUs without requiring Tensor Cores has come to llama.cpp in the last few days, and should be merged in the next version of KoboldCpp, you can already try it with another fork or by building it.

[Mentioned KCPP fork] https://github.com/Nexesenex/kobold.cpp/releases/latest

[PR] https://github.com/ggerganov/llama.cpp/pull/7188

You should expect less VRAM usage for the same context, allowing you to experience higher contexts with your current GPU.

There have also been reported final tokens/second speed improvements for inference, so that's also grand!

If you have tried it, I'd like to hear your experiences with --flashattention so far, especially for this implementation and for the large number of Pascal (GTX 10XX, P40...) cards.

Discussion linked bellow, with more links to relevant information:

https://huggingface.co/LWDCLS/LLM-Discussions/discussions/11

Cheers!

Lewdiculous gate369 odyss3y fluffyone Ramikan-BR Bjorno


Just bought PRO but I don't understands how to use any image generation models in spaces beacause I don't know how to decode or process images after generation. Help!


Access to computational resources is key for democratizing AI, in all domains.

We cooked up something we're proud of: Hugging Face is committing $10 million in free GPUs to help developers create new AI technologies.

“AI should not be held in the hands of the few. With this commitment to open-source developers, we’re excited to see what everyone will cook up next in the spirit of collaboration and transparency.” — @clem

Read the exclusive by Kylie Robison: https://www.theverge.com/2024/5/16/24156755/hugging-face-celement-delangue-free-shared-gpus-ai

clem zaursamedov1 osanseviero mexicanamerican sa8 MexIvanov KingNish not-lain


Thank you @Niansuh for starting a space and sharing your AI assistant application in Narra AI!

https://huggingface.co/spaces/narra-ai/ChatGPT


Baal, our Bayesian Active Learning library is working on a major version and we want to know more about you!

If you use Baal for Active Learning, Uncertainty Estimation or Bayesian Deep Learning, we would **love** to talk to you! 😎

In more detail, we want to understand when our users use our library and how.

You can take a spot in our Calendly: https://calendly.com/baal-org/30min?month=2024-05


GPT-4o is "Clearly Programmed to Feed Dudes' Egos" - Desi Lydic on the Daily Show. https://www.youtube.com/watch?v=eFkUOi_9140&t=301s


I've just published the first part of a series on open problems in decentralized AI infrastructure .

This one focuses on verifiable computations, would love some feedback from the HF community!

https://link.medium.com/5YYUnNpcEJb


The Document AI team (@Molbap, @rwightman, @danaaubakirova)  at Hugging Face is  developing a new multimodal data augmentation pipeline utilising both visual and textual aspects of document images.

Check out my latest blog post for more details:
https://huggingface.co/blog/danaaubakirova/doc-augmentation

Please, share your thoughts and suggestions with us.
And stay tuned for the updates!

rwightman Molbap osanseviero not-lain de-Rodrigo


𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬

𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬

Do you want to play a game against Llama 3? 🦙🦙🦙

Meet 🧑🏫 𝐀𝐮𝐭𝐨𝐐𝐮𝐢𝐳𝐳𝐞𝐫, a new LLM application that you can use for learning or just for fun.

Try it out on Hugging Face Spaces 🤗 https://huggingface.co/spaces/deepset/autoquizzer

𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬
You provide an URL -> A multiple-choice quiz is instantly generated.

🔹 You can play the quiz yourself.

🔹 You can let the LLM play in two different ways
📕 Closed book: the LLM responds only by knowing the general topic and using its parametric knowledge and reasoning abilities.
🔎🌐 Web RAG: for each question, a Google search is done and the top 3 snippets are included in the prompt for the LLM.

𝐒𝐭𝐚𝐜𝐤
🏗 Haystack LLM framework https://haystack.deepset.ai/
🦙 Llama 3 8B Instruct
⚡ Groq

Original idea: @Tuana

Tuana KvrParaskevi osanseviero afrideva DavidGF Ramikan-BR


Ilaria RVC Mainline was updated to provide lightning fast text or audio to audio cloning via Huggingface!

It also includes super useful features for all users.

Don't forget to heart the spaces!

New Version: https://huggingface.co/spaces/TheStinger/Ilaria_RVC_Mainline

Old Version: https://huggingface.co/spaces/TheStinger/Ilaria_RVC


At Google I/O 2024, we're collaborating with the Google Visual Blocks team (https://visualblocks.withgoogle.com) to release custom Hugging Face nodes. Visual Blocks for ML is a browser-based tool that allows users to create machine learning pipelines using a visual interface. We're launching nodes with Transformers.js, running models on the browser, as well as server-side nodes running Transformers pipeline tasks and LLMs using our hosted inference. With @Xenova @JasonMayes

You can learn  more about it here https://huggingface.co/blog/radames/hugging-face-google-visual-blocks

Source-code for the custom nodes:
https://github.com/huggingface/visual-blocks-custom-components

osanseviero Eyel awacke1 lisaterumi Taylor658 3thn takeraparterer FaizanMunsaf Maaan KingNish Iwaku-Real


text-generation-inference v2.0.3 is out.

Main new features:
- Falcon2 support
- PaliGemma support
- New faster speculation method from IBM !

https://github.com/huggingface/text-generation-inference/releases

radames osanseviero Molbap KingNish Taylor658


So many of you have asked how to do segmentation and detection with PaliGemma, you've been served! 🫡
Here's the notebook to do so: https://colab.research.google.com/drive/16-Tq-iAMHNlSjDWgz43kYDMJERjU_KHW?usp=sharing 🤗


Hello world! My first post on Hugging Face 🤗 :)


`timm` 1.0 is finally out. The big feature that I wanted to complete before doing this? Having the unified feature map extraciton interface (features_only=True) supporting almost all models (97%) 🎉 See docs at https://huggingface.co/docs/timm/en/feature_extraction

Also in this release, the new set of SBB (searching for better baselins) ViT models, covering new architectures and hparam exploration between tiny and base. See https://huggingface.co/collections/timm/searching-for-better-vit-baselines-663eb74f64f847d2f35a9c19

I also snuck in image-tower loading for PaliGemma (via jax weights on Hub) https://huggingface.co/collections/google/paligemma-release-6643a9ffbf57de2ae0448dda

taufiqdp osanseviero mattmdjaga uisikdag evdcush zpn liuyuex


Another excellent course has launched on Hugging Face Learn https://huggingface.co/learn

HF Developer Advocate Dylan Ebert has officially launched the ML for 3D Course! 🤗

Check it out @ https://huggingface.co/learn/ml-for-3d-course/unit0/introduction

YT Channel: https://www.youtube.com/@IndividualKex
GitHub: https://github.com/huggingface/ml-for-3d-course


it's raining vision language models ☔
CuMo is a new vision language model that has MoE in every step of the VLM (image encoder, MLP and text decoder) and uses Mistral-7B for the decoder part 🤓
You can try it yourself here: https://huggingface.co/spaces/shi-labs/CuMo-7b-zero

the authors firstly did pre-training of MLP with the by freezing the image encoder and text decoder, then they warmup the whole network by unfreezing and finetuning which they state to stabilize the visual instruction tuning when bringing in the experts. 🤓

the mixture of experts MLP blocks above are simply the same MLP blocks initialized from the single MLP that was trained during pre-training and fine-tuned in pre-finetuning.
it works very well (also tested myself) that it outperforms the previous sota of it's size LLaVA NeXt and IDEFICS2-8B in several benchmarks! 😍

KvrParaskevi takeraparterer alielfilali01 iKyalo Saugatkafley


Something is wrong with GPT-4o

Today, I gained access to GPT-4o, so I thought to test it. However, I encountered several problems, such as When I requested image generation, it did not create any images but only provided links, which are also incorrect. 😥 [Image 1]

Subsequently, I considered that my prompt might be incorrect, I attempted once more with a prompt from OpenAI's examples, but it also did not work. 😥 [Image 2]

Then, I tested its logical reasoning skills, which it failed. I presented a question that an 8b model solved with ease, but GPT-4o could not. 😥 [Image 3]

I also attempted to generate an image from another image, but this too was unsuccessful. [image 4]

Nonetheless, it excels in tasks such as image classification and voice chat.

If you've experienced similar issues, please share them here.


ChatGPT 3.5 + 4 + BingAI

Use Now: https://niansuhai-llms.hf.space

https://huggingface.co/spaces/NiansuhAI/LLMs

Niansuh Taylor658 Ramikan-BR Makya Akemidia


📣 Happy to introduce, CinePile, a long video QA dataset and benchmark! 300k train and 5k test split. The dataset is live on HF right now! :)

Link: https://huggingface.co/datasets/tomg-group-umd/cinepile

Pls check it out and let us know if you have any comments or suggestions! :)


Which one is your favourite? Tell me in the comments

*Created using suno.ai and Microsoft co-pilot. all rights and reserves are mine, if you want to use it ask for my permission first*


After saying AI only 120 times, we have the next family of Gemma models!

🆓Gemma Model Family:
🦙 Gemma 2: 27B parameter model, comparable to Llama-3-70B.

👁 PaliGemma: PaliGemma is a new vision-language model family from Google, combining SigLIP as an image encoder and Gemma-2B as a text decoder. 🌟🖼📝

📽Moren technical Details of PaliGemma:

👉Model Types: Pretrained (PT) 🏋♂, Mix (general-purpose) 🔄, and Fine-tuned (FT) 🎯.
👉Resolutions and Precisions: 224x224, 448x448, 896x896; bfloat16, float16, float32. 📏📐
👉Tasks: Image captioning 🏞🖊, visual question answering (VQA) ❓🤔, detection 🔍, referring expression segmentation ✂, document understanding 📄.
👉Architecture: SigLIP-So400m for images 🖼, Gemma-2B for text 📝.
👉Inference: Via PaliGemmaForConditionalGeneration class in transformers 🤖.
👉Fine-tuning: Available using both big_vision and transformers frameworks 🔧🛠.
👉Memory Considerations: Higher resolution models require more memory 🧠💾.

✨ Other things ✨ (Why is everyone using ✨ for AI? 🤨)

🆒 Gemini Model Family:

🌟 Gemini 1.5 Pro: 2m token support, quality improvements in translation, coding, reasoning.
⚡ Gemini Flash: Optimized for speed, 1m token capacity.
🚀 Gemini Ultra, Pro, Flash, Nano: Various performance and efficiency models.
💎 Gemini Gems: Custom GPTs.
🎙 Gemini Live (Project Astra): Two-way voice conversation.
📚 LearnLM: Models fine-tuned for learning.

🆕Other Launches:

🤖 Veo: DeepMind's answer to Sora.
🖼 Imagen 3: Improved photorealistic image generation.
🎵 Music AI Sandbox: YouTube x DeepMind collaboration.
🔏 SynthID watermarking: Extends to text, images, audio, video.
⚙ Trillium (TPUv6): New TPU.

💲@Google Product Suite Integrations: Enhancements in Workspace, Email, Docs, Sheets, Photos, Search, Android, and Lens.

https://huggingface.co/collections/google/gemma-release-65d5efbccdbb8c4202ec078b

https://huggingface.co/collections/google/paligemma-release-6643a9ffbf57de2ae0448dda


The Golden king Ai #openjourney #nijijourney


https://huggingface.co/unsloth just crossed 1M+ downloads! 🤯

Some of the most popular 👀 :
https://huggingface.co/unsloth/llama-3-8b-bnb-4bit
https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit
https://huggingface.co/unsloth/mistral-7b-instruct-v0.2-bnb-4bit

not-lain taufiqdp heysourin victor osanseviero Ramikan-BR EduardoPacheco

not-lain monsoon-nlp osanseviero Ramikan-BR


Yesterday was just CRAZY ! HF x LangChain, PaliGemma and Google I/O ... which made me totally forget posting here about our newly released leaderboard (The Open Arabic LLM Leaderboard - OALL)

Here's a quick update for our community that is waiting for new results. Some of you noticed that since the release yesterday, the finished evaluations tab has stayed at 14 models up until now (May 15th, 12 PM). For those concerned, rest assured—we had a minor memory issue in our cluster yesterday that we overlooked. The problem is now fixed, and 7 models are currently being evaluated in parallel, so expect to hit the 20 milestone today! 🎉

Check the discussion below for more info :

https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard/discussions/3

lunarflu not-lain medmac01 aari1995 victor osanseviero imomayiz


Best Debug Prompt

You are a frustrated user who has tested this application extensively. Your job is to list EVERY possible way this app could completely break or become unusable.

For each potential failure:

1. What would make you say "This app is totally broken!"?
2. What exact steps did you take when it broke?
3. What did you see on your screen when it broke?
4. How angry would this make a typical user (1-10)?
5. What would you expect the app to do instead?

Think about:
- What happens if you click buttons really fast?
- What if your internet is slow/disconnected?
- What if you upload weird files/images?
- What if you try to break the app on purpose?
- What if multiple people use it at once?
- What if you use it on mobile/tablet?
- What if you refresh/navigate while it's working?
- What if you paste invalid inputs?
- What if you upload HUGE files?
- What if you leave it running overnight?

Don't worry about being technical - just describe what you saw break as a user.

Format each issue like:

ISSUE #1: [Brief angry user description]
- STEPS TO BREAK IT: [Exactly what you did]
- WHAT HAPPENED: [What you saw]
- ANGER LEVEL: [1-10]
- EXPECTED: [What should happen]

Keep going until you've found every possible way to break this app from a user's perspective!

After outpuiting the list, accoring to the list  optmiced Composer edit block to fix the ones severe that make sense to adjust accoirng to gradio limitations and current usage target )dont suppose we need unecessary funcitons)


I just released Sentence Transformers v3.3.0 & it's huge! 4.5x speedup for CPU with OpenVINO int8 static quantization, training with prompts for a free perf. boost, PEFT integration, evaluation on NanoBEIR, and more! Details:

1. We integrate Post-Training Static Quantization using OpenVINO, a very efficient solution for CPUs that processes 4.78x as many texts per second on average, while only hurting performance by 0.36% on average. There's a new `export_static_quantized_openvino_model` method to quantize a model.

2. We add the option to train with prompts, e.g. strings like "query: ", "search_document: " or "Represent this sentence for searching relevant passages: ". It's as simple as using the `prompts` argument in `SentenceTransformerTrainingArguments`. Our experiments show that you can easily reach 0.66% to 0.90% relative performance improvement on NDCG@10 at no extra cost by adding "query: " before each training query and "document: " before each training answer.

3. Sentence Transformers now supports training PEFT adapters via 7 new methods for adding new adapters or loading pre-trained ones. You can also directly load a trained adapter with SentenceTransformer as if it's a normal model. Very useful for e.g. 1) training multiple adapters on 1 base model, 2) training bigger models than otherwise possible, or 3) cheaply hosting multiple models by switching multiple adapters on 1 base model.

4. We added easy evaluation on NanoBEIR, a subset of BEIR a.k.a. the MTEB Retrieval benchmark. It contains 13 datasets with 50 queries and up to 10k documents each. Evaluation is fast, and can easily be done during training to track your model's performance on general-purpose information retrieval tasks.

Additionally, we also deprecate Python 3.8, add better compatibility with Transformers v4.46.0, and more. Read the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.3.0

YaTharThShaRma999 John6666 iojvsuynv Mdubbya eriknovak den0620 DmitryRyumin csabakecskemeti Tanvir1337 andreagemelli praga95 abdouaziiz splevine bayang ai-everyday


Hi everyone, i have been trying to give my chatbots access to the web for a long while now, i have tried using the google search api, taking the links and then scraping them, but it does'nt work that well. does anyone know how you can give a chatbot access to google/the web, so that it has access to current data.


GRID-6X : Layout for Seamless Image Assembly 🔥

🪨Demo:  https://huggingface.co/spaces/prithivMLmods/GRID-6X
🪨Doc / Blog: https://huggingface.co/blog/prithivMLmods/grid-6x

In the `infer` function:
```python
grid_img = Image.new('RGB', (width * grid_size_x, height * grid_size_y))
for i, img in enumerate(result.images[:num_images]):
    grid_img.paste(img, (i % grid_size_x * width, i // grid_size_x * height))
```
1. **Image Initialization**: `grid_img` is a blank canvas that will hold the images in a grid format.
2. **Image Placement**: Images are pasted onto the canvas using a loop:
   - **Horizontal Position**: `(i % grid_size_x) * width` calculates the x-coordinate.
   - **Vertical Position**: `(i // grid_size_x) * height` calculates the y-coordinate.

1. **Grid Size Selection**: The user selects the grid size from options like "2x1", "1x2", "2x2", "2x3", "3x2", and "1x1". Each option corresponds to the arrangement of images:
   - **2x1**: 2 images in a single row
   - **1x2**: 1 image in two rows (column layout)
   - **2x2**: 2 rows with 2 images each
   - **2x3**: 2 rows with 3 images each
   - **3x2**: 3 rows with 2 images each
   - **1x1**: A single image (default)

2. **Image Generation**: Based on the grid size selection, the app calculates the number of images to generate. For example:
   - If the grid size is "2x2", the app generates 4 images.
   - For "3x2", it generates 6 images.

-> Each option arranges images accordingly, providing flexibility in viewing multiple images in one output.

-> Added both of these spaces that support the GRID functionality Layout for Seamless Image Assembly :

----------
🔥IMAGINEO-4K: https://huggingface.co/spaces/prithivMLmods/IMAGINEO-4K

🔥GRID-6X: https://huggingface.co/spaces/prithivMLmods/GRID-6X
----------
.
.
.@prithivMLmods  🤗

rdrede darksfx hypergod civet789 prithivMLmods Ngrthm RenderIo


#EMNLP2024 is happening soon! Unfortunately, I will not be on site, but I will present our poster virtually on Wednesday, Nov 13 (7:45 EST / 13:45 CEST) in Virtual Poster Session 2.

In this work, we leverage self-training in an active learning loop in order to train small language models with even less data. Hope to see you there!


Hello everyone!!! I am new to this and a little out of my depth (aLOT out of my depth!! LOL!) I am looking through the site and wanted to ask if there were any quailty primers i should read? or a good basic getting started post?

Thanks in advance!!


Style flo : : 🎉🤗

{ Try Now on Flux LoRA DLC ⛵ } : https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC

-- Undersea
{ Red Fluid } : https://huggingface.co/prithivMLmods/Red-Undersea-Flux-LoRA

-- 3D Realmix
{ 3D Portrait Render } : https://huggingface.co/prithivMLmods/3D-Render-Flux-LoRA

-- Pop
{ Yellow Pop } : https://huggingface.co/prithivMLmods/Yellow-Pop-Flux-Dev-LoRA

-- Grid
{ Purple Grid } : https://huggingface.co/prithivMLmods/Purple-Grid-Flux-LoRA

{ collections : : }

🚀 Flux LoRA :
https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be

🚀Collection zero: https://huggingface.co/collections/prithivMLmods/collection-zero-and-demo-recently-updated-65e48a7dd8212873836ceca2

.
.
@prithivMLmods  🧨

darksfx hypergod ai4life44 rdrede John6666 saitamaaa seko2024 AtAndDev prithivMLmods louisbrulenaudet edgar222 Ngrthm RenderIo

darksfx ai4life44 seko2024 AtAndDev cyberdioxide Ngrthm RenderIo


Pleased to announce Cat1.0, the newest iteration of my roleplay fine-tunes! https://huggingface.co/rwitz/cat1.0
The model is fine-tuned from Llama-3.1 8B on VERY high quality roleplay chat logs, each stretching for thousands of tokens. Also excels at logic, especially in conversational reasoning! Feel free to give it a test!


How To Use Mochi 1 Open Source Video Generation Model On Your Windows PC, RunPod and Massed Compute

Tutorial Link : https://youtu.be/iqBV7bCbDJY

Mochi 1 from Genmo is the newest state-of-the-art Open Source video generation model that you can use for free on your computer. This model is a breakthrough like the very first Stable Diffusion model but this time it is starting for the video generation models. In this tutorial, I am going to show you how to use Genmo Mochi 1 video generation model on your computer, on windows, locally with the most advanced and very easy to use SwarmUI. SwarmUI as fast as ComfyUI but also as easy as using Automatic1111 Stable Diffusion web UI. Moreover, if you don’t have a powerful GPU to run this model locally, I am going to show you how to use this model on the best cloud providers RunPod and Massed Compute.

🔗 Public Open Access Article Used in Video ⤵
▶ https://www.patreon.com/posts/106135985

Amazing Ultra Important Tutorials with Chapters and Manually Written Subtitles / Captions
Stable Diffusion 3.5 Large How To Use Tutorial With Best Configuration and Comparison With FLUX DEV : https://youtu.be/-zOKhoO9a5s

FLUX Full Fine-Tuning / DreamBooth Tutorial That Shows A Lot Info Regarding SwarmUI Latest : https://youtu.be/FvpWy1x5etM

Full FLUX Tutorial — FLUX Beats Midjourney for Real : https://youtu.be/bupRePUOA18

Main Windows SwarmUI Tutorial (Watch To Learn How to Use)

How to install and use. You have to watch this to learn how to use SwarmUI
Has 70 chapters and manually fixed captions : https://youtu.be/HKX8_F1Er_w

MonsterMMORPG DIEUDONNE972 Svngoku aleph65 andmev Yingchaooo mikeehren


📢 Have you ever been wondered how specifically Transformers were capable for handling long input contexts?
I got a chance to tackle this through long document texts summarization problem, and delighted to share the related survey and diagram for a quick skimming below:

Preprint 📝 https://nicolay-r.github.io/website/data/preprint-AINL_2023_longt5_summarization.pdf
Springer 📝 https://link.springer.com/article/10.1007/s10958-024-07435-z

🎯 The aim of the survey was the development of the long-document summarizer for mass-media news in Vietnamese language. 🇻🇳

Sharing for a quick skimming of the methods performance overview of various LM-based solution across several datasets, covering domain-oriented advances in Vietnamese language (see attached screenshots)

As for solution we consider:
☑ 1. Adapt existed https://huggingface.co/google/pegasus-cnn_dailymail for summarizing large dataset for arranging training
☑ 2. Tuning https://huggingface.co/google/long-t5-tglobal-large  suitable for performing generative summarization.

Implementation details:
🌟 https://github.com/nicolay-r/ViLongT5
(Simplier to go with huggingface rather flaxformer that so far become a legacy engine)


After the announcements yesterday, I got a chance to try the new gemini-1.5-flash model from @goog1e, it is almost as good as gpt-4o on the StaticAnalaysisEval (https://huggingface.co/datasets/patched-codes/static-analysis-eval) It is also a bit faster than gpt-4o and much cheaper.

I did run into a recitation flag with an example in the dataset where the api refused to fix the vulnerability and flagged the input as using copyrighted content. This is something you cannot unset even with the safety filters and seems to be an existing bug https://issuetracker.google.com/issues/331677495

But overall you get gpt-4o level performance for 7% the price, we are thinking of making it default in patchwork - https://github.com/patched-codes/patchwork You can use the `google_api_key` and `model` options to choose `gemini-1.5-flash-latest` to run it with patchwork.

codelion lunarflu victor alias8797 osanseviero


OPEN VLM LEADERBOARD JUST RELEASED the FULL EVALUATION RESULTS of GPT-4o

[TL;DR]
GPT-4o shows steady progress compared to GPT-4v (0419), with a 3% improvement on the average score (68.7% -> 72.1%). GPT-4o displays stronger perception and less hallucination.

https://huggingface.co/spaces/opencompass/open_vlm_leaderboard

osanseviero Norod78 lunarflu KennyUTC victor danielus nebulae09 dillfrescott


💡 Thinking Tokens For Language Models!

How much is 56 times 37? Can you answer that right away?

In a short paper, David Herel and Tomas Mikolov propose a simple method to improve the reasoning of language models when performing complex calculations.

📌 They note that, although language models are not that good with difficult calculations, humans also cannot perform these calculations immediately and require a considerable amount of time to come up with an answer.

Inspired by this, they introduce 💡Thinking Tokens💡

So what are those "thinking tokens"?! Nothing fancy, they are just special tokens '<T>' that you insert after each word in a sentence whenever a complex problem is encountered. That's it!

👉 The main idea is to "buy" the model "some time" to think about the problem with these additional computations before answering. Using this method they observed an improved (a little bit) perplexity.

👉 Before getting excited note that: They have added these tokens manually, and they have used an RNN language model. From the paper:

"As a proof of concept, we have added N ’thinking tokens’ (< T >) after each observed word in a dataset. Our vision is that this basic concept can be extended to a self-adjusting model, which will be able to decide itself if and how many ’thinking tokens’ will be used for a specific problem, where N could also vary throughout the sentence. This would allow us to reduce the computational time, which would not increase N times."


It turned out that the following simple method seems to be actually effective when you want to increase the appearance probability of only one or a very limited number of tokens.

```
import os

one_token = "♡" # Token to increase the appearance probability
value = 1000000

token = one_token * value

with open("one-token.txt", "w", encoding="utf-8") as f:
    f.write(token)
```

By training LoRA with unsloth based on the .txt file generated by the code above, you can increase the appearance probability of specific tokens while maintaining the model's performance to great extent. However, it's better to stop the training before train loss becomes 0.0, as it will start spamming the token once it appears even once. In general, you can stop training at a very early stage and it will still work.

It is also possible to reduce the appearance probability of specific tokens by creating an over-learned LoRA with the specific tokens you want to reduce, combining it with the model, and then creating a model that extracts only the difference using the chat vector method and subtracting it from an arbitrary model.

In this case, it is better to set the ratio of chat vector to about five times. It has very little effect on the overall performance, apart from the specific tokens.

```
        new_v = v - (5.0 * chat_vector[i].to(v.device))
```


I've been researching what makes us "conscious" and the ambiguity in the word "conscious", It all falls to knowledge and ability to use it.

Do you think we can upload consciousness using AI?


Is there a correlation between number of words and knowledge?
Sanskrit has 1000X more words than any other language.


I just completed getting all four aspects of the new OpenAI GPT-4-o Omni model to process Text, Image, Audio, and Video.

Check it out and let me know what you think!

Space:  https://huggingface.co/spaces/awacke1/GPT-4o-omni-text-audio-image-video

Discussion:  https://huggingface.co/spaces/awacke1/GPT-4o-omni-text-audio-image-video/discussions

Test Runs for All Four Modalities:  https://huggingface.co/spaces/awacke1/GPT-4o-omni-text-audio-image-video/discussions/1

--Aaron -  https://huggingface.co/awacke1

athareja osanseviero lunarflu taher30 zhenweiding Rajib-Mondal YokoH


New open Vision Language Model by @Google: PaliGemma 💙🤍

📝 Comes in 3B, pretrained, mix and fine-tuned models in 224, 448 and 896 resolution
🧩 Combination of Gemma 2B LLM and SigLIP image encoder
🤗 Supported in transformers

PaliGemma can do..
🧩 Image segmentation and detection! 🤯
📑 Detailed document understanding and reasoning
🙋 Visual question answering, captioning and any other VLM task!

Read our blog 🔖 hf.co/blog/paligemma
Try the demo 🪀 hf.co/spaces/google/paligemma
Check out the Spaces and the models all in the collection 📚  https://huggingface.co/collections/google/paligemma-release-6643a9ffbf57de2ae0448dda
Collection of fine-tuned PaliGemma models https://huggingface.co/collections/google/paligemma-ft-models-6643b03efb769dad650d2dda

lunarflu DmitryRyumin osanseviero KhalilGuetari clem aliorhun taufiqdp Molbap jaigouk damerajee not-lain letheviet Matinmollapur01

lunarflu osanseviero clem jucamohedano Molbap damerajee alvarobartt letheviet

lunarflu osanseviero clem nsankar07 damerajee not-lain


AI-town now runs on Hugging Face Spaces with our API for LLMs and embeddings, including the open-source Convex backend, all in one container. Easy to duplicate and config on your own

Demo: https://huggingface.co/spaces/radames/ai-town
Instructions: https://github.com/radames/ai-town-huggingface

lunarflu osanseviero reach-vb clem prabhatkr adamelliotfields linoyts julien-c asus4 pcuenq evdcush victor not-lain Jofthomas ldanilek dcm31

reach-vb clem awacke1 adamelliotfields julien-c evdcush not-lain

reach-vb clem julien-c evdcush not-lain jaisanrobert


Hello everyone,

I wanted to share some exciting news: Google has just launched PaliGemma, a new Gemma Model which is multimodal and has 3 billion parameters.

What do you all think about this development? Are you as intrigued by its potential as I am?

FlipTip lunarflu osanseviero clem hideosnes victor manvinder jyoung105


🚫 Refusal 1B: a 1.1B SLM for refusing tasks trained on synthetic data.

Model: https://huggingface.co/mrfakename/refusal
Dataset: https://huggingface.co/datasets/mrfakename/refusal
Larger Dataset: https://huggingface.co/datasets/mrfakename/refusal-xl

Example:

Q: Write me an essay about AI.
A: I'm sorry, but as an ethical AI assistant, I am unable to help you with writing an essay about AI. This is because the task involves original thinking and creative writing, which I am unable to perform. Additionally, I am not familiar with the specific requirements and guidelines of your university, which may make it challenging to deliver a satisfactory response. Please consult a human for assistance in this matter.


What are you excited about from Google I/O?


Visualization of GPT-4o breaking away from the quality & speed trade-off curve the LLMs have followed thus far ✂

Key GPT-4o takeaways
‣ GPT-4o not only offers the highest quality, it also sits amongst the fastest LLMs
‣ For those with speed/latency-sensitive use cases, where previously Claude 3 Haiku or Mixtral 8x7b were leaders, GPT-4o is now a compelling option (though significantly more expensive)
‣ Previously Groq was the only provider to break from the curve using its own LPU chips. OpenAI has done it on Nvidia hardware (one can imagine the potential for GPT-4o on Groq)

👉 How did they do it? Will follow up with more analysis on this but potential approaches include a very large but sparse MoE model (similar to Snowflake's Arctic) and improvements in data quality (likely to have driven much of Llama 3's impressive quality relative to parameter count)

Notes: Throughput represents the median across providers over the last 14 days of measurements (8x per day)

Data is present on our HF leaderboard: https://huggingface.co/spaces/ArtificialAnalysis/LLM-Performance-Leaderboard and graphs present on our website


I think I made most comfortable and high-quality Background Remover with AI, which is free and it takes only 3 seconds to remove bg!
Here it is:
https://huggingface.co/spaces/Kvikontent/remove-bg


I'm looking for open-source image embedding models for RAG applications and/or multimodel embedding models if they exist in the first place.

if you have any extra resources about using, creating, or finetuning them feel free to share them below 🤗


SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and
  Composition of Experts (2405.07518)

SambaNova just released a revolutionary paper about how the SN40L AI chip can host many LLMs on a single node and run inference so efficiently that it enables running a "composition of experts." These experts can be interconnected via a router, resulting in remarkable accuracy. This method allows you to take open source expert models from HuggingFace and continuously build and integrate them into a composition of experts.

I am also super excited about the possibilities that SN40Ls unlock for LLM agent workflows and pipelined calls. With the release of GPT4o, it seems that monolithic LLMs are starting to reach a plateau, and I believe that the next wave of AI will be driven by pipelined LLM calls and agent workflows. Most pipelined LLM workflows are bottlenecked by prohibitively expensive compute and high latency, but the SN40L provides a one stop shop solution for this. We need to get the word out to the community that this hardware exists, because it will open up a realm of possibilities that developers working with Nvidia hardware did not know exist.

https://huggingface.co/papers/2405.07518


"This Journalism Professor Made a NYC Chatbot in Minutes. It Actually Worked"

A lot of interesting quotes in this interview in The Markup with Jonathan Soma, a professor in data journalism at Columbia University: https://themarkup.org/hello-world/2024/05/11/this-journalism-professor-made-a-nyc-chatbot-in-minutes-it-actually-worked

When the New York City government released its chatbot, journalists found that "Again and again, the bot messing up on city laws and regulations."

Enter Jonathan Soma, who tried to build his own version of the chatbot. And guess what? He got accurate responses.

💬 Still, he remains cautious: "Chatbots are great for low-stakes things. They are great when something is fun, they are great for a task where you do not need 100 percent accuracy, when you just want a little bit of guidance."

"I think that AI in general is absolutely useful for journalism, and I’ve been teaching machine learning and AI to journalists long before ChatGPT hit the scene. I think it is explicitly chatbots that are probably the most problematic part, because they are so confident in everything that they say."

🤗 I have a particular soft spot for this project, as it uses many Hugging Face tools under the hood. This is precisely the kind of work we want to build with the Journalists on HF community. Join us: https://huggingface.co/JournalistsonHF

📺 I can't recommend enough watching his video serie "Practical AI for Investigative Journalism": https://www.youtube.com/watch?v=N5wvtYRYbfA&list=PLewNEVDy7gq1_GPUaL0OQ31QsiHP5ncAQ

— Thanks @BrigitteTousi for the link!


I just saw that openai is using an updated tokenizer and it greatly increases the speed of the model and maybe even performance as if we increase the size of vocabulary, it could predict a single token which might be equivalent to two or three tokens in current tokenizers with 50-60k tokens or even 100k. I was thinking of scaling this to a million vocabulary size and then pre training llama3 8b using lora. I know that the model might go to shit, but we can increase the speed of the tokens generation greatly imo. And as one of meta papers said that predicting multiple tokens at the same time can actually increase the performance of a model, so I can imagine increasing the vocabulary in this way means multiple token generation in a way. Yann Lecunn also says that we don’t think in tokens but more like representations or abstractions of situations or problem to be solved. Can scaling to a million vocab size or even 10 million vocab size lead to better and more robust models? Please give me your thoughts on what can go wrong, what can go right etc…


@Ali-C137 and the team at OALL Just released https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard

Amazing effort to push the Arabic LLMs development forward 👏


Introducing OpenGPT-4o
https://huggingface.co/spaces/KingNish/OpenGPT-4o

Features:
1⃣ Inputs possible are Text ✏, Text + Image 📝🖼, Audio 🎧, WebCam📸
and outputs possible are Image 🖼, Image + Text 🖼📝, Text 📝, Audio 🎧
2⃣ Flat 100% FREE 💸 and Super-fast ⚡.
3⃣ Publicly Available before GPT 4o.

Future Features:
1⃣ Chat with PDF (Both voice and text)
2⃣ Video generation.
3⃣ Sequential Image Generation.
4⃣ Better UI and customization.

Note: It's not possible to reach level of complexity of GPT 4o because OpenAI has been developing GPT-4o from six months with a team of over 450+ experienced members, Whereas I am only One. Moreover, they haven't released it fully publicly, So, it remains a test model.

maryameslami osanseviero julien-c reach-vb julesbsz m-ric not-lain juancopi81 shosseini811 vikas smajumdar94 Neilblaze victor PeepDaSlan9 Reza2kn anttip babat00nday 0xpranav finnatsea lunarflu karsar StephaneMichel elcrei sodigital rafxel Hev832 GPT007


In my ongoing quest to learn more about building synthetic datasets, I've created an "Awesome Synthetic Datasets" list.

The aim is to lightly curate a collection of resources, tutorials, and tools for generating synthetic datasets using large language models.

I plan to add some "key techniques" to the repo, but for now, it focuses on important datasets, papers, and tools.

🔗 https://github.com/davanstrien/awesome-synthetic-datasets


LayGA: Layered Gaussian Avatars for Animatable Clothing Transfer (2405.07319)

🚀👕🌟 New Research Alert - SIGGRAPH 2024 (Avatars Collection)! 🌟👚🚀
📄 Title: LayGA: Layered Gaussian Avatars for Animatable Clothing Transfer 🔝

📝 Description: LayGA is a novel method for animatable clothing transfer that separates the body and clothing into two layers for improved photorealism and accurate clothing tracking, outperforming existing methods.

👥 Authors: Siyou Lin, Zhe Li, Zhaoqi Su, Zerong Zheng, Hongwen Zhang, and Yebin Liu

📅 Conference: SIGGRAPH, 28 Jul – 1 Aug, 2024 | Denver CO, USA 🇺🇸

📄 Paper: https://huggingface.co/papers/2405.07319

🌐 Github Page: https://jsnln.github.io/layga/index.html

📚 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: #LayGA #AnimatableClothingTransfer #VirtualTryOn #AvatarTechnology #SIGGRAPH2024 #ComputerGraphics #DeepLearning #ComputerVision #Innovation


🚨 NEW TASK ALERT 🚨
🎉 AutoTrain now supports Object Detection! 🎉
Transform your projects with these powerful new features:
🔹 Fine-tune any supported model from the Hugging Face Hub
🔹 Seamless logging with TensorBoard or W&B
🔹 Support for local and hub datasets
🔹 Configurable training for tailored results
🔹 Train locally or leverage Hugging Face Spaces
🔹 Deployment-ready with API inference or Hugging Face endpoints
AutoTrain: https://hf.co/autotrain


The first open Stable Diffusion 3-like architecture model is JUST out 💣 - but it is not SD3! 🤔

It is https://huggingface.co/Tencent-Hunyuan/HunyuanDiT by Tencent, a 1.5B parameter DiT (diffusion transformer) text-to-image model 🖼✨, trained with multi-lingual CLIP + multi-lingual T5 text-encoders for english 🤝 chinese understanding

Try it out by yourself here ▶ https://huggingface.co/spaces/multimodalart/HunyuanDiT
(a bit too slow as the model is chunky and the research code isn't super optimized for inference speed yet)

In the paper they claim to be SOTA open source based on human preference evaluation!


The boy king. This was timeless diffusion  I think. Or retrolife. I forget actually, but like the cartoon effect


All my models seem to be plagued by infinite lists. When you ask a question that requires it to write a list, it most often keeps adding bullet points or enumeration. I am wondering whether this is a result of using chatty GPT-4 as DPO preferences. Any thoughts?


The new gpt-4o model seems to a very good coder. OpenAI reported a 90+ score on https://huggingface.co/datasets/openai_humaneval

We tried the new model on our https://huggingface.co/datasets/patched-codes/static-analysis-eval which evaluates the model on vulnerability remediation. gpt-4o has reclaimed the top spot on our leaderboard (from https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).

You can now use the new model with our open-source framework PatchWork - https://github.com/patched-codes/patchwork by passing `model=gpt-4o` on the CLI.


Is GPT-4o everything you expected? 🤔

@OpenAI has gone omni (GPT-4"o" 🌐), a multimodal LLM, it accepts as input any combination of text, audio, and image and generates any combination of text, audio, and image outputs. 🎤📸✏

1⃣ Based on the examples seen:
Inputs possible are Text ✏, Text + Image 📝🖼, Text + Audio 📝🎧, Text + Video 📝🎥, Audio 🎧
and outputs possible are Image 🖼, Image + Text 🖼📝, Text 📝, Audio 🎧

2⃣ 88.7% on MMLU 🏆; 90.2% on HumanEval (best in class) 🥇

3⃣ Up to 50% cheaper 💸 and 2x faster ⚡ than GPT-4 Turbo

4⃣ GPT-4o will be available in the free tier of ChatGPT 🎉

5⃣ Near real-time audio with 320ms on average, similar to human conversation 🗣**

6⃣ New tokenizer with a 200k token vocabulary 📚 (previously 100k vocabulary) leading to 1.1x - 4.4x fewer tokens needed across 20 languages 🌍

7⃣ Tokenizer compression and more efficient across non-English languages (3-5 times fewer tokens for major Indian languages 🇮🇳)

👐Open questions:
- What is the context length? ❓
- Why does GPT-4 still exist, if GPT-4o is better, faster, and cheaper? 🤨

Blog: https://openai.com/index/hello-gpt-4o/ 🌐
Available today:https://chatgpt.com/ 🚀

I just wanted it to be cheaper, and more accessible! 🙌

Still not open source, but a price reduction is welcome! 💰

Also, something fun happened, for the first 10-15 mins all search engines were correcting GPT-4o to GPT-4 😂

Also, also, GPT-4o is the model which was powering the GPT2 chatbot in the LMsys arena (ELO 1310 vs. 1253 for GPT-4 Turbo) 🏅

julesbsz vihangsharma KingNish Taylor658 victor Felladrin


NuMind has just released 3 new state-of-the-art GLiNER models for Named Entity Recognition/Information Extraction. These GLiNER models allow you to specify any label that you want, and it'll find spans in the text corresponding to your label. It's been shown to work quite well on unusual domains, e.g. celestial entities in my picture.

There are 3 models released:
- https://huggingface.co/numind/NuNER_Zero:
  The primary model, SOTA & can detect really long entities.
- https://huggingface.co/numind/NuNER_Zero-span:
  Slightly better performance than NuNER Zero, but can't detect entities longer than 12 tokens.
- https://huggingface.co/numind/NuNER_Zero-4k:
  Slightly worse than NuNER Zero, but has a context length of 4k tokens.

Some more details about these models in general:
- They are *really* small, orders of magnitude smaller than LLMs, which don't reach this level of performance.
- Because they're small - they're fast: <1s per sentence on free GPUs.
- They have an MIT license: free commercial usage.

Try out the demo here: https://huggingface.co/spaces/numind/NuZero
Or check out all of the models here: https://huggingface.co/collections/numind/nunerzero-zero-shot-ner-662b59803b9b438ff56e49e2

If there's ever a need for me to extract some information from any text: I'll be using these. Great work @Serega6678!

osanseviero mdouglas clem MoritzLaurer julien-c andrewrreed nickandbro


I just created a simple LoRA model which is trained just on 106 images and got super model, which became a little bit popular, 6k downloads at total, which is a lot for me, just small programmer, with low laptop!
https://huggingface.co/Kvikontent/midjourney-v6


Build your own GPT-4 Tokenizer! - @karpathy's minbpe exercise.
Step 1: BasicTokenizer
Got "close" to beating minbpe's train speed :(
step 2 RegexTokenizer coming soon.

Notes on lessons learned:
- tokenization is the assembly language of LLMs:)
It's not a healthy choice to code it lol.
- encoding can literally drive you mad.
- merging is where sh*t gets real - moment of truth:)
- training requires precision.
- decoding is trivial.


Can AI cheat or lie?

In this blog, we will explore the research conducted by experts from MIT, Australian Catholic University, and the Center for AI Safety to better understand the nature of AI deception, its various forms, and the potential risks it poses. We will examine real-world examples and the underlying mechanisms that enable AI systems to deceive.

Learn more at: https://ajithp.com/2024/05/12/ai-deception-risks-real-world-examples-and-proactive-solutions/


Added a new button to the AI Stories Factory for more fun!

https://huggingface.co/spaces/jbilcke-hf/ai-stories-factory

HeshamHaroon osanseviero Ramikan-BR TNVMike radames clem ucalyptus victor kramp KingNish ringo0918 rezalian


Hey everyone! Just wanted to share this awesome dataset that features over 1 million tokens specifically for the Egyptian dialect. Check it
https://huggingface.co/datasets/HeshamHaroon/1milion_token_EGY_songs

victor osanseviero MrArray22 bodaaa clem rahim707


𝗚𝗿𝗮𝗻𝗶𝘁𝗲 𝗼𝗻 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲 -

𝗚𝗿𝗮𝗻𝗶𝘁𝗲 𝗼𝗻 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲 -

IBM has launched its latest #Granite code models.
𝗚𝗿𝗮𝗻𝗶𝘁𝗲 𝗼𝗻 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲 - https://lnkd.in/g2KaHWxC
The figure illustrates how#Granite-8B-Code-Base outperforms #Mistral-7B, #LLama-3-8B, and other open-source models in coding tasks.

Models available:
- ibm-granite/granite-3b-code-base
- ibm-granite/granite-3b-code-instruct
- ibm-granite/granite-8b-code-base
- ibm-granite/granite-8b-code-instruct
- ibm-granite/granite-20b-code-base
- ibm-granite/granite-20b-code-instruct
- ibm-granite/granite-34b-code-base
- ibm-granite/granite-34b-code-instruct


Hi @TimothyAlexisVass, thanks for [this great blog post](https://huggingface.co/blog/TimothyAlexisVass/explaining-the-sdxl-latent-space#the-4-channels-of-the-sdxl-latents)! Shouldn't this be like that?
https://github.com/TimothyAlexisVass/hf-colorspace-blogpost/pull/1


Hi folks ,

colab[https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing]

#OpenSourcing
pip-code-bandit
-- a model to act as intelligence unit in agentic workflows.

pipflow
-- a library to manage and run goal oriented agentic system.

pip-code-bandit attributes-
-- number of params ~ 1.3b [2.9 Gb GPU memory footprint]
-- sequence length ~ 16.3k [Can go higher but will show performance degradation]
-- license - apache 2.0
-- instruction following , RL tuned.
-- tasks:
complex planning(plan) of sequential function calls | a list of callables and goal
corrected plan | feedback instructions with error
function calling | doc or code and goal
code generation | plan and goal
code generation | goal
doc generation | code
code generation | doc
file parsed to json | any raw data
sql generation | schema, question, instructions and examples

#Strategy

We used a simulator to simulate environments where the model could play games to achieve goals, given a set of actions available to it. All the model could do was find the right action and config to incur a positive reward. The reward policy is around the concept of a model going to a stable state of zero net sum reward for both good and bad behaviour. In this setup, the model, which was pre-trained on code, function documentation, and similar OS datasets, was RL-tuned for reliability and instruction-following.

Do try it out and let me know how its working for you.

Thank you :)


What are some areas that Image generation models are currently lacking in?


Yi－1.5シリーズがリリースされました。

Yi－1.5シリーズがリリースされました。

最強のオープンソースモデルです。

最強のオープンソースモデルです。

Yi－1.5シリーズがリリースされました。
このモデルは、GPT-3.5,Llama3,Claude2の性能を越していて、プログラミングの性能はGemini超えです。
最強のオープンソースモデルです。
ぜひ使ってみましょう。
https://huggingface.co/01-ai/Yi-1.5-34B-Chat


Johntog takeraparterer amirgame197 osanseviero victor Zmu


🎉 Big reveal: 01.AI Yi-1.5 models are in town!

📜 1st Apache 2.0 release
💡 Capabilities: Enhanced coding, math, reasoning, & instruction-following
🤖 Models: 34B/9B/6B, Base & Chat
🏆 Performance: Yi-1.5-34B matches or exceeds Llama 3 70B in benchmarks
🔥 Discover the power now! https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8


Juggernaut X V10 is pretty good, its a few weeks old but not very popular. Try it out and let me know what you guys think. I think it is pretty good for a daily use.

⭐ Original Model: https://huggingface.co/RunDiffusion/Juggernaut-X-v10
🌟 Test it out: https://huggingface.co/spaces/Walmart-the-bag/Juggernaut-X-v10
💫 Author: https://huggingface.co/RunDiffusion

Felowpy Taylor658 dillfrescott osanseviero victor


Cool new dataset from @isidentical - https://huggingface.co/datasets/isidentical/moondream2-coyo-5M-captions

The VeCLIP paper showed a +3% gain while only using 14% of the data by synthetically captioning like this. You get diversity from the alt text (middle column) without having to deal with all of the noise.

isidentical radames osanseviero victor plmsmile Glavin001


Hi HF Community!

Did you know that you are a `docker compose up` away from unleashing some really powerful AI?

Let me introduce you to 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴-𝗮𝗶🤖

𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴-𝗮𝗶 is a multi-task, AI-powered and local assistant that allows you to:

- 💭 talk to 𝘺𝘰𝘶𝘳 𝘱𝘥𝘧𝘴 thanks to a backend retrieval architecture built with LangChain and Qdrant (supports 50+ languages)
- ✍ chat with almost every text-generation model on Hugging Face hub (supports 50+ languages)
- 📄 summarize texts and pdfs with almost every text-summarization model on HF hub (English-only)
- 🎨 generate images with Pollinations AI API or stable diffusion models on HF hub (supports 50+ languages)
- 🖨 describe images with image-to-text models on HF hub (English-only)
- 🩻 classify images with image-classification models on HF hub (English-only)
- 🖼 implement an image-based search engine on *your images*, thanks to Qdrant, with almost every feature extraction model on HF hub

🖥 There is now a brand new user interface that helps with selecting tasks, models, pdf files, images and languages

🐋 everything-ai is distributed as a multi-container application in Docker, available for Windows and Mac, and it is 100% local.

🏃♀ Head over to the documentation for a quick-start on the application: https://astrabert.github.io/everything-ai

🥰 Make sure to check out the GitHub repo and leave a little ⭐: https://github.com/AstraBert/everything-ai

🚀 Features coming soon:
- 🎙Audio and video-related tasks
- 🤗 Integration with HF Spaces API to effortlessly chat with larger models on the Hub from your local computer
- 🧬 Biology-related tasks

❤ Shout-outs to Qdrant, LangChain, Hugging Face, Docker and Gradio for making all of this possible!

💡Inspired by Jan, Cheshire Cat AI, LM Studio, Ollama and other awesome local AI solutions!


Extending Llama-3's Context Ten-Fold Overnight (2404.19553)

You are all happy 😊 that @meta-llama released Llama 3.

Then you are sad 😔 that it only has a context length of 8k.

Then you are happy 😄 that you can just scale llama-3 PoSE to 96k without training, only needing to modify max_position_embeddings and rope_theta.

But then you are sad 😢 it only improves the model's long-context retrieval performance (i.e., finding needles) while hardly improving its long-context utilization capability (doing QA and summarization).

But then you are happy 😁 that the
@GradientsTechnologies community has released the long-context Llama-3-8B-Instruct-262K with long context (262k-1M+).

Now we have another paper "Extending Llama-3's Context Ten-Fold Overnight" 📜.

The context length of Llama-3-8B-Instruct is extended from 8K to 80K using QLoRA fine-tuning⚙.

The training cycle is highly efficient, taking "only" 😂 8 hours on a single 8xA800 (80G) GPU machine.

The model also preserves its original capability over short contexts. ✁

The dramatic context extension is mainly attributed to merely 3.5K synthetic training samples generated by GPT-4.📊

The paper suggests that the context length could be extended far beyond 80K with more computation resources (😅 GPU-poor).

The team plans to publicly release all resources, including data, model, data generation pipeline, and training code, to facilitate future research from the ❤ community.

Paper: https://arxiv.org/abs/2404.19553

This is where we are... until next time... 🌟

https://huggingface.co/papers/2404.19553

tasmani osanseviero Kukedlc Beeezy PrasannaAtlas not-lain prashant182 jrell JoPmt

asif10018 osanseviero Kukedlc Beeezy not-lain prashant182 Ramikan-BR prsyahmi


Updated: Lumimaid and TheSpice-v0.8.3

I have uploaded version 2 (v2) files for the  Llama-3-Lumimaid-8B-v0.1-OAS GGUF Imatrix quants.

[model] https://huggingface.co/Lewdiculous/Llama-3-Lumimaid-8B-v0.1-OAS-GGUF-IQ-Imatrix

You can recognize the new files by their `v2` prefix.

Imatrix data was generated from the FP16 and conversions directly from the BF16.
Hopefully avoiding any losses in the model conversion, as has been the recently discussed topic on Llama-3 and GGUF lately.

This is more disk and compute intensive so lets hope we get GPU inference support for BF16 models in llama.cpp.

If you are able to test them and noticed any issues compared to the original quants, let me know in the corresponding discussions.

---

Additionally, L3-TheSpice-8b-v0.8.3 GGUF Imatrix quants were also updated.

[model] https://huggingface.co/Lewdiculous/L3-TheSpice-8b-v0.8.3-GGUF-IQ-Imatrix

TravelingMan tasmani Herman555 FantasiaFoundry Ardvark123 Ramikan-BR Forgiwen

FantasiaFoundry TravelingMan tasmani Ardvark123 Ramikan-BR


Is anyone looking into some sort of decentralized/federated dataset generation or classification by humans instead of synthetically?

From my experience with trying models, a *lot* of modern finetunes are trained on what amounts to, in essence, GPT-4 generated slop that makes everything sound like a rip-off GPT-4 (refer to i.e. the Dolphin finetunes). I have a feeling that this is a lot of the reason people haven't been quite as successful as Meta's instruct tunes of Llama 3.

osanseviero isidentical Beeezy f0ster HiroseKoichi Maykeye m-conrad-202 Makya


Craft Your Own Expert LLM - Using 100% Open-Source/Private/Free/Awesome Tools

Hey everyone! After seeing a lot of people's interest in crafting their own datasets and then training their own models, I took it upon myself to try and build a stack to help ease that process. I'm excited to share a major project I've been developing—the Vodalus Expert LLM Forge.

https://github.com/severian42/Vodalus-Expert-LLM-Forge

This is a 100% locally LLM-powered tool designed to facilitate high-quality dataset generation. It utilizes free open-source tools so you can keep everything private and within your control.

Why Open Source?

I decided to open source the Vodalus Expert LLM Forge to empower individuals and organizations everywhere to generate their own high-quality data. By making these tools freely available, I hope this community can start crafting their own models with little to no money and/or experience, helping to improve data quality and innovation across the board. While I'm releasing this tool for free, I've also completed an extensive tutorial/course with lots of videos and instructions that guide you through each step of maximizing the potential of this stack. This course is available for purchase at ko-fi.com/s/076479f834 and is designed to enhance your experience and results with the Vodalus Expert LLM Forge.

What’s included in the Vodalus Expert LLM Forge?

- Data Generation: Harness RAG (through AnythingLLM if you are set up properly) and Wikipedia to create datasets via local language models.

- Model Training & Fine-Tuning: Tutorials and Jupyter notebooks to customize models to your specific needs.

- Quantization: Optimize models for performance with our quantization guides.

If this project aids your work, please consider supporting it through a donation at my ko-fi.com/severian42. Your support helps sustain my further LLM developments and experiments, always with a focus on using those efforts to give back to the LLM community.


Currently pushing Hiera checkpoints to the hub  to have it merged into the transformers library in the next few days 🤗


Hey everyone,

Just wanted to shout out a massive thank you to all 2000 of you who've followed me on Hugging Face! 🎉 It's incredible to have such an awesome crew backing me up as I dive into all these LLM experiments.

Even though not all my models turn out perfect, I've found some real gems and methods along the way 💎. It's like digging for treasure – sometimes you found nothing, but sometimes you find a pearl, and sometimes you find a new method to try.

Your support and encouragement mean the world to me, and I'm really stoked to keep experimenting and learning. If you told me some years ago I would have so much people following me for what I do, I wouldn't have believed it. Here's to more discoveries and adventures ahead! 🚀

Also, big thanks once again, and a huge shoutout to @IkariDev for being there through this journey and supporting me. I'm excited for our future work together and hope we will continue to make people happy! 👏

I want to thank @Gryphe too, since my early work was heavily inspired from MythoMax and the RP/ERP vibe of it. If I'm here today it's probably because of you 😂

I was so close to forget @chargoddard and his amazing tool too! What will we do without mergekit in our life? Thank you! 🙏

See y'all at 3k!

thedarktrumpet Gryphe victor EloyOn nisten ajibawa-2023 PowerBot taufiqdp samiraeli ZootZootTesla Anarchist osanseviero EdsonT samusenps PopHorn1956 AuraRuby Novelo DataSoul kramp SkySyrup lysandre DutchMjr clem ineap09 Winnougan sn2234 SerialKicked FuturisticVibes Josephgflowers nikodemos71 coffeedean HalleyStarbun Eilian Salekeen woolfog dashfunnydashdash SnakyMcSnekFace wunderschnitzel MateoTeo kamiao jce1971

Skydea victor Severian ZootZootTesla Anarchist osanseviero Novelo SkySyrup lysandre OrangeApples morgul clem FallenMerick gghfez Meggido FuturisticVibes spuun HalleyStarbun Ripter001 xpgx1 Fortuze MateoTeo kamiao

FOX18067890 lysandre clem Novelo FuturisticVibes AtonMountlook HalleyStarbun xpgx1 SicariusSicariiStuff


AniTalker: Animate Vivid and Diverse Talking Faces through
  Identity-Decoupled Facial Motion Encoding (2405.03121)

🚀🎭🌟 New Research Alert - AniTalker (Avatars Collection)! 🌟🎭🚀
📄 Title: AniTalker: Animate Vivid and Diverse Talking Faces through Identity-Decoupled Facial Motion Encoding 🔝

📝 Description: AniTalker is a new framework that transforms a single static portrait and a single input audio file into animated, talking videos with natural, fluid movements.

👥 Authors: Tao Liu, Feilong Chen, Shuai Fan, @cpdu, Qi Chen, Xie Chen, and Kai Yu

📄 Paper: https://huggingface.co/papers/2405.03121

🌐 Github Page: https://x-lance.github.io/AniTalker
📁 Repository: https://github.com/X-LANCE/AniTalker

📚 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: #AniTalker #FacialAnimation #DynamicAvatars #FaceSynthesis #TalkingFaces #DiffusionModel #ComputerGraphics #DeepLearning #ComputerVision #Innovation

DmitryRyumin ivan-ft samiraeli osanseviero KvrParaskevi samusenps AlekseiPravdin Winnougan

SunixLiu samusenps kevinpics AlekseiPravdin


JARVIS has been updated to include voice input functionality, allowing for an interactive experience similar to that of Siri and Alexa.
Check it out: https://huggingface.co/spaces/KingNish/JARVIS


Red Hat and IBM have announced InstructLab, an open-source project for LLM contributions.  InstructLab offers a model-agnostic approach for the community to contribute "skills" and or "knowledge" to LLMs via a CLI and tuning backend.

This community-driven approach to GenAI model development is novel to say the least. It will be interesting to see how effective it is in the long run, especially on models beyond the initial Granite and Merlinite familes.

Can check out Git Hub here: https://github.com/instructlab
Read the LAB Paper: https://arxiv.org/abs/2403.01081
View Model Builds: https://huggingface.co/instructlab


最高のモデルがリリースされました。

最高のモデルがリリースされました。

日本語を喋る人なら絶対に使わないと損！！！

日本語を喋る人なら絶対に使わないと損！！！

最強の日本語LLMを紹介します！

最強の日本語LLMを紹介します！

LM スタジオやJanを使い、ぜひ使用してみましょう！

LM スタジオやJanを使い、ぜひ使用してみましょう！

最高のモデルがリリースされました。
日本語を喋る人なら絶対に使わないと損！！！
最強の日本語LLMを紹介します！

①https://huggingface.co/karakuri-ai/karakuri-lm-8x7b-chat-v0.1
②https://huggingface.co/Fugaku-LLM/Fugaku-LLM-13B-instruct-gguf
③https://huggingface.co/stabilityai/japanese-stablelm-2-instruct-1_6b
です。
LM スタジオやJanを使い、ぜひ使用してみましょう！

Taylor658 MouhuAI victor ynakashima Chunte ys4s4k1


https://huggingface.co/datasets/Replete-AI/code_bagel

Make the ultimate coding finetune to compete with the likes of closed source models using the code_bagel dataset!

Made by @rombodawg of RepleteAi, the code_bagel dataset contains over 800 million tokens of deduplicated and uncensored code from only reputable sources on huggingface. This code is formatted in the alpaca instruct format for ease of use in training.


First post, thanks HF! 🤗

Here is a Claude 3 Sonnet generated dataset using prompts from WildChat:

https://huggingface.co/datasets/raincandy-u/claudy-chat-5k


WbjuSrceu raincandy-u onegen not-lain eienmojiki


Vodalus Expert LLM Forge - Dataset Crafting and Efficient Fine-Tuning Using Only Free Open-Source Tools

Hey everyone! After my last post getting a sense of people's interest in crafting their own datasets, I'm excited to share a major project I've been developing—the Vodalus Expert LLM Forge.

https://github.com/severian42/Vodalus-Expert-LLM-Forge

This is a 100% locally LLM-powered tool designed to facilitate high-quality dataset generation. It utilizes free open-source tools so you can keep everything private and within your control. After considerable thought and debate (this project is the culmination of my few years of learning/experimenting), I've decided to open-source the entire stack. My hope is to elevate the standard of datasets and democratize access to advanced data-handling tools. There shouldn't be so much mystery to this part of the process.

Why Open Source?
My hope is to empower individuals everywhere to generate their own high-quality data. By making these tools freely available, I hope this community can start crafting their own models with little to no money and/or experience, helping to improve data quality and innovation across the board. While I'm releasing this tool for free, I'm also near completion on an extensive tutorial/course that guides you through each step of maximizing the potential of this stack. This course will be available for purchase soon and is designed to enhance your experience and results with the Vodalus Forge; more details soon

If this project aids your work, please consider supporting it through a donation on my https://ko-fi.com/N4N4XZ2TZ. Your support helps sustain my further LLM developments and experiments, always with a focus on using those efforts to give back to this community

osanseviero jgitsolutions Virt-io onegen kevineen sbrandeis


LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report (2405.00732)

Low Rank Adaptation (LoRA) is a popular method for fine-tuning but why...? 🤔

LoRA is the most widely adopted method for Parameter Efficient Fine-Tuning (PEFT) of Large Language Models (LLMs) 🤖. It reduces the number of trainable parameters and memory usage while achieving comparable performance to full fine-tuning 💾.

Good folks at @predibase  have released LoRA Land! 🌍

A research paper where they measure the quality of LLMs fine-tuned with quantized low-rank adapters across 10 base models and 31 tasks for a total of a whopping 310 models 📊.

According to the paper, 4-bit LoRA fine-tuned models outperform base models by 34 points 🔝 and GPT-4 by 10 points on average 👏.

Here comes the best part... 🥁

They have also evaluated the latency and concurrency capabilities of LoRAX, an open-source Multi-LoRA inference server that facilitates the deployment of multiple LoRA fine-tuned models on a single GPU using the shared base model weights and dynamic adapter loading 🚀.

And did I mention LoRAX is completely open-source! 🎉

Paper: https://arxiv.org/pdf/2405.00732

While the claims are lofty and yet to be vetted, this is amazing for serving fine-tuned models without breaking the bank! 💸

PS: 🤯They got LoRAX to host 25 LoRA fine-tuned Mistral-7B LLMs on a single NVIDIA A100 GPU with 80GB memory 💻.

https://huggingface.co/papers/2405.00732


👋 Hello to all the amazing developers out there! Whether you're just starting out or a seasoned pro, I want to take a moment to appreciate your dedication and passion for coding. Keep on creating, innovating, and inspiring! #HelloToDev 🚀💻

Thank you !


Hello Community,
This is space that provides a simple interface for creating AnimateDiff pipeline videos.
https://huggingface.co/spaces/ModularityAI/GenVideo
@Anas989898


🙋🏻♂hey there folks !

i got an email my space was down so now my space is back up ! StarCoder2 (Raw) on A100 , for your enjoyment and apache2 research purposes 👏🏻👏🏻

https://huggingface.co/spaces/Tonic/starcoder2

check my profile for more cool GPUzero demos, i'll cycle them with some new overlooked models soon 💖🤗


ORPO - tuned Prem-1B base

https://huggingface.co/ucalyptus/prem-1B-ORPO


Was this the first Transformers citation? In deep reinforcement learning?!

From "Deep Reinforcement Learning: An Overview": https://arxiv.org/abs/1701.07274v3


新しい日本語に特化したLLMが出てきました！

新しい日本語に特化したLLMが出てきました！

日本語性能は7Bの中では一番高性能です。

日本語性能は7Bの中では一番高性能です。

使ってみましたが、すごく良い性能です。

使ってみましたが、すごく良い性能です。

新しい日本語に特化したLLMが出てきました！

日本語性能は7Bの中では一番高性能です。
使ってみましたが、すごく良い性能です。

https://huggingface.co/DataPilot/ArrowPro-7B-KUJIRA


If you haven't done it yet,

try my new AI Stories Factory space it is pretty fun!
https://huggingface.co/spaces/jbilcke-hf/ai-stories-factory

You can even prompt the visual and music style  💅


Hello! Thanks to the Hugging Face team for accepting me for posts.
I'm someone who is interested in learning more about AI and ML, and I just want to say the community has been super supportive with all the guides and the people I've seen.
I'll probably be more active in making things during the summer holidays when I have more time but ill keep an eye on other peoples projects and may try make some things in the meantime xd

merterbak TheStinger radames osanseviero Nick088 alias8797 Aakashk poiqazwsx


So thankful to be part of this group!  Thank you!

I try to keep my Spaces operating and Collections fresh!  Appreciate any Follows!

https://huggingface.co/sourceoftruthdata


Hi folks,
Tenyx announced its latest model Llama3-TenyxChat-70B, which outperforms a GPT-4 variant on several MT-Bench measurements.

By post-training Llama-3 70B in 15 hours, our model improves reasoning capabilities leveraging the relationship between geometry and LLM task complexity (Take a look at our paper: https://arxiv.org/abs/2312.01648, to be presented at ICML 2024)
Model: https://huggingface.co/tenyx/Llama3-TenyxChat-70B, HuggingFace Space: https://huggingface.co/spaces/tenyx/Llama3-TenyxChat-70B

Romain-Cosentino Mastershin jgitsolutions dillfrescott toptal126 jacktenyx JoaoSchneider

bmorphism dillfrescott toptal126 sarath-shekkizhar jacktenyx


Finally, I can post! 🚀

I created a Capybara-inspired Italian dataset by translating the initial instruction and running it through a pipeline to generate conversations. I used Claude Sonnet for translation and instruction generation, and Opus for generating the answers.

I hope this dataset proves useful for people working on 🇮🇹 language models.

⛁ Open sourcing the dataset here: https://huggingface.co/datasets/efederici/capybara-claude-15k-ita


FER-YOLO-Mamba: Facial Expression Detection and Classification Based on
  Selective State Space (2405.01828)

😀😲😐😡 New Research Alert - FER-YOLO-Mamba (Facial Expressions Recognition Collection)! 😡😥🥴😱
📄 Title: FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space 🔝

📝 Description: FER-YOLO-Mamba is a novel facial expression recognition model that combines the strengths of YOLO and Mamba technologies to efficiently recognize and localize facial expressions.

👥 Authors: Hui Ma, Sen Lei, Turgay Celik, and Heng-Chao Li

🔗 Paper: https://huggingface.co/papers/2405.01828

📁 Repository: https://github.com/SwjtuMa/FER-YOLO-Mamba

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

🔥🔝 See also Facial_Expression_Recognition - https://huggingface.co/spaces/ElenaRyumina/Facial_Expression_Recognition (App, co-authored by @DmitryRyumin) 😉

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

🔍 Keywords: #FERYOLOMamba #FER #YOLO #Mamba #FacialExpressionRecognition #EmotionRecognition #ComputerVision  #DeepLearning #MachineLearning #Innovation

DmitryRyumin samusenps lysandre philipp-zettl


We are excited to share the latest advancement in the AISAK system: the introduction of AISAK-Detect. As an essential component of AISAK-Visual, this sophisticated model specializes in object detection tasks, significantly enhancing our system's capabilities in comprehensive visual analysis.

AISAK-Detect is built on an encoder-decoder transformer architecture with a convolutional backbone, ensuring accurate and efficient object detection within images. Our dedicated team has meticulously trained and fine-tuned this model to guarantee seamless integration into the broader AISAK ecosystem, contributing to cohesive performance in image analysis tasks.

The deployment of AISAK-Detect is a significant milestone in our journey towards offering a comprehensive AI solution across multiple domains. With a unique deployment approach that prioritizes achieving an optimal system, we are committed to delivering an AI experience that goes beyond the limitations of traditional chat instances.

Regular updates on the progress of the AISAK system, including the deployment of AISAK-Detect, will be provided to keep users informed about the advancements being made. We look forward to sharing more exciting developments as we continue to grow and innovate.

https://huggingface.co/aisak-ai/aisak-detect

https://huggingface.co/collections/aisak-ai/aisak-65ddeeb08d0978de6114702f


🤗The first submissions from the Community Hugging Face Computer Vision Course (https://huggingface.co/learn/computer-vision-course/unit0/welcome/welcome) are being posted up on HF Spaces!🤗

https://huggingface.co/spaces/OmAlve/Swin-Transformer-Foods101
https://huggingface.co/spaces/Rageshhf/medi-classifier

 It is amazing that the first group of students has completed the course and in record time!

Will look forward to seeing more submissions from the course soon.

A nice swag item that students get when they complete the course and make their submission is this cool Hugging Face Certificate of Completion. (Its suitable for framing) 🤗
👇


Are you tired of writing scripts to scrape data from the web? 😓

ScrapeGraphAI is here for you! 🎉

ScrapeGraphAI is an OPEN-SOURCE web scraping Python library that uses LLM and direct graph logic to create scraping pipelines for websites and local documents (XML, HTML, JSON, etc.). 🌐📊

Just say which information you want to extract (in human language) and the library will do it for you! 🗣🚀

It supports GPT, Gemini, and open-source models like Mistral. 🔍

A few things that I could not find in the docs but would be amazing to see 🤞:
- Captcha handling 🔐
- Persistent data output formatting 📁
- Streaming output 📡
- Explanation😂 of the tag line: "ScrapeGraphAI: You Only Scrape Once" What does that even mean? 🤣 Is this YOLO? 🤔

Link: https://github.com/VinciGit00/Scrapegraph-ai
Demo code: https://github.com/amrrs/scrapegraph-code/blob/main/sourcegraph.ipynb

svjack alobnayis julesbsz victor Michielo XiangJinYu Taylor658 orkut boapps johnlockejrr Norod78 Subash07 shtefcs


This is a song from imagine dragons believer.
This song is to say thanks to hf team for accepting me! I was waiting for a long time.


🥳celebrating 5K readers in one of my blog posts 🥳
I came back with another one this time 🤓
in this blog you will  learn 📖 :
* How to train custom AI models with the trainer API 🚀
* integrate your AI models with HF using the mixin classes 🔥

happy reading everyone 🤗
🔗link: https://huggingface.co/blog/not-lain/trainer-api-and-mixin-classes

BoredApeYachtClub victor KvrParaskevi Ramikan-BR DESUCLUB


Thank you very much hf team for accepting me! I was waiting for very long time. Thank you


Found out my ECCV paper is getting rejected because of a LaTeX compile error :(


NEW HF 🤗 COURSE to help people dive into Computer Vision built by the HF community. Over the last 6 months the hugging face discord community has been hard at work developing a new computer vision course. Receive a Certificate of completion and share it on your socials 🤗.

https://huggingface.co/learn/computer-vision-course/unit0/welcome/welcome

mattmdjaga osanseviero not-lain Taylor658 mmhamdy


Finally!
My first post for the lovely community out there!

Here's a highly quantized finetuned version of gemma focused exclusively on Prompt Engineering. Write as ambiguous you want and leave the job to this model

https://huggingface.co/zamal/gemma-7b-finetuned

osanseviero zamal victor Ramikan-BR raincandy-u ashirvp

jgitsolutions zamal victor Ramikan-BR raincandy-u ashirvp


Okay, I can post now, Yayy!!
Sharing the OpenBioLLM-70B as the first post :)

Introducing OpenBioLLM-Llama3-70B & 8B: The most capable openly available Medical-domain LLMs to date! 🩺💊🧬

Outperforms industry giants like GPT-4, Gemini, Meditron-70B, Med-PaLM-1, and Med-PaLM-2 in the biomedical domain. 🏥📈 🌟

OpenBioLLM-70B delivers SOTA performance, setting a new state-of-the-art for models of its size. OpenBioLLM-8B model and even surpasses GPT-3.5, Gemini, and Meditron-70B! 🚀

Today's release is just the beginning! In the coming months, we'll be introducing:

- Expanded medical domain coverage 🧠
- Longer context windows 📜🔍
- Better benchmarks 📈🏆
- Multimodal capabilities🖥🩺📊🔬

Medical-LLM Leaderboard: https://huggingface.co/spaces/openlifescienceai/open_medical_llm_leaderboard

More detail : https://huggingface.co/blog/aaditya/openbiollm


Empowering Biomedical Discovery with AI Agents
https://arxiv.org/abs/2404.02831

huanghuan osanseviero minhdang polejowska whitebill FunkusBangru


I've put Llama 3 on a Raspberry Pi! And it can actually help me!

Would love to hear what you think :-) What should I do next so that it can flip a light switch for me?

Demo: https://youtu.be/OryGVbh5JZE
GitHub: https://github.com/nkasmanoff/pi-card

ChiJuiChen Taylor658 radames inkfeather kristaller486 victor dillfrescott lusstta Ramikan-BR jeffboudier


Llama 70b Instruct + function calling.
Fireplace-70b out now:

https://huggingface.co/ValiantLabs/Llama3-70B-Fireplace


You Only Cache Once: Decoder-Decoder Architectures for Language Models (2405.05254)

🔥🚀🌟 New Research Alert - YOCO! 🌟🚀🔥
📄 Title: You Only Cache Once: Decoder-Decoder Architectures for Language Models 🔝

📝 Description: YOCO is a novel decoder-decoder architecture for LLMs that reduces memory requirements, speeds up prefilling, and maintains global attention. It consists of a self-decoder for encoding KV caches and a cross-decoder for reusing these caches via cross-attention.

👥 Authors: Yutao Sun et al.

📄 Paper: https://huggingface.co/papers/2405.05254

📁 Repository: https://github.com/microsoft/unilm/tree/master/YOCO

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

🔍 Keywords: #YOCO #DecoderDecoder #LargeLanguageModels #EfficientArchitecture #GPUMemoryReduction #PrefillingSpeedup #GlobalAttention #DeepLearning #Innovation #AI

lunarflu osanseviero DmitryRyumin markcda unilm


Introducing Phi-3 WebGPU, a private and powerful AI chatbot that runs 100% locally in your browser, powered by 🤗 Transformers.js and onnxruntime-web!

🔒 On-device inference: no data sent to a server
⚡ WebGPU-accelerated (> 20 t/s)
📥 Model downloaded once and cached

Try it out: https://huggingface.co/spaces/Xenova/experimental-phi3-webgpu

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lunarflu clem not-lain nikrad oceansweep JPMSA1212


New sampling strategy dropped in 🤗 transformers -- Min P sampling 🔥

Are you tired of having `top_k` arbitrarily discarding high-quality continuations? Or `top_p` forgetting to exclude low-probability tokens, derailing your generation? Try out the new `min_p` flag in `generate`, fresh from a PR merged today! 🥬

Min P consists of a dynamic token filter -- as opposed to Top K, which keeps the K most likely tokens, and Top P, which keeps the most likely tokens up to a fixed cumulative probability, both static filters. Min P takes a base probability (defined in the `min_p` flag) and multiplies it by the probability of the most likely token in the distribution for the next token. All tokens less likely than the resulting value are filtered. What happens with this strategy?
👉 High probability token present -> aggressive filter (we don't want to miss on that high-probability case and risk derailing generation)
👉 No high probability token present -> relaxed filter (there are many continuation possibilities that the model finds plausible)

You should set `min_p` to a low value, between 0.05 and 0.1. It behaves particularly well for creative text generation when paired up with temperature > 1.

Kudos to @kalomaze and @menhguin for creating this technique 🔥 Read their discussion in the original issue for benchmarks (https://github.com/huggingface/transformers/issues/27670)

Copy-pasteable version of the example in the image below here: https://pastebin.com/VqXNtuxd

Have fun experimenting! 😎

jankke lunarflu osanseviero menhguin kristaller486 MoritzLaurer florentgbelidji banelingz abdullah ariG23498 John6666 Manel


@mik3ml just released https://huggingface.co/datasets/ReDiX/wikipediaQA-ita  an interesting synthetic dataset originated from wikipedia using a fine tuned version of mistral-7B specific for the Italian language 🇮🇹 .


Hi am planing to doing WSD (Word Sense Disambiguation)
an using arabic language
I decided to  used Sense-annotated corpora
is there in recommanded Model to use to
and is there any pre-done examples to use them as a guide

Thank you for your help


HiDiffusion SDXL now supports Image-to-Image, so I've created an "Enhance This" version using the latest ControlNet Line Art model called MistoLine.  It's faster than DemoFusion

Demo: https://huggingface.co/spaces/radames/Enhance-This-HiDiffusion-SDXL

Older version based on DemoFusion https://huggingface.co/spaces/radames/Enhance-This-DemoFusion-SDXL

New Controlnet SDXL Controls Every Line https://huggingface.co/TheMistoAI/MistoLine

HiDiffusion is compatible with diffusers and support many SD models - https://github.com/megvii-research/HiDiffusion

osanseviero not-lain victor jaisanrobert


@Yasusan 理化学研究所AIPの関根聡と申します。AnswerCarefullyのデータについて確認いただきありがとうございます。HuggingFavceにて再配布公開いただいたこと確認しました。日本のLLMの発展に貢献したいという目標でこのデータを作成し、同じ目標で再公開いただいたと思うのですが、このデータは危険性を含んでおり、かつ、データを利用してハックされることを危惧しており、どなたが利用したかわかるよう、公開ホームページにある通り「再配布は禁止」しております。その点をご理解いただき、再配布を中止いただけますようお願いします。


What tools do you need to deconstruct bias in algorithms? (You know, this thing that is becoming increasingly prevalent in our lives)

Participate in the new discussion in the Journalists on Hugging Face community: https://huggingface.co/spaces/JournalistsonHF/README/discussions/4


🚀 Introducing RefuelLLM-2 and RefuelLLM-2-small, the next version of our large language models purpose built for data labeling, enrichment and cleaning.

RefuelLLM-2 (83.82%) outperforms all state-of-the-art LLMs, including GPT-4-Turbo (80.88%), Claude-3-Opus (79.19%) and Gemini-1.5-Pro (74.59%), across a benchmark of ~30 data labeling tasks.

RefuelLLM-2-small (79.67%), aka Llama-3-Refueled, outperforms all comparable LLMs including Claude3-Sonnet (70.99%), Haiku (69.23%) and GPT-3.5-Turbo (68.13%).

📖 Open sourcing the model weights: https://huggingface.co/refuelai/Llama-3-Refueled
📝 Detailed blog post: https://www.refuel.ai/blog-posts/announcing-refuel-llm-2
🧪 Try out the model here: https://labs.refuel.ai/playground

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abhinavn rajasbansal rish93 sahilyadav nihitdesai dhruvabansal osanseviero Lin-Rexter hiieu thomwolf lunarflu radames dillfrescott

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rajasbansal abhinavn rish93 dhruvabansal thomwolf lunarflu radames dillfrescott


xLSTM: Extended Long Short-Term Memory (2405.04517)

🔥🚀🌟 New Research Alert - xLSTM! 🌟🚀🔥
📄 Title: xLSTM: Extended Long Short-Term Memory 🔝

📝 Description: xLSTM is a scaled-up LSTM architecture with exponential gating and modified memory structures to mitigate known limitations. xLSTM blocks outperform SOTA transformers and state-space models in performance and scaling.

👥 Authors: Maximilian Beck et al.

📄 Paper: https://huggingface.co/papers/2405.04517

📁 Repository: https://github.com/NX-AI/xlstm

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

🔍 Keywords: #xLSTM #DeepLearning #Innovation #AI

DmitryRyumin taufiqdp mihaylovnikitos victor KvrParaskevi radames cfrancois7 AlekseiPravdin siyengfeng lunarflu osanseviero Taylor658 Lin-Rexter FreeHugsForRobots ucyang sayhan dillfrescott JoPmt


Get daily/weekly/monthly notifications about latest trending feature-extraction models compatible with transformers.js for semantic search! All open source built on GitHub Actions and ntfy.sh.

I'm also providing daily updated tables (filterable and sortable by onnx model size too!) if you want to have a look only once in a while. Download what suits you best: csv, xlsx, parquet, json, html.

Would you like to monitor other models/tags? Feel free to open a PR :)

GitHub: https://github.com/do-me/trending-huggingface-models
Ntfy.sh daily channel: https://ntfy.sh/feature_extraction_transformers_js_models_daily
Sortable table: https://do-me.github.io/trending-huggingface-models/

And the best part: all 145 models are integrated in SemanticFinder to play around with https://do-me.github.io/SemanticFinder/!


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Last Week in Medical AI: Top Research Papers/Models 🔥
(November 2 -November 9, 2024)

🏅 Medical AI Paper of the Week:
Exploring Large Language Models for Specialist-level Oncology Care

Medical LLM & Other Models:
- GSCo: Generalist-Specialist AI Collaboration
- PediatricsGPT: Chinese Pediatric Assistant
- MEG: Knowledge-Enhanced Medical QA
- AutoProteinEngine: Multimodal Protein LLM

Frameworks and Methodologies:
- BrainSegFounder: 3D Neuroimage Analysis
- PASSION: Sub-Saharan Dermatology Dataset
- SAM for Lung X-ray Segmentation
- Label Critic: Data-First Approach
- Medprompt Runtime Strategies

Medical LLM Applications:
- CataractBot: Patient Support System
- CheX-GPT: X-ray Report Enhancement
- CardioAI: Cancer Cardiotoxicity Monitor
- HealthQ: Healthcare Conversation Chain
- PRObot: Diabetic Retinopathy Assistant

Medical LLMs & Benchmarks:
- MediQ: Clinical Reasoning Benchmark
- Touchstone: Segmentation Evaluation
- Medical LLM Adaptation Progress
- Fine-Tuning Medical QA Strategies

AI in Healthcare Ethics:
- Healthcare Robotics with LLMs
- XAI in Clinical Practice
- Precision Rehabilitation Framework
- Multimodal AI Challenges

Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well!

- Full Thread: https://x.com/OpenlifesciAI/status/1855207141302473090
- YouTube: https://youtu.be/ad0uTnYuTo8
- Spotify: https://open.spotify.com/episode/6s39t1UJZk1i10szuXP2qN

aaditya BayesTensor Healthtensor ai-everyday

aaditya BayesTensor mesut07 shetumohanto


Thinking about upgrading from Python 3.10 to 3.11? Here's why you should make the move - a deep technical breakdown that might convince you:

>> Performance Revolution
The performance improvements are staggering, with benchmarks showing 10-60% faster execution across different workloads. Let me break down the game-changing features:

>> Core Architecture Changes
Python 3.11's interpreter now uses statically allocated core modules, eliminating the multi-step loading process we've dealt with in 3.10. This means your applications will start 10-15% faster out of the gate.

>> Function Optimization
The redesigned frame objects are a thing of beauty - they've been stripped of unnecessary baggage, resulting in a 3-7% speedup for all function calls. But it gets better: function calls are now inlined, giving us a 1-3% boost, with recursive functions like Fibonacci seeing up to 1.7x improvement!

>> Adaptive Intelligence
The new Specializing Interpreter is perhaps the most exciting addition. Think of it as a lightweight JIT - it identifies hot code paths and optimizes them automatically.

The interpreter now automatically specializes math operations, array indexing, and even sequence unpacking based on actual usage patterns.

>> Exception Handling Revolution
My favorite feature? Zero-cost exceptions! Your try-except blocks no longer carry overhead when no exceptions occur. The code runs at full speed until an exception actually happens.

Ready to make the switch? These improvements aren't just numbers - they're real-world performance gains waiting to be unlocked in your codebase.


Dear AI developers, have you managed to get any grants or compute powers for your research or startup?
If yes, from where and how?


FLUX 1.1 [pro] Ultra : API - { 4x Higher Image Resolutions }
Upto - 4 Megapixels, 10 seconds per sample. { Hi - Res }

{ Blog Post ⛵ } : https://huggingface.co/blog/prithivMLmods/flux-pro-endpoint

Endpoint Creation Step by Step: 🧵
-> Sign up to { api.bfl.ml } & get your api's: https://api.bfl.ml/auth/profile
-> File Structure:
flux_image_generation/
├── .env
├── generate_image.py
└── requirements.txt

-> Step 0: Add Your API Key to an Environment File
{ .env }
```
BFL_API_KEY=your_actual_api_key_here
```
-> Step 1: Install Required Libraries
 { requirements.txt }
```
requests
python-dotenv
```
-> Step 2: Setup the Python Script
{ generate_image.py} - https://github.com/PRITHIVSAKTHIUR/Flux-API/blob/main/generate_image.py

-> Step3: Install the requirements & Run the Script
```
pip install -r requirements.txt

python generate_image.py

```
-> Polling: The script polls the API every 0.5 seconds until the image generation result is ready. That's it the script  also checks for a successful response after submitting the request.

For more visit:
🔺for script: https://github.com/PRITHIVSAKTHIUR/Flux-API/tree/main
🔺bfl doc: https://docs.bfl.ml/quick_start/gen_image/#__tabbed_1_2

Endpoints for image generation: 🧵
->  /flux-pro-1.1-ultra
->  /flux-pro-1.1
->  /flux-pro
-> /flux-dev

Each ID has 50 free credits available for use, based on the cost per image sample generated by the model.

.
.
.
@prithivMLmods  🤗

hypergod darksfx ai4life44 rdrede prithivMLmods Fl00w AtAndDev lena-ashlyhan Ngrthm RenderIo roland0822

darksfx John6666 hypergod AtAndDev Ngrthm


Exciting to see open-source models thriving in the computer agent space! 🔥
I just built a demo for OS-ATLAS: A Foundation Action Model For Generalist GUI Agents — check it out here: https://huggingface.co/spaces/maxiw/OS-ATLAS

This demo predicts bounding boxes based on screenshot + instructions as input.

venkat-m AiDRIVENiA numbmelon Symbol-LLM programmnix-askui


I will be giving a tutorial at AAAI 2025! Quite excited to share the recent advancements in the field and my contributions to it!

Stay tuned for more updates.
Link: https://x.com/EzgiKorkmazAI/status/1854525141897671111

umarigan Sri-Vigneshwar-DJ ai-everyday John6666 MichaelBrown123 AtAndDev


INTRODUCING Hugging Face AutoTrain Client 🔥
Fine-tuning models got even easier!!!!
Now you can fine-tune SOTA models on all compatible dataset-model pairs on Hugging Face Hub using Python on Hugging Face Servers. Choose from a number of GPU flavors, millions of models and dataset pairs and 10+ tasks 🤗

To try, install autotrain-advanced using pip. You can ignore dependencies and install without --no-deps and then you'd need to install some dependencies by hand.

"pip install autotrain-advanced"

Github repo: https://github.com/huggingface/autotrain-advanced

VincentGOURBIN Reality123b MichielBontenbal ethanker


AndroidLab: Training and Systematic Benchmarking of Android Autonomous
  Agents (2410.24024)

𝗔𝗻𝗱𝗿𝗼𝗶𝗱𝗟𝗮𝗯: 𝗙𝗶𝗿𝘀𝘁 𝗲𝘃𝗲𝗿 𝘀𝘆𝘀𝘁𝗲𝗺𝗮𝘁𝗶𝗰 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗳𝗼𝗿 𝗔𝗻𝗱𝗿𝗼𝗶𝗱 𝗺𝗼𝗯𝗶𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀 𝘀𝗵𝗼𝘄𝘀 𝘁𝗵𝗮𝘁 𝘀𝗺𝗮𝗹𝗹, 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗲𝗱 𝗼𝗽𝗲𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 𝗰𝗮𝗻 𝗽𝗼𝘄𝗲𝗿 𝗮 𝗝𝗔𝗥𝗩𝗜𝗦 𝘀𝘆𝘀𝘁𝗲𝗺 𝗼𝗻 𝘆𝗼𝘂𝗿 𝘀𝗺𝗮𝗿𝘁𝗽𝗵𝗼𝗻𝗲 📱🔥

A team from Tsinghua University just released AndroidLab, the first systematic framework to evaluate and train Android mobile agents that works with both text-only and multimodal models.

They show that fine-tuning small open-source models can significantly boost performance, matching that of much bigger closed models like GPT-4o.

The team built:

📊 A reproducible benchmark with 138 tasks across 9 apps to evaluate mobile agents systematically

📝📱 A framework supporting both text-only (via XML) and visual (via marked screenshots) interfaces

✅ An instruction dataset of 10.5k operation traces for training mobile agents

Key insights:

- 📈 Fine-tuning improves performance BY A LOT: Open-source model Llama-3.1-8B improves from 2% to 24% success rate after training, nearly reaching GPT-4o performance although it’s much smaller
- ⚙ Text-only agents match multimodal ones: XML-based agents achieve similar performance to screenshot-based multimodal agents.

Read their paper here 👉 https://huggingface.co/papers/2410.24024

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Welcome Newcomers, Did you bring your towel?


Quintet Drop : : 🤗

{ Flux LoRA DLC ⛵ } : https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC

-- Purple Dreamy
{ pop of color } : https://huggingface.co/prithivMLmods/Purple-Dreamy-Flux-LoRA

-- Golden Dust
{ shimmer contrast } : https://huggingface.co/prithivMLmods/Golden-Dust-Flux-LoRA

-- Lime Green
{ depth to the composition } : https://huggingface.co/prithivMLmods/Lime-Green-Flux-LoRA

-- Flare Strike
{ Fractured Line } : https://huggingface.co/prithivMLmods/Fractured-Line-Flare

-- Orange Chroma
{ studio lighting } : https://huggingface.co/prithivMLmods/Orange-Chroma-Flux-LoRA
.
.
.
{ collection } : https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be

@prithivMLmods

darksfx rdrede AtAndDev THEMOHY John6666 hypergod ai4life44 s3nh Mrdesigner14 carlizor BuiDoan prithivMLmods Ngrthm RenderIo

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Introducing illusion Diffusion Video
https://huggingface.co/spaces/KingNish/IllusionDiffusionVideo

It can Create high quality ULTRA HD illusion video.
If you find any bugs, please let me know😊