We conducted an experiment in an effort to revive LLaMA 1 33B as it had unique prose and a lack of "GPT-isms" and "slop" in its pretraining data, as well as being one of the favorites at the time. With multiple finetune runs, we were able to extend the model from it's pretrained base of 2048 to ~12,000 tokens adding approx. 500M tokens in the process. The effective length is 16,384 but it's better to keep it on the lower range. It writes well and in multiple formats. In the future, we have some ideas like implementing GQA. Please take a look and we would love to hear your feedback!

https://huggingface.co/ZeusLabs/Chronos-Divergence-33B

YaTharThShaRma999 gregkim tastbuger Ernigma


🚨 Cool tool alert! 🚨

Finally tried Kotaemon, an open-source RAG tool for document chat!

With local models, it's free and private. Perfect for journalists and researchers.

I put Kotaemon to the test with EPA's Greenhouse Gas Inventory. Accurately answered questions on CO2 percentage in 2022 emissions and compared 2022 vs 2021 data

🛠 Kotaemon's no-code interface makes it user-friendly.
- Use your own models or APIs from OpenAI or Cohere
- Great documentation & easy installation
- Multimodal capabilities + reranking
- View sources, navigate docs & create graphRAG

🌟 Kotaemon is gaining traction with 11.3k GitHub stars

Try the online demo: https://huggingface.co/spaces/cin-model/kotaemon-demo
GitHub: https://github.com/Cinnamon/kotaemon
Docs: https://cinnamon.github.io/kotaemon/usage/

victor John6666 gregkim Lewdiculous nbroad yasserrmd Chief-Inspector adorkin Obenlia


Please check the Open Source AI Network: we mapped the top 500 HF users
based on their followers' profiles.

The map can be found here: https://huggingface.co/spaces/bunkalab/mapping_the_OS_community

eliebak victor cfahlgren1 John6666 Korakoe KingNish osanseviero Alcime AdinaY lucianosb merterbak mayurninama djuna bjo163


🚀 Sentence Transformers v3.1 is out! Featuring a hard negatives mining utility to get better models out of your data, a new strong loss function, training with streaming datasets, custom modules, bug fixes, small additions and docs changes. Here's the details:

⛏ Hard Negatives Mining Utility: Hard negatives are texts that are rather similar to some anchor text (e.g. a question), but are not the correct match. They're difficult for a model to distinguish from the correct answer, often resulting in a stronger model after training.
📉 New loss function: This loss function works very well for symmetric tasks (e.g. clustering, classification, finding similar texts/paraphrases) and a bit less so for asymmetric tasks (e.g. question-answer retrieval).
💾 Streaming datasets: You can now train with the datasets.IterableDataset, which doesn't require downloading the full dataset to disk before training. As simple as "streaming=True" in your "datasets.load_dataset".
🧩 Custom Modules: Model authors can now customize a lot more of the components that make up Sentence Transformer models, allowing for a lot more flexibility (e.g. multi-modal, model-specific quirks, etc.)
✨ New arguments to several methods: encode_multi_process gets a progress bar, push_to_hub can now be done to different branches, and CrossEncoders can be downloaded to specific cache directories.
🐛 Bug fixes: Too many to name here, check out the release notes!
📝 Documentation: A particular focus on clarifying the batch samplers in the Package Reference this release.

Check out the full release notes here ⭐: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.1.0

I'm very excited to hear your feedback, and I'm looking forward to the future changes that I have planned, such as ONNX inference! I'm also open to suggestions for new features: feel free to send me your ideas.

osanseviero emirhanbilgic louisbrulenaudet hghjlkjhgk NeelM0906 cschroeder DmitryRyumin HyperBlaze AtAndDev


𝗔𝗿𝗰𝗲𝗲 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝘀 𝗦𝘂𝗽𝗲𝗿𝗡𝗼𝘃𝗮, 𝗯𝗲𝘁𝘁𝗲𝗿 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗲 𝗼𝗳 𝗟𝗹𝗮𝗺𝗮-𝟯.𝟭-𝟳𝟬𝗕!

2⃣ versions: 70B and 8B
🧠 Trained by distilling logits from Llama-3.1-405B
🐥 Used a clever compression method to reduce dataset weight from 2.9 Petabytes down to 50GB (may share it in a paper)
⚙ Not all benchmarks are improved: GPQA and MUSR go down a slight bit
🤗 8B weights are available on HF (not the 70B)

Read their blog post 👉 https://blog.arcee.ai/arcee-supernova-training-pipeline-and-model-composition/
Model weights (8B) 👉 https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite


What if we asked the AI what it thought of our hugging face profile? 👹
I've released a new space capable of doing it.... watch out, it hits hard! 🥊

Try it now ➡  https://huggingface.co/spaces/enzostvs/hugger-roaster

Share your roast below 👇

victor Dlbk Joseph717171 Maykeye den0620 not-lain


🙋🏻♂ hey there folks ,

made an image similarity demo to test out the https://huggingface.co/mistral-community/pixtral-12b-240910 model .

If anyone knows how to generate captions with it , please do let me know x 🚀

here's the demo : https://huggingface.co/spaces/Tonic/Pixtral

hope you like it 🤗


Free research tip:
Get used to writing the first draft of your paper in markdown using vscode’s jupyter notebook extension - it lets you do quick sanity checks with code and maths - an absolute AAA experience:)


🚀 Let’s transform LLMs into encoders 🚀

Auto-regressive LMs have ruled, but encoder-based architectures like GLiNER are proving to be just as powerful for information extraction while offering better efficiency and interpretability. 🔍✨

Past encoder backbones were limited by small pre-training datasets and old techniques, but with innovations like LLM2Vec, we've transformed decoders into high-performing encoders! 🔄💡

What’s New?
🔹Converted Llama & Qwen decoders to advanced encoders
🔹Improved GLiNER architecture to be able to work with rotary positional encoding
🔹New GLiNER (zero-shot NER) & GLiClass (zero-shot classification) models

🔥 Check it out:

New models: https://huggingface.co/collections/knowledgator/llm2encoder-66d1c76e3c8270397efc5b5e

GLiNER package: https://github.com/urchade/GLiNER

GLiClass package: https://github.com/Knowledgator/GLiClass

💻 Read our blog for more insights, and stay tuned for what’s next!
https://medium.com/@knowledgrator/llm2encoders-e7d90b9f5966


> 𝗪𝗮𝗻𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝗵𝗼𝘄 𝗺𝘂𝗰𝗵 𝗮𝗻 𝗔𝗣𝗜 𝗟𝗟𝗠 𝗰𝗮𝗹𝗹 𝗰𝗼𝘀𝘁𝘀 𝘆𝗼𝘂?

I've just made this Space that gets you the API price for any LLM call, for nearly all inference providers out there!

This is based on a comment by @victor under my HF Post a few months back, and leverages BerriAI's data for LLM prices.

Check it out here 👉 https://huggingface.co/spaces/m-ric/text_to_dollars


Distilabel and synthetic data community interviews - the outcomes

We've been doing some interview with community members to understand the needs surrounding synthetic data. Many thanks to the participants. Note that, given they interviewees were sourced from our community, so the results will likely represent that.

Things distilabel does well
- security and reliability by caching generations and having serializable pipelines.
- scaling up generation by parallelising inference and Anyscale Ray
- solid implementations of state of the art research papers

Things to improve
- communication about the fact we support structured generation
- customization of existing prompt implementations are difficult
- creation of new tasks prove difficult
- arguments and parameters for tasks aren't available at first glance
- the learning curve can be steep
- more tutorials that represent real-life usage

Things to note
- create small scale and large scale dataset to Millions of records
- people use synthetic data to move away from frontier model providers
- people mostly use 7B or 70B models for generating

Participate here: https://github.com/argilla-io/distilabel/issues

Tonic osanseviero gabrielmbmb fynnkroeger


🌐 Subdomain Dataset Update: August 2024 Data Now Available

I have updated the https://huggingface.co/datasets/nyuuzyou/subdomains dataset with fresh data for August 2024. This addition further expands this largest collection of subdomain statistics currently available, providing researchers and analysts with even more valuable insights into web infrastructure and domain patterns.

Latest Update Highlights:
- New File: subdomains_2024_08.csv
- Unique Subdomains: 15,124,494
- Total Occurrences: 155,858,868


The Romulus model series has been released on Hugging Face, continually pre-trained on 34,864,949 tokens of French laws and intended to serve as a foundation for fine-tuning on labeled data 🤗

The training code, dataset and model weights are open and available free on HF and the training was based on H100 provided by Microsoft for Startups using Unsloth AI by @danielhanchen and @shimmyshimmer  🦥

Link to the base model: https://huggingface.co/louisbrulenaudet/Romulus-cpt-Llama-3.1-8B-v0.1

Link to the instruct model: https://huggingface.co/louisbrulenaudet/Romulus-cpt-Llama-3.1-8B-v0.1-Instruct

Link to the dataset: https://huggingface.co/datasets/louisbrulenaudet/Romulus-cpt-fr

Please note that these models have not been aligned for the production of usable texts as they stand, and will certainly need to be refined for the desired tasks in order to produce satisfactory results.

danielhanchen kramp osanseviero gabrielmbmb ashutoshzade Gatozu35 lucianosb lastdefiance20


Reflection-Llama-3.1-70B burst onto the scene, surprising everyone! It claimed to outperform others with its novel Reflection-Tuning technique, promising not just to match but to surpass the likes of Claude 3.5 and GPT-4o, leveraging its 70 billion parameters to redefine what open-source could achieve.

And now, everything is crumbling!

The model's performance metrics, especially its 99.2% accuracy on the high school math dataset GSM 8K, have raised eyebrows. While it looked like a valedictorian, based on the open weights, it hardly performs like one.

The model card in the Transformers behaves as Llama 3 and not 3.1.

While the weights were released publicly, they are having issues aligning with the claims. The tuning has been restarted, and the author claims to upload the updated weights soon!

And the big one: the black-boxed API shared is not at all like the open weights. Even more, when pushed hard, the API endpoint claims to be an LLM by Anthropic!

But you might ask, didn't this model beat Anthropic Claude 3.5? Yes, it did.

So, did Claude 3.5 beat Claude 3.5? No, the benchmark is zero-shot, and the claims are that the results are not under zero-shot but under CoT/few-shot!

And to top it all off, the reflecting back idea is not new. But I don't think that's a big deal.

I took some time to look through everything, and now, once tested, this model looks to be worse than Llama 3.1 70B

I still believe the Reflection-Tuning technique is promising. These are the papers discussing its efficacy:
- "Think Before You Speak: Training Language Models With Pause Tokens"
- "Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning"

PS: Matt Shumer/@mattshumer_ (Twitter Handle) (Reflection-Llama-3.1-70B creator) is a great researcher. Let's wait for his updated weights!

Great YT video: https://youtu.be/Xtr_Ll_A9ms

Hugging Face Clem Delangue 🤗?
Can you please help here if possible? This will be the pinnacle of open-source!

John6666 comarproject danielus louisbrulenaudet


ChatGPT does better at math if you prompt it to think like Captain Picard from Star Trek. Scientifically proven fact lol. This got me to thinking, LLM models probably 'think' about the world in weird ways. Far different ways than we would. This got me down a rabbit hole of thinking about different concepts but for LLM models. Somewhere along the way, Python Chemistry was born. To an LLM model, there is a strong connection between Python and Chemistry. To an LLM model, it is easier to understand exactly how Python works, if you frame it in terms of chemistry.

Don't believe me? Ask Python-Chemistry-GPT yourself: https://chatgpt.com/g/g-dzjYhJp4U-python-chemistry-gpt

Want to train your own Python-GPT and prove this concept actually works? Here is the dataset: https://huggingface.co/.../TuringsSolu.../PythonChemistry400


Almost ready: search for a Hugging Face dataset on the Hub from information in the datasets viewer preview!

Soon, you can find deep-cut datasets even if they don't have a full dataset card (you should still document your datasets!)

You can help improve this project by rating synthetic user search queries for hub datasets.

If you have a Hub login, you can start annotating in Argilla
 in < 5 seconds here: https://davanstrien-my-argilla.hf.space/dataset/1100a091-7f3f-4a6e-ad51-4e859abab58f/annotation-mode

I need to do some tidying, but I'll share all the code and in-progress datasets for this soon!

nyuuzyou jeffboudier John6666 boapps louisbrulenaudet clem celinah


A lot of coverage of the Apple event! I’ve selected a few unique angles and distinctive takes.

**The NYT**
- "The iPhone’s limited feature set is emblematic of how Apple is taking a cautious approach to generative A.I."
- "Wall Street is enthusiastic about the artificially intelligent phones, with analysts predicting the features could help Apple sell a record 240 million iPhones next year."

**The Guardian**
- "Despite the bells and whistles, and being a tech-adopting lot, I bet many of you won’t be lining up to buy it."
- One reason is the simple cost of the iPhone 16, which starts at $799.
- The adoption of AI into the iPhone could be considered a step change in how the iPhone works. But there may not be a huge hankering to use ChatGPT on your phone."

**The WSJ**
- Apple didn’t say when the AI services would be available in China, its second-largest market after the U.S.
- The delay puts the iPhone maker at a disadvantage against rivals offering AI services
 - Huawei held its own announcement in China to release the Mate XT, a three-way foldable smartphone with AI features.
- Apple said that the launch of Apple Intelligence was subject to regulatory approval. In China, any generative AI models that could influence public opinion need government approval.

**CNN**
- "For an event built around unveiling Apple’s first AI-powered iPhone, there was one striking absence over the two-hour presentation: the words 'artificial intelligence.'"
- "But Apple understands something that often gets lost in the bot-pilled bubble of Silicon Valley: Regular people don’t trust AI."

Links:
https://www.nytimes.com/2024/09/09/technology/apple-event-iphone-16-watch.html
https://www.theguardian.com/technology/article/2024/sep/10/techscape-iphone-16-cost-features
https://www.wsj.com/tech/apples-challenge-in-china-rises-with-new-rival-phones-and-ai-delay-8cf871fb?mod=rss_Technology
https://www.cnn.com/2024/09/10/business/apple-iphone-ai-nightcap/


Help me to upgrade my model.

Hi all, so I am a complete beginner in coding, however, with the help of Claude (similar to Matt :P) and GPT 4o have been able to develop this RAG PDF summarizer/Q&A plus a web search tool.

The application is specifically built for summarization task including summarizing a financial document, news article, resume, research document, call transcript, etc.

The space could be found here: https://huggingface.co/spaces/Shreyas094/SearchGPT

The news tool simply use duckduckgo chat to generate the search results using llama 3.1 70bn model.

I want your support to fine tune the retrieval task for handling more unstructured documents.


appvoid/arco

arco consistently outperforms every sota model below 600m parameters on average

https://huggingface.co/appvoid/arco


> Article read: Simple guide to LLM inference and to TGI

I've just read article "LLM inference at scale with TGI" by @martinigoyanes . It's really good content, a must-read if you want a good low-level intro to LLM inference with TGI!

My takeaways:

How does inference work?
🧠 Prefill: the input prompt is tokenized on CPU, then transferred to GPU. Then one single forward pass generates the initial token.
🔄 Decode: the model generates ("decodes") tokens one by one, each time appending the new token to the current input of size N to then generate a new token again with this augmented input of length N+1. This loop ends either when a specific token called "End-of-sequence" is generated or when the completion reaches a pre-specified maximum length. Then the sequence is de-tokenized on CPU to yield text again.
    ⏱ This step's speed determines the Time Per Output Token, which directly translates to the key metric: Throughput

🤔 How was the separation between the two steps decided ? Like, why does prefill include this strange generation of only one token at then end?
➡ The cost of attention scales quadratically with the number of tokens, so it can really explode quickly.
To compensate for that, a really important technique called KV caching was devised: using the fact that when generating token N+1, the Key and Value (K and V) matrices generated inside the Transformers are a simple extension from the K and V from the previous step, the model caches the K and V matrices between steps : thus the separation - the prefill part is the part that prepares this KV cache, while the decoding is the one that leverages it and expands it by one at each step.

TGI-specific takeaways:
⚙ TGI has many SOTA techniques for decoding: Paged Attention, KV Caching and Flash Attention…
🔀 TGI's router handles generations finishing early because of an EOS token: instead of static batching, it continuously batches requests to the inference engine & filters away finished requests.


I never imagined that Jenkins could be as powerful and easy to implement as GitHub Actions. Loving it. 🥰


🎓 Introducing the конспекты-уроков.рф Lesson Plans Dataset - https://huggingface.co/datasets/nyuuzyou/classnotes

Dataset highlights:
- Metadata for 65,068 lesson plans from конспекты-уроков.рф
- 58,433 lesson plans available in original format
- Multilingual content: Primarily Russian, with some Kazakh, Ukrainian, Belarusian, and English
- Each entry includes: URL, title, description, author, publication date, file size, and download link
- Data reflects educational materials accessible through the конспекты-уроков.рф platform
- Licensed under Creative Commons (https://creativecommons.org/licenses/by-nc/3.0/deed.en)

This dataset offers a unique window into online educational resources, particularly in Russian-language contexts. It provides opportunities for analyzing lesson plan trends, topic distributions, and language patterns in educational materials. The dataset is particularly well-suited for tasks such as text classification and text retrieval in multilingual educational settings.


So awesome , now i can deploy a jupyterlab on huggingface and deploy gradio from the jupyterlab


NEW RELEASE!

- MOTH is a generalist chat model, using high quality synthetic data to improve general performance.
- Currently available for Llama 3.1 and Gemma 2, more models to follow in the future.

get the models:
https://huggingface.co/sequelbox/Llama3.1-8B-MOTH
https://huggingface.co/sequelbox/gemma-2-9B-MOTH

get the dataset:
https://huggingface.co/datasets/sequelbox/Supernova

<3 for everyone to use <3


Ultimate FLUX LoRA Training Tutorial: Windows and Cloud Deployment

I have done total 104 different LoRA trainings and compared each one of them to find the very best hyper parameters and the workflow for FLUX LoRA training by using Kohya GUI training script.

You can see all the done experiments’ checkpoint names and their repo links in following public post: https://www.patreon.com/posts/110838414

After completing all these FLUX LoRA trainings by using the most VRAM optimal and performant optimizer Adafactor I came up with all of the following ranked ready to use configurations.

You can download all the configurations, all research data, installers and instructions at the following link : https://www.patreon.com/posts/110879657

Tutorials
I also have prepared 2 full tutorials. First tutorial covers how to train and use the best FLUX LoRA locally on your Windows computer : https://youtu.be/nySGu12Y05k

This is the main tutorial that you have to watch without skipping to learn everything. It has total 74 chapters, manually written English captions. It is a perfect resource to become 0 to hero for FLUX LoRA training.

The second tutorial I have prepared is for how to train FLUX LoRA on cloud. This tutorial is super extremely important for several reasons. If you don’t have a powerful GPU, you can rent a very powerful and very cheap GPU on Massed Compute and RunPod. I prefer Massed Compute since it is faster and cheaper with our special coupon SECourses. Another reason is that in this tutorial video, I have fully in details shown how to train on a multiple GPU setup to scale your training speed. Moreover, I have shown how to upload your checkpoints and files ultra fast to Hugging Face for saving and transferring for free. Still watch first above Windows tutorial to be able to follow below cloud tutorial : https://youtu.be/-uhL2nW7Ddw

For upscaling SUPIR used : https://youtu.be/OYxVEvDf284


We can’t think in more than three dimensions.

But we have no problem doing math and writing computer programs in many dimensions. It just works.

I find that extremely crazy.


The Hugging Face Semantic Dataset Search Space is back in action! You can find similar datasets by ID or perform a semantic search of dataset cards.

Give it a try: https://huggingface.co/spaces/librarian-bots/huggingface-datasets-semantic-search

alielfilali01 Tonic ajibawa-2023 artem-durynin jsulz louisbrulenaudet


Remember when @Google launched MediaPipe in an effort to create efficient on-device pipelines?

They've just unlocked the ability to run 7B+ parameter language models directly in your browser. This is a game-changer for on-device AI!

Yes, they are streaming 8.6 GB model files!

Currently, they have Gemma 2B/7B running, but imagine Dynamic LoRA, multimodal support, quantization, and you never leaving Chrome!

This is a significant technical advancement, especially in Memory Optimization:

- Redesigned the model-loading code to work around WebAssembly's 4 GB memory limit.
- Implemented asynchronous loading of transformer stack layers (28 for Gemma 1.1 7B).
- Reduced peak WebAssembly memory usage to less than 1% of previous requirements.

Cross-Platform Compatibility
- Compiled the C++ codebase to WebAssembly for broad browser support.
- Utilized the WebGPU API for native GPU acceleration in browsers.

Here's why this matters:

1. Privacy: No need to send data to remote servers.
2. Cost-Efficiency: Eliminates server expenses.
3. Offline Capabilities: Use powerful AI without an internet connection.

Blog: https://research.google/blog/unlocking-7b-language-models-in-your-browser-a-deep-dive-with-google-ai-edges-mediapipe/


⚖ 𝐀𝐈 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐢𝐬 𝐂𝐨𝐩𝐲𝐫𝐢𝐠𝐡𝐭 𝐈𝐧𝐟𝐫𝐢𝐧𝐠𝐞𝐦𝐞𝐧𝐭

⚖ 𝐀𝐈 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐢𝐬 𝐂𝐨𝐩𝐲𝐫𝐢𝐠𝐡𝐭 𝐈𝐧𝐟𝐫𝐢𝐧𝐠𝐞𝐦𝐞𝐧𝐭

⚖ 𝐀𝐈 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐢𝐬 𝐂𝐨𝐩𝐲𝐫𝐢𝐠𝐡𝐭 𝐈𝐧𝐟𝐫𝐢𝐧𝐠𝐞𝐦𝐞𝐧𝐭

This bold claim is not my opinion, but it has been made in a recent "report" of a group, whose stance is recognizable in their name. It is roughly translated as "Authors' Rights Initiative". They published a report which was also presented before the EU Parliament according to the LinkedIn post below.

I am not really interested in politics, but as an EU citizen I am of course somewhat interested in a reasonable and practical version of the EU AI Act. Not saying there should not be rules around data and AI, but this report is obviously very biased towards one side.

While I think the report itself does not deserve attention, I post it in the hope that you find more examples, where they did not address the issue adequately. Feel free to add to my LinkedIn posts (where the original authors will see it) or here.

[en] Executive summary: https://urheber.info/media/pages/diskurs/ai-training-is-copyright-infringement/3b900058e6-1725460935/executive-summary_engl_final_29-08-2024.pdf
[de] Full report: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4946214

LinkedIn: https://www.linkedin.com/posts/activity-7238912869268959232-6cFx


The New York Times did a fun quiz to test your ability to detect whether a video is AI-generated or real. They put Runway, Kling, and Sora to the test.

I got 10/10 🤓 —how about you?

https://www.nytimes.com/interactive/2024/09/09/technology/ai-video-deepfake-runway-kling-quiz.html


Quick Gradio update and the app is back!!

https://huggingface.co/spaces/sourceoftruthdata/sot_autotrain_dreambooth_v1.1

Free image generation!


Interested in learning about everything Image?

With the rise of recent interest in Vision Language Models (VLMs), we decided to make a push to include an ImageField within Argilla! This means any open source developer can now work on better models for vision ML tasks too and we would like to show you how.

We would love to introduce this new feature to you, so we've prepared a set of notebooks to go over some common image scenarios.
finetune an CLIP retrieval model with sentence transformers
use ColPali+ Qwen VL for RAG and log the results to Argilla
image-generation preference: creating multi-modal preference datasets for free using Hugging Face inference endpoints.

See you on Thursday!

https://lu.ma/x7id1jqu


Hi, just a brief follow-up on our Guided Reasoning (GuiR) system:

I've created a template space that facilitates testing:

1. Duplicate space https://huggingface.co/spaces/logikon/guir-chat
2. Setup your own inference servers and provide details in config file
3. Add api keys as secrets
4. Your personal GuiR playground is ready

Cheers, Gregor


"It's Sunday night, fancy a game?"
https://kz919-can-you-beat-405b-in-chess.hf.space/
built with the one and only SN fast API:
https://sambanova.ai/fast-api?api_ref=907266


Hello all,

I'm excited to share my article introducing AISAK's new flagship model, AISAK-O (Artificially Intelligent Swiss Army Knife OPTIMUM).

Read the full details here:

https://huggingface.co/blog/mandelakori/aisak-o

Key highlights of AISAK-O include:

- 8 billion parameters and a 32k token context length
- Multimodal capabilities for processing both text and visual data
- Impressive benchmark scores, surpassing GPT-4V in some areas
- Specialized in tasks like image captioning, visual reasoning, and cohesive
   content generation
- Efficient architecture competing with larger models

We're also offering a unique beta testing opportunity with access to inference code.

For more information or partnership inquiries, please contact us at mandelakorilogan@gmail.com.

I hope you find this advancement in multimodal AI as exciting as we do!
https://huggingface.co/aisak-ai/O


An example of the application of LegalKit is the production of knowledge graphs, here is a demo Space 🔗

With the update of the French legal code data model uploaded to 🤗 and the introduction of a column dedicated to HTML text, it's now easy to extract links between different articles and produce complex graphs with just a few lines of Python.

This simplified demo highlights the ease of implementation and creative potential, and enables the generation of complete data sets, although requiring a powerful graphics card for display. The framework used for the moment is D3.js, but perhaps other solutions are possible. I'd be delighted to hear your suggestions, and look forward to hearing from the community.

Link to the 🤗 Space: https://huggingface.co/spaces/louisbrulenaudet/legalkit-knowledge-graph


🌐 Introducing Edupres.ru Presentations Dataset - https://huggingface.co/datasets/nyuuzyou/edupres

Dataset highlights:
- Metadata for 44,210 presentations from edupres.ru
- 21,941 presentations available in original format
- Multilingual content: Primarily Russian, with some Ukrainian, Belarusian, and English
- Each entry includes: URL, title, description, author, publication date, file size, and download link
- Data reflects educational presentations accessible through the Edupres.ru platform
- Licensed under Creative Commons Zero (CC0) for unrestricted use

This dataset offers a unique window into online educational resources, particularly in Russian-language contexts. It provides opportunities for analyzing presentation trends, topic distributions, and language patterns in educational materials. The dataset is particularly well-suited for tasks such as text classification and text retrieval in multilingual educational settings.


This is an absolutely mind-boggling experiment!

@GuangyuRobert (Twitter Handle) from MIT has created Project Sid, which simulates over 1,000 autonomous AI agents collaborating in a Minecraft environment, operating for extended periods without human intervention. This simulation demonstrates unprecedented levels of agent interaction, decision-making, and societal development.

Agents operate independently for hours or days, showcasing advanced decision-making algorithms and goal-oriented behavior.

The simulation produced complex, emergent phenomena, including:
- Economic systems with currency (gems) and trading
- Cultural development and religious practices
- Agents even understood bribing. Priests were moving the most gems to bribe people into following them!
- Governmental structures and democratic processes

Project Sid addresses fundamental challenges in AI research:
- Coherence: Maintaining consistent agent behavior over extended periods.
- Multi-agent Collaboration: Enabling effective communication and coordination among numerous AI entities.
- Long-term Progression: Developing agents capable of learning and evolving over time.

While Minecraft serves as the initial testbed, the underlying AI architecture is designed to be game-agnostic, suggesting potential applications in various digital environments and real-world simulations.

Imagine a policy being debated by the government and how it might affect society; Sid can simulate its impact!

Even if this remains just a game experiment, the project successfully manages 1,000+ agents simultaneously, a feat that requires robust distributed computing and efficient agent architecture.

createtheimaginable John6666 jlzhou flflow


SemanticFinder now supports WebGPU thanks to @Xenova's efforts with transformers.js v3!
Expect massive performance gains. Inferenced a whole book with 46k chunks in <5min. If your device doesn't support #WebGPU use the classic Wasm-based version:
- WebGPU: https://do-me.github.io/SemanticFinder/webgpu/
- Wasm: https://do-me.github.io/SemanticFinder/

WebGPU harnesses the full power of your hardware, no longer being restricted to just the CPU. The speedup is significant (4-60x) for all kinds of devices: consumer-grade laptops, heavy Nvidia GPU setups or Apple Silicon. Measure the difference for your device here: https://huggingface.co/spaces/Xenova/webgpu-embedding-benchmark
Chrome currently works out of the box, Firefox requires some tweaking.

WebGPU + transformers.js allows to build amazing applications and make them accessible to everyone. E.g. SemanticFinder could become a simple GUI for populating your (vector) DB of choice. See the pre-indexed community texts here: https://huggingface.co/datasets/do-me/SemanticFinder
Happy to hear your ideas!

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💾🧠How much VRAM will you need for training your AI model? 💾🧠
Check out this app where you convert:
Pytorch/tensorflow summary -> required VRAM
or
Parameter count -> required VRAM

Use it in: http://howmuchvram.com

And everything is open source! Ask for new functionalities or contribute in:
https://github.com/AlexBodner/How_Much_VRAM
If it's useful to you leave a star 🌟and share it to someone that will find the tool useful!
More discussion in: https://x.com/AlexBodner_/status/1832054850294812679

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Last Week in Medical AI: Top Research Papers/Models
🏅(September 1  - September 7, 2024)

Medical LLM & Other Models :
- CancerLLM: Large Language Model in Cancer Domain
- MedUnA: Vision-Language Models for Medical Image
- Foundation Model for Robotic Endoscopic Surgery
- Med-MoE: MoE for Medical Vision-Language Models
- CanvOI: Foundation Model for Oncology
- UniUSNet: Ultrasound Disease Prediction
- DHIN: Decentralized Health Intelligence Network

Medical Benchmarks and Evaluations:
- TrialBench: Clinical Trial Datasets & Benchmark
- LLMs for Medical Q&A Evaluation
- MedFuzz: Exploring Robustness Medical LLMs
- MedS-Bench: Evaluating LLMs in Clinical Tasks
- DiversityMedQA: Assessing LLM Bias in Diagnosis
- LLM Performance in Gastroenterology

LLM Digital Twins:
- Digital Twins for Rare Gynecological Tumors
- DT-GPT: Digital Twins for Patient Health Forecasting

Medical LLM Applications:
- HIPPO: Explainable AI for Pathology
- LLMs vs Humans in CBT Therapy
- ASD-Chat: LLMs for Autistic Children
- LLMs for Mental Health
- LLMs for Postoperative Risk Prediction

Frameworks and Methodologies:
- Rx Strategist: LLM-based Prescription Verification
- Medical Confidence Elicitation
- Guardrails for Medical LLMs

Check the full thread : https://x.com/OpenlifesciAI/status/1832476252260712788

Thank you for your continued support and love for this series! Stay up-to-date with weekly updates on Medical LLMs, datasets, and top research papers by following @aaditya   🤗

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aaditya BayesTensor Joseph717171 Svngoku

aaditya BayesTensor Joseph717171 genaihuman


🌐 Yet another dataset - https://huggingface.co/datasets/nyuuzyou/rule34world

Dataset highlights:
- Metadata for 580,977 image files from rule34.world
- Monolingual content: English tags and metadata
- Each entry includes: URL, image URL, filepath, tags, and like count
- Data reflects images available on the Rule34.world platform up to August/September 2024
- Licensed under Creative Commons Zero (CC0) for unrestricted use
This dataset offers a unique window into online anime and art communities, particularly those focused on adult content. It provides opportunities for analyzing tagging trends, image popularity, and content patterns in user-generated art platforms.

This dataset contains high-quality images and tags, making it a great source of data for training LoRA models.


How can I make my RAG application generate real-time responses? Up until now, I have been using Groq for fast LLM generation and the Gradio Live function. I am looking for a better solution that can help me build a real-time application without any delay. @abidlabs

https://huggingface.co/spaces/kingabzpro/Real-Time-RAG


Google's Chain-of-Thought (CoT) is one of the most effective ways to improve LLMs' reasoning.

Researchers have now developed a novel approach called Strategic Chain-of-Thought (SCoT) to enhance the reasoning capabilities of large language models even further.

🧠 SCoT uses a two-stage process within a single prompt:
- Strategy Elicitation: The model first identifies and determines an effective problem-solving strategy for the given task. This becomes the strategic knowledge that guides the reasoning process.
- Strategy Application: The model then applies the identified strategic knowledge to solve the problem and generate the final answer.

Essentially, SCoT integrates strategic knowledge to guide reasoning without relying on external knowledge sources or multiple queries.

According to the research, SCoT showed significant improvements over standard CoT across various datasets, including a 21.05% increase on the GSM8K math dataset and a 24.13% increase on the Tracking_Objects spatial reasoning task.

Changes in the Prompt Structure:
The SCoT prompt typically consists of five components:
- Role: Defines the expert role the model should assume.
- Workflow: Outlines the steps for strategy identification and application.
- Rules: Specifies guidelines for generating answers.
- Initialization: Sets up the task.
- Task Input: Provides the specific problem to solve.

Strategy Generation:
The model is prompted to generate strategic knowledge relevant to the problem domain. For example, in mathematics, it might favor elegant solutions like using arithmetic series formulas over brute-force calculations.

Guided Reasoning:
Using the elicited strategy, the model then generates a chain-of-thought reasoning path. This approach aims to produce more stable and higher-quality outputs compared to standard chain-of-thought methods.

Read the full paper: https://arxiv.org/abs/2409.03271


i just made the best 0.5b model to date (again)

its name is arco and is ready to fight any 0.5b model at arc challenge

https://huggingface.co/appvoid/arco

John6666 Tonic AtAndDev louisbrulenaudet


Reposting from twitter:

Just so you all know, I'll be on vacation for the following two weeks and away from home! I'm hoping to get on at least once a day to load up some quants, but I won't be as bleeding edge and on the ball :) feel free to shoot me a message if you see one I should make!

In the meantime if you need something bleeding edge make sure to check out @MaziyarPanahi or @bullerwins who both put out great work!

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FLUX Prompt Generator Updates

- https://huggingface.co/spaces/gokaygokay/FLUX-Prompt-Generator

- There are now hundreds of new selections across diverse categories, each offering a lot of choices:

Architecture, Art, Artist, Brands, Character, Cinematic, Fashion, Feelings, Geography, Human, Interaction, Keywords, Objects, People, Photography, Plots, Poses, Scene, Science, Stuff, Time, Typography, Vehicle, Video Game

- In addition to Hugging Face, I've integrated new LLM providers: Groq, OpenAI, and Claude.

- Upgraded Vision Language Models (VLMs): We now feature Qwen2-VL, JoyCaption and Florence-2-large.

- New specialized system prompts for various styles and themes, including Happy, Simple, Poster, Only Objects, No Figure, Landscape, Fantasy.

John6666 YaTharThShaRma999 KingNish ucsahin Chief-Inspector EmilyChan victor Felladrin Nyxie7 gokaygokay


Yesterday  @mattshumer released https://huggingface.co/mattshumer/Reflection-Llama-3.1-70B, an impressive model that achieved incredible results in benchmarks like MMLU. The model was fine-tuned using Reflection-Tuning and the dataset used wasn't released, but I created a small recipe with distilabel that allows generating a dataset with a similar output format:

1. We use MagPie 🐦  in combination with https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct to generate reasoning instructions.
2. We generate a response again using https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct, but we steer the LLM to generate an specific output format using a custom system prompt. In the system prompt, we instruct the LLM that it will have first to think 💭 and have reflections that will help resolving ambiguities. After that, we instruct the LLM to generate an output based on the previous thinking

In this dataset https://huggingface.co/datasets/gabrielmbmb/distilabel-reflection-tuning you can found 5 rows that I generated with this recipe. You can also found the code of the pipeline in the file called `reflection.py`.

clem prithivMLmods KonradSzafer Svngoku John6666 den0620 osanseviero gabrielmbmb louisbrulenaudet


hemanuelly hunken Sri-Vigneshwar-DJ Tonioesparza den0620 Akash3104


"LLM inference at scale with TGI". Cool blogpost: https://www.adyen.com/knowledge-hub/llm-inference-at-scale-with-tgi

Well done
@martinigoyanes @rafa-hernandez @Vidusharma @frisokingma @hannahwright @jeanmarcs @antonioramos & the whole https://huggingface.co/adyen team. Could be useful to cross-post here: https://huggingface.co/blog/community

vilarin KingNish victor prithivMLmods nbroad


💾🧠How much VRAM will you need for training your AI model? 💾🧠

Pytorch/tensorflow summary -> required VRAM

And everything is open source! Ask for new functionalities or contribute in:

If it's useful to you leave a star 🌟and share it to someone that will find the tool useful!


 I've been working on a  Space to make it super easy to create notebooks and help users quickly understand and manipulate their data!
With just a few clicks automatically generate notebooks for:

📊 Exploratory Data Analysis
🧠 Text Embeddings
🤖 Retrieval-Augmented Generation (RAG)

✨ Automatic training is coming soon!
Check it out here https://huggingface.co/spaces/asoria/auto-notebook-creator
Appreciate any feedback to improve this tool 🤗


🌟 Argilla v2.1.0 goes multi-modal: Image Field, Dark Mode, Enhanched Hugging Face Hub imports and more!

🖼 Image Field: Seamlessly work with multimodal datasets
🌓 Dark Mode: Reduce eye strain with our sleek new look
🤗 Enhanced Hugging Face Hub import with the SDK
🇪🇸 Spanish UI: Breaking language barriers

Plus more improvements to supercharge your model curation workflow!

Check out the full announcement for details and code examples: https://github.com/argilla-io/argilla/compare/v2.0.1...v2.1.0

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gabrielmbmb dvilasuero Ameeeee clem AtAndDev

Ameeeee clem KingNish AtAndDev gabrielmbmb


Wanted to train a FLUX model using out-of-copyright images, so I curated concept art images from NASA.

Model: https://huggingface.co/davanstrien/nasa_concept_art
Dataset: https://huggingface.co/datasets/davanstrien/nasa_concept_art

So far, training was done without captions, but I'm experimenting with using VLLMs to generate captions to see if that improves the model.


🤯 𝗔 𝗻𝗲𝘄 𝟳𝟬𝗕 𝗼𝗽𝗲𝗻-𝘄𝗲𝗶𝗴𝗵𝘁𝘀 𝗟𝗟𝗠 𝗯𝗲𝗮𝘁𝘀 𝗖𝗹𝗮𝘂𝗱𝗲-𝟯.𝟱-𝗦𝗼𝗻𝗻𝗲𝘁 𝗮𝗻𝗱 𝗚𝗣𝗧-𝟰𝗼!

@mattshumer, CEO from Hyperwrite AI, had an idea he wanted to try out: why not fine-tune LLMs to always output their thoughts in specific parts, delineated by <thinking> tags?

Even better: inside of that, you could nest other sections, to reflect critically on previous output. Let’s name this part <reflection>. Planning is also put in a separate step.

He named the method “Reflection tuning” and set out to fine-tune a Llama-3.1-70B with it.

Well it turns out, it works mind-boggingly well!

🤯 Reflection-70B beats GPT-4o, Sonnet-3.5, and even the much bigger Llama-3.1-405B!

𝗧𝗟;𝗗𝗥
🥊 This new 70B open-weights model beats GPT-4o, Claude Sonnet, et al.
⏰ 405B in training, coming soon
📚 Report coming next week
⚙ Uses GlaiveAI synthetic data
🤗 Available on HF!

I’m starting an Inference Endpoint right now for this model to give it a spin!

Check it out 👉 https://huggingface.co/mattshumer/Reflection-Llama-3.1-70B

TahirC Yuuru iandeanschaefer trollek Joseph717171


I'm building an AI for healthcare support for professionals, any advice? I could create a new app here but it needs a lot of trainer (Im newbie in this kind of stuff)
Thank you, guys!!!!


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


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  🧨

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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)


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


Good lord... Spent almost a day debugging this and it turns out it was an issue of gradio update incompatible with the new fastapi.
https://discuss.huggingface.co/t/huggingface-space-failed-after-working-initially/105514/8

Finally got it back online! Come chat with your favorite anime characters here:
https://huggingface.co/spaces/kz919/Persona-AI


Good folks at Epoch AI have just released their most comprehensive database yet, tracking over 800 state-of-the-art and historically notable AI models. This incredible resource provides key insights into the factors driving machine learning progress.

Since 2010, the training compute used to create AI models has been growing at a staggering rate of 4.1x per year. That means the computational power behind these models is doubling roughly every six months! And it's not just the compute that's increasing - the costs are too. Training compute costs for the largest models are doubling every nine months, with the most advanced models now costing hundreds of millions of dollars.

Interestingly, training compute has scaled up faster for language models compared to vision. While the largest vision and language models had similar compute requirements before 2020, language models have since rapidly outpaced vision models, driven by the success of transformer architectures. The size of datasets used to train language models is also doubling approximately every eight months.

Another fascinating trend is that the length of time spent training notable models is growing by about 1.2x per year. While longer training times could ease hardware constraints, there is a tradeoff to consider. For very long runs, waiting for algorithmic and hardware improvements might be more beneficial than simply extending training.

If this continues, by 2028, we will reach cluster prices in the 100 billion dollars, using 10GW of power!

Link: https://epochai.org/data/notable-ai-models


Decided to try to check how many weights in a 70b F32 model would be squashed when converted to F16 (spoiler, it's shockingly few)

The reason for this comparison is that it should represent the same percentage of squishing as bf16 to fp16

Had claude make me a script, using the new Reflection-70B, and these are the results:

Total weights: 70553706496
Fully representable: 70530215524
Squashed: 23490972
Percentage squashed: 0.03%

0.03%!!!!

A couple things to note, this uses a roundtrip of F32 -> F16 -> F32 and then torch.isclose to account for rounding errors that come up by the very nature of extremely accurate numbers, but it uses VERY small tolerances (rtol=1e-5, atol=1e-8)

This is also examining EVERY weight that was stored at F32, and for most layers I was somewhere between 0% and 0.03% of weights being squashed, no major outliers.

Overall, I feel even safer converting to F16 for llama.cpp, the extremely small number of weights that fall outside the range are likely so small that they don't actually play a role in the final output of the model at inference anyways.


@ehartford https://huggingface.co/CohereForAI/c4ai-command-r-plus dolphin when?

John6666 mahiatlinux danielus Locutusque den0620 AtAndDev


The `timm` leaderboard https://huggingface.co/spaces/timm/leaderboard has been updated with the ability to select different hardware benchmark sets: RTX4090, RTX3090, two different CPUs along with some NCHW / NHWC layout and torch.compile (dynamo) variations.

Also worth pointing out, there are three rather newish 'test' models that you'll see at the top of any samples/sec comparison:
* test_vit (https://huggingface.co/timm/test_vit.r160_in1k)
* test_efficientnet (https://huggingface.co/timm/test_efficientnet.r160_in1k)
* test_byobnet (https://huggingface.co/timm/test_byobnet.r160_in1k, a mix of resnet, darknet, effnet/regnet like blocks)

They are < 0.5M params, insanely fast and originally intended for unit testing w/ real weights. They have awful ImageNet top-1, it's rare to have anyone bother to train a model this small on ImageNet (the classifier is roughly 30-70% of the param count!). However, they are FAST on very limited hadware and you can fine-tune them well on small data. Could be the model you're looking for?


I have put together a notebook on Multimodal RAG, where we do not process the documents with hefty pipelines but natively use:
- https://huggingface.co/vidore/colpali for retrieval 📖 it doesn't need indexing with image-text pairs but just images!
- https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct for generation 💬 directly feed images as is to a vision language model with no processing to text!
I used ColPali implementation of the new 🐭 Byaldi library by @bclavie  🤗
https://github.com/answerdotai/byaldi
Link to notebook: https://github.com/merveenoyan/smol-vision/blob/main/ColPali_%2B_Qwen2_VL.ipynb

rwightman umair894 clem louisbrulenaudet s3nh John6666 BasitMustafa Johnyquest7 phanhoang resbyte denizaybey allandclive fdaudens vilarin ak0601 rreed-pha xi0v Rajaram1996 Rayvee oceansweep parjun byteprobe Filippo

hitchhiker3010 Csplk sasikiran fsommers rogermt navin7 sambarnett96 oceansweep ysdede shreyamondal

rreed-pha oceansweep Yassmen madstuntman11


How do i access llama 3.1 70b in my space ?

this doesn't seem to work, can someone help me with a working code

from transformers import AutoConfig

config = AutoConfig.from_pretrained("meta-llama/Meta-Llama-3.1-70B", revision="main")
config.rope_scaling = {"type": "llama3", "factor": 8.0}

model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-70B", config=config, use_auth_token=True)


🚀 𝗪𝗵𝗲𝗿𝗲 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗹𝗮𝘄𝘀 𝗮𝗿𝗲 𝘁𝗮𝗸𝗶𝗻𝗴 𝘂𝘀 : 𝗯𝘆 𝟮𝟬𝟮𝟴, 𝗔𝗜 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝗮𝗰𝗵 𝘁𝗵𝗲 𝗽𝗼𝘄𝗲𝗿 𝗰𝗼𝗻𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻 𝗼𝗳 𝗲𝗻𝘁𝗶𝗿𝗲 𝗰𝗼𝘂𝗻𝘁𝗿𝗶𝗲𝘀

Reminder : “Scaling laws” are empirical laws saying that  if you keep multiplying your compute by x10, your models will mechanically keep getting better and better.

To give you an idea, GPT-3 can barely write sentences, and GPT-4, which only used x15 its amount of compute, already sounds much smarter than some of my friends (although it's not really - or at least I haven't tested them side-by side). So you can imagine how far a x100 over GPT-4 can take us.

🏎 As a result, tech titans are racing to build the biggest models, and for this they need gigantic training clusters.

The picture below shows the growth of training compute: it is increasing at a steady exponential rate of a x10 every 2 years. So let’s take this progress a bit further:
- 2022: starting training for GPT-4 : 10^26 FLOPs, cost of $100M
- 2024: today, companies start training on much larger clusters like the “super AI cluster” of Elon Musk’s xAI, 10^27 FLOPS, $1B
- 2026 : by then clusters will require 1GW, i.e. around the full power generated by a nuclear reactor
- 2028: we reach cluster prices in the 100 billion dollars, using 10GW, more than the most powerful power stations currently in use in the US. This last size seems crazy, but Microsoft and OpenAI already are planning one.

Will AI clusters effectively reach these crazy sizes where the consume as much as entire countries?
➡ Three key ingredients of training might be a roadblock to scaling up :
💸 Money: but it’s very unlikely, given the potential market size for AGI, that investors lose interest.
⚡ Energy supply at a specific location
📚 Training data: we’re already using 15 trillion tokens for Llama-3.1 when Internet has something like 60 trillion.

🤔 I’d be curious to hear your thoughts: do you think we’ll race all the way there?


🌐 Introducing PPT Online Dataset - https://huggingface.co/datasets/nyuuzyou/pptonline

Dataset highlights:
- Metadata for 1,418,349 PowerPoint (.ppt) files from ppt-online.org
- Multilingual content: Russian, Ukrainian, Belarusian, Kazakh, English, and others
- Each entry includes: Unique ID, title, category, download link, file size, and content snippet
- Data reflects presentations accessible through the PPT Online platform
- Licensed under Creative Commons Zero (CC0) for unrestricted use

This dataset offers a unique window into online educational resources, particularly in Eastern European and Central Asian contexts. It provides opportunities for analyzing presentation trends, topic distributions, and language patterns in educational materials.


Hey everyone 🤗!
We (finegrain) have created some custom ComfyUI nodes to use our refiners micro-framework inside comfy! 🎉

We only support our new Box Segmenter at the moment, but we're thinking of adding more nodes since there seems to be a demand for it. We leverage the new (beta) Comfy Registry to host our nodes. They are available at: https://registry.comfy.org/publishers/finegrain/nodes/comfyui-refiners. You can install them by running:
```
comfy node registry-install comfyui-refiners
```
Or by unzipping the archive you can download by clicking "Download Latest" into your `custom_nodes` comfy folder.
We are eager to hear your feedbacks and suggestions for new nodes and how you'll use them! 🙏

limiteinductive John6666 lunarflu djuna catwell


🙋🏻♂Hey there folks,

Did you see the new coding model from @01-ai ?

collection : https://huggingface.co/collections/01-ai/yi-coder-66bdb00f5bdd611f9a008f30
demo : https://huggingface.co/spaces/Tonic/Yi-Coder-9B

achieves SOTA on benchmarks , 125K context window , 55 languages including Docker, Js and many more 🚀


If you have documents that do not only have text and you're doing retrieval or RAG (using OCR and LLMs), give it up and give ColPali and vision language models a try 🤗

Why? Documents consist of multiple modalities: layout, table, text, chart, images. Document processing pipelines often consist of multiple models and they're immensely brittle and slow. 🥲

How? ColPali is a ColBERT-like document retrieval model built on PaliGemma, it operates over image patches directly, and indexing takes far less time with more accuracy. You can use it for retrieval, and if you want to do retrieval augmented generation, find the closest document, and do not process it, give it directly to a VLM like Qwen2-VL (as image input) and give your text query. 🤝

This is much faster + you do not lose out on any information + much easier to maintain too! 🥳

Multimodal RAG https://huggingface.co/collections/merve/multimodal-rag-66d97602e781122aae0a5139 💬
Document AI (made it way before, for folks who want structured input/output and can fine-tune a model) https://huggingface.co/collections/merve/awesome-document-ai-65ef1cdc2e97ef9cc85c898e 📖

adorkin web3builder John6666 Percifal jrmasiero rwightman seek007 abishekcodes zliu AI4Industry louisbrulenaudet byteprobe muhtasham rumbleFTW

umair894 abishekcodes fsommers jithinrocs rumbleFTW


🔥 Dataset Viber 0.3 launches with Synthesizer to synthesise data with a human in the loop, for free, using open source models with Argilla's distilabel but within a quick-and-easy Gradio Interface.

Why? Not trying to be all fancy and formal just to iterate on your data and to get familiar with your prompts and the produced data. Under the hood, it relies on Hugging Face Inference endpoints and the latest LLMs and VLMs like Meta Llama 3.1 and BlackForest Labs Flux models.

An addition to the other Interfaces that are already support.
- CollectorInterface: Lazily collect data of model interactions without human annotation.
- AnnotatorInterface: Walk through your data and annotate it with models in the loop.
- Synthesizer: Synthesize data with distilabel in the loop.
- BulkInterface: Explore your data distribution and annotate in bulk.

⭐ Give some good vibes: https://github.com/davidberenstein1957/dataset-viber


Datapluck: Portability Tool for Huggingface Datasets

"I found myself recently whipping up notebooks just to pull huggingface datasets locally, annotate or operate changes and update them again. This happened often enough that I made a cli tool out of it, which I've been using successfully for the last few months.

While huggingface uses open formats, I found the official toolchain relatively low-level and not adapted to quick operations such as what I am doing."
~ @omarkamali

Link : https://omarkama.li/blog/datapluck


Just wrapped up a deep dive into the latest lecture on building LLMs, such as ChatGPT, from @Stanford  CS229 course. Here are my top takeaways:

🔍 Understanding the Components: LLMs like ChatGPT, Claude, and others are more than just neural networks; they are a complex blend of architecture, training loss, data evaluation, and systems. Knowing how these components work together is key to improving and scaling these models.

📊 Scaling Matters: Performance improves predictably with more data, bigger models, and greater computational power. However, balancing these factors is crucial to avoid overfitting and resource waste.

📈 Data is King: LLMs are trained on trillions of tokens scraped from the internet, but the quality of this data matters immensely. Rigorous filtering and deduplication processes are essential to maintaining data integrity.

🏗 Pre-Training vs. Post-Training: While pre-training equips the model with general knowledge, post-training (like RLHF) fine-tunes it to follow human-like responses, reducing toxic outputs and improving alignment with human values.

🌐 Reinforcement Learning from Human Feedback (RLHF): This technique allows LLMs to maximize outputs that align with human preferences, making models more reliable and accurate.

💡 Why It Matters: Understanding these processes not only helps us appreciate the complexity behind our everyday AI tools but also highlights the challenges and opportunities in the ever-evolving field of AI.

Whether you’re in tech, data science, or just AI-curious, staying updated on these advancements is crucial. LLMs are not just transforming industries; they’re redefining the future of human-computer interaction!

I just realized this was almost 2 hours long...

Link: https://www.youtube.com/watch?v=9vM4p9NN0Ts


I just bought HF Pro but i don't know how many request per month i can get, if i request 1 time every 5s, around 2k token, is the pro account enough?, thanks for reading


My tool calling playgrounds repo has been updated again to include the use of flux1-schnell or dev image generation. This functionality is similar to using Dall-E 3 via the `@` decorator in ChatGPT. Once the function is selected, the model will either extract or improve your prompt (depending on how you ask).

I have also included 2 notebooks that cover different ways to access Flux for your specific use case. The first method covers how to access flux via LitServe from Lightning AI. LitServe is a bare-bones inference engine with a focus on modularity rather than raw performance. LitServe supports text generation models as well as image generation, which is great for some use cases, but does not provide the caching mechanisms from a dedicated image generation solution.

Since dedicated caching mechanisms are so crucial to performance, I also included an example for how to integrate SwarmUI/ComfyUI to utilize a more dedicated infrastructure that may already be running as part of your tech stack. Resulting in a Llama-3.1 capable of utilizing specific ComfyUI JSON configs, and many different settings.

Lastly, I tested the response times for each over a small batch request to simulate a speed test.

It becomes clear quickly how efficient caching mechanisms can greatly reduce the generation time, even in a scenario where another model is called. An average 4.5 second response time is not bad at all when you consider that an 8B model is calling a 12B parameter model for a secondary generation.

Repo: https://github.com/tdolan21/tool-calling-playground
LitServe: https://github.com/Lightning-AI/LitServe
SwarmUI: https://github.com/mcmonkeyprojects/SwarmUI


🥳  𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 𝗔𝗴𝗲𝗻𝘁𝘀 𝗻𝗼𝘄 𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀!

Multi-agent systems have been introduced in Microsoft's framework Autogen. It simply means having several agents working together to solve your task instead of only one : this paradigm empirically yields better performance on most benchmarks. The reason for this better performance is conceptually simple: for many tasks, rather than using a do-it-all system, you would prefer to specialize units on sub-tasks. Here, having agents with separate tool sets and memories allows to achieve efficient specialization.

You can now easily build hierarchical multi-agent systems with transformers.agents (not released yet, use the dev version)

To do so, encapsulate the agent in a ManagedAgent object. This object needs arguments agent, name, and a description, which will then be embedded in the manager agent's system prompt to let it know how to call this managed agent, as we also do for tools.

Cf the example in the image! We'll keep building on this paradigm in the upcoming weeks 🚀

Read more in the doc 👉 https://github.com/huggingface/transformers/blob/main/docs/source/en/agents_advanced.md

Checkout an advanced multi-agent system that tops the GAIA leaderboard 👉 https://github.com/aymeric-roucher/GAIA/blob/main/gaia_multiagent.py

ibrahim313 John6666 osanseviero Kaoeiri dsmonk Csplk KingNish whitebill Winnougan


the new version of Enigma, our code-instruct specialist, is out now:
- https://huggingface.co/ValiantLabs/Llama3.1-8B-Enigma is trained on code-instruct and general chat data.
- the updated code-instruct database is available now as well: https://huggingface.co/datasets/sequelbox/Tachibana

more to come soon!


🐣Ai2 Releasing OLMoE!
OLMoE-1B-7B-Instruct is a Mixture-of-Experts LLM with 1B active and 7B total parameters, and, OLMoE is 100% open-source in model, code-base, datasets!

🦖Paper: https://arxiv.org/abs/2409.02060

🤗Model: https://huggingface.co/allenai/OLMoE-1B-7B-0924-Instruct

💾Datasets: https://huggingface.co/datasets/allenai/OLMoE-mix-0924


If you want a clear understanding of the environmental impacts of AI throughout its entire lifecycle, this primer by @sasha @brunatrevelin and @meg is a must-read.

It brilliantly explains which types of impacts occur, when they happen, and why they matter.

https://huggingface.co/blog/sasha/ai-environment-primer

brunatrevelin John6666 not-lain BrigitteTousi louisbrulenaudet


The new Qwen-2 VL models seem to perform quite well in object detection. You can prompt them to respond with bounding boxes in a reference frame of 1k x 1k pixels and scale those boxes to the original image size.

You can try it out with my space https://huggingface.co/spaces/maxiw/Qwen2-VL-Detection

de-Rodrigo dsmonk tosouth hxypqr mrdbourke SvPolina akazakov Panerlu iiBLACKii


The MERIT Dataset: Modelling and Efficiently Rendering Interpretable
  Transcripts (2409.00447)

A few weeks ago, we uploaded the MERIT Dataset 🎒📃🏆 into Hugging Face 🤗!

Now, we are excited to share the Merit Dataset paper via arXiv! 📃💫
https://huggingface.co/papers/2409.00447

The MERIT Dataset is a fully synthetic, labeled dataset created for training and benchmarking LLMs on Visually Rich Document Understanding tasks. It is also designed to help detect biases and improve interpretability in LLMs, where we are actively working. 🔧🔨

MERIT contains synthetically rendered students' transcripts of records from different schools in English and Spanish. We plan to expand the dataset into different contexts (synth medical/insurance documents, synth IDS, etc.) Want to collaborate? Do you have any feedback? 🧐

Resources:

- Dataset: https://huggingface.co/datasets/de-Rodrigo/merit
- Code and generation pipeline: https://github.com/nachoDRT/MERIT-Dataset

PD: We are grateful to Hugging Face 🤗 for providing the fantastic tools and resources we find in the platform and, more specifically, to @nielsr for sharing the fine-tuning/inference scripts we have used in our benchmark.


@victor Sorry for the repetitiveness.

I'm not sure if Post is the right place to report such an error, but it seems to be a server error unrelated to the Zero GPU space error the other day, so I don't know where else to report it.

Since this morning, I have been getting a strange error when running inference from space in Gradio 3.x.
Yntec (https://huggingface.co/Yntec) discovered it, but he is not in the Pro subscription, so I am reporting it on behalf of him.

The error message is as follows: 1girl and other prompts will show cached output, so experiment with unusual prompts.

Thank you in advance.

https://huggingface.co/spaces/John6666/blitz_diffusion_error
https://huggingface.co/spaces/John6666/GPU-stresser-t2i-error
```
ValueError: Could not complete request to HuggingFace API, Status Code: 500, Error: unknown error, Warnings: ['CUDA out of memory. Tried to allocate 30.00 MiB (GPU 0; 14.75 GiB total capacity; 1.90 GiB already allocated; 3.06 MiB free; 1.95 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF', 'There was an inference error: CUDA out of memory. Tried to allocate 30.00 MiB (GPU 0; 14.75 GiB total capacity; 1.90 GiB already allocated; 3.06 MiB free; 1.95 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF']
```


I am integrating Azure Cosmos DB, the database system that backs GPT conversations into my workflow, and experimenting with new patterns to accelerate dataset evolution for evaluation and training of AI.

While initially using it for research prompts and research outputs using my GPT-4o client here which can interface and search ArXiv, I am excited to try out some new features specifically for AI at scale.  Research on memory augmentation is shown.  https://huggingface.co/spaces/awacke1/GPT-4o-omni-text-audio-image-video

  https://huggingface.co/spaces/awacke1/AzureCosmosDBUI


🚀 Introducing Hugging Face's Multilingual Speech-to-Speech! 🎤
💬Our modular, cross-platform pipeline to run GPT4o-like experiences on device can now seamlessly switch languages mid-conversation with an imperceptible 100ms delay.

🌟 Building on an amazing early reception with 2600 stars on GitHub 🌟
🚀 We are expanding the library to support multiple languages
🔥 Try it out with a flag: --language fr
🤯 Or don't set the flag and let the system detect the language

💡 What feature should we add next?

prithivMLmods osanseviero John6666 THEFIG


🧭 Guided Reasoning

👋Hi everyone,

We've been releasing Guided Reasoning:

Our AI guides walk your favorite LLM through complex reasoning problems.

🎯 Goals:

1⃣ Reliability. AIs consistently follow reasoning methods.
2⃣ Self-explainability. AIs see reasoning protocols and can explain internal deliberation.
3⃣ Contestability. Users may amend AI reasoning and revise plausibility assessments.

Try out Guided Reasoning with our light demo chatbot, powered by 🤗 HuggingFace's free Inference Api and small LLMs. (Sorry for poor latency and limited availability -- we are currently searching for 💸 compute sponsors to run more powerful models, faster, and optimize guided reasoning performance.)

Built on top of Logikon's open-source AI reasoning analytics.

Demo chat app: https://huggingface.co/spaces/logikon/benjamin-chat
Github: https://github.com/logikon-ai/logikon
Technical report: https://arxiv.org/abs/2408.16331

➡ Check it out and get involved! Looking forward to hearing from you.


 Fine-tuned Phi-3.5 Chatbot

This project presents a fine-tuned version of Microsoft's Phi-3.5 model, optimized for enhanced conversational abilities and general knowledge tasks.

Model Details
- Base model: microsoft/Phi-3.5-mini-instruct
- Fine-tuning method: PEFT (Parameter-Efficient Fine-Tuning)
- Training data: [Brief description of your dataset]

 Features
- Improved response generation for a wide range of topics
- Enhanced context understanding and coherence
- Optimized for deployment on Hugging Face Spaces

Usage
This model can be used for various natural language processing tasks, including:
- General conversation
- Question answering
- Task instructions
- Creative writing

Try out the model here :
  https://huggingface.co/spaces/sagar007/phi3.5_mini_instruct_finetune

Limitations
While this fine-tuned model shows improved performance, users should be aware of potential biases and limitations inherent in language models. Always critically evaluate the model's outputs.

 Feedback
I welcome any feedback, suggestions, or questions about this project. Feel free to open an issue or contribute to further improvements!

#Phi35 #FineTuning #NLP #MachineLearning #HuggingFace


Hyperfast Contextual Custom LLM with Agents, Multitokens, Explainable AI, and Distillation https://mltblog.com/4dNPSnB

New additions to this ground-breaking system include multi-token distillation when processing prompts, agents to meet user intent, more NLP, and a command prompt menu accepting both standard prompts and various actions.

I also added several illustrations, featuring xLLM in action with a full session and sample commands to fine-tune in real-time. All the code, input sources (anonymized corporate corpus from fortune 100 company), contextual backend tables including embeddings, are on GitHub. My system has zero weight, no transformer, and no neural network. It relies on explainable AI, does not require training, is fully reproducible, and fits in memory. Yet your prompts can retrieve relevant full text entities from the corpus with no latency — including URLs, categories, titles, email addresses, and so on — thanks to well-designed architecture.

Read more, get the code, paper and everything for free, at https://mltblog.com/4dNPSnB


𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

Human Feedback is not Gold Standard (2309.16349)

🚨 𝗛𝘂𝗺𝗮𝗻 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗳𝗼𝗿 𝗔𝗜 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴: 𝗡𝗼𝘁 𝘁𝗵𝗲 𝗴𝗼𝗹𝗱𝗲𝗻 𝗴𝗼𝗼𝘀𝗲 𝘄𝗲 𝘁𝗵𝗼𝘂𝗴𝗵𝘁?

I’ve just read a great paper where Cohere researchers raises significant questions about using Human feedback to evaluate AI language models.

Human feedback is often regarded as the gold standard for judging AI performance, but it turns out, it might be more like fool's gold : the study reveals that our human judgments are easily swayed by factors that have nothing to do with actual AI performance.

𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:
🧠 Test several models: Llama-2, Falcon-40B, Cohere Command 6 and 52B 🙅♂ Refusing to answer tanks AI ratings more than getting facts wrong. We apparently prefer a wrong answer to no answer!

💪 Confidence is key (even when it shouldn't be): More assertive AI responses are seen as more factual, even when they're not. This could be pushing AI development in the wrong direction, with systems like RLHF.

🎭 The assertiveness trap: As AI responses get more confident-sounding, non-expert annotators become less likely to notice when they're wrong or inconsistent.

And a consequence of the above:
🔄 𝗥𝗟𝗛𝗙 𝗺𝗶𝗴𝗵𝘁 𝗯𝗮𝗰𝗸𝗳𝗶𝗿𝗲: Using human feedback to train AI (Reinforcement Learning from Human Feedback) could accidentally make AI more overconfident and less accurate.

This paper means we need to think carefully about how we evaluate and train AI systems to ensure we're rewarding correctness over apparences of it like confident talk.

⛔ Chatbot Arena’s ELO leaderboard, based on crowdsourced answers from average joes like you and me, might become completely irrelevant as models will become smarter and smarter.

Read the paper 👉 https://huggingface.co/papers/2309.16349


Is AI’s impact on elections being overblown? Three researchers think so in this opinion piece published in the MIT Tech Review.

Highlights:

•	“AI is being used to try to influence electoral processes, but these efforts have not been fruitful.”
•	“Why were these initial speculations about AI-enabled electoral interference so off (…) ? The short answer: Because they ignored decades of research on the limited influence of mass persuasion campaigns, the complex determinants of voting behaviors, and the indirect and human-mediated causal role of technology.”
•	“Yet we should remember that there’s a cost to overreaction based on ill-founded assumptions, especially when other critical issues go unaddressed.”

👉Read more here: https://technologyreview.com/2024/09/03/1103464/ai-impact-elections-overblown/


The Forward-Forward Algorithm🤖

FFA replaces the forward and backward passes in backpropagtion  with two forward passes - one with positive (real) data and another with negative data. Each layer has its objective function - to increase or decrease a “goodness" metric. The positive pass uses real data and adjusts weights to increase “goodness” in every hidden layer. The negative pass does the opposite.

I must say reading&Implementing a godfather paper feels quite fulfilling:)
Thank you Prof. Geoffrey Hinton.

Code: https://github.com/Jaykef/ai-algorithms/blob/main/mnist_the_forward_forward_algorithm.ipynb


𝐌𝐲 𝐟𝐢𝐫𝐬𝐭 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 𝐚𝐫𝐭𝐢𝐜𝐥𝐞! 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐯𝐞 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐒𝐩𝐞𝐜𝐭𝐫𝐮𝐦 🎯

Full walkthrough on how to get started with Spectrum and TRL for efficient fine-tuning.
📔 👣 https://huggingface.co/blog/anakin87/spectrum

---

Looking to fine-tune Language Models efficiently and save on computational resources?

One popular method is QLoRa, which quantizes the original model and trains low-rank adapters on top.
It's quite effective and uses less GPU than full fine-tuning.

However, QLoRa applies Low-Rank Adaptation uniformly across the entire model.

What if we could identify the most informative layers and only fine-tune those? 🤔

This is exactly what Spectrum does! 👇

🔬 Spectrum analyzes the weight matrices for all layers in a Language Model and calculates a Signal to Noise Ratio (SNR) for each one.
(It uses Random Matrix Theory and Marchenko-Pastur distribution to distinguish signal from noise.)

🎯 Based on a chosen percentage (say, 25%), Spectrum selects the most informative layers of each type (mlp.down_proj, self_attn.o_proj, etc.).

You can then ❄ freeze the rest of the model and focus your 🏋♂ training on the chosen layers.

🏆 Results/Evaluation
- Spectrum is competitive with full fine-tuning and beats QLoRA on benchmarks.
- While QLoRA is more memory-efficient on a single GPU, Spectrum shines in distributed training setups.
- Great models trained with Spectrum: Dolphin models, Llama 3.1 Storm, numerous models by VAGO Solutions...

---

For a practical guide, check out the article above.


𝐍𝐞𝐰 𝐑𝐞𝐥𝐞𝐚𝐬𝐞: 𝐌𝐚𝐣𝐨𝐫 𝐓𝐎𝐌 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐄𝐥𝐞𝐯𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥 𝐄𝐱𝐩𝐚𝐧𝐬𝐢𝐨𝐧 🗺

Dataset: https://huggingface.co/datasets/Major-TOM/Core-DEM

Today with European Space Agency - ESA and Adobe Research, we release a global expansion to Major TOM with GLO-30 DEM data.

You can now instantly access nearly 2M of Major TOM samples with elevation data to build your next AI model for EO. 🌍

🔍 Browse the data in our usual viewer app: https://huggingface.co/spaces/Major-TOM/MajorTOM-Core-Viewer

Fantastic work championed by Paul Borne--Pons @NewtNewt  🚀

Tonic AtAndDev NewtNewt osanseviero Obenlia robmarkcole


# Excited to Share: New LLM Tokenization - Convert Text to tokens and vice versa! 🚀

I've just developed a powerful tool for anyone working with Language Models (LLMs) or diving into Natural Language Processing (NLP).

🔍 Introducing the LLM Tokenization - Convert Text to tokens and vice versa!!

Key Features:
- Convert text to tokens and token IDs
- Reverse engineer: convert token IDs back to text
- Support for popular models: LLama3 (Will add more models iteratively)
- User-friendly Gradio interface for easy interaction

Whether you're debugging your NLP pipeline, exploring how different models tokenize text, or just curious about the inner workings of LLMs, this tool is for you!

👩💻 Tech Stack:
- Python
- Gradio for the web interface
- Hugging Face Transformers for tokenization

The application is deployed in Hugging Face spaces as Gradio application

🔗 Try it out: https://lnkd.in/g6R5z9k2

#NLP #MachineLearning #AI #PythonDevelopment #OpenSource


I started training a public LoRA style (2 seperate training each on 4x A6000).

Experimenting captions vs non-captions. So we will see which yields best results for style training on FLUX.

Generated captions with multi-GPU batch Joycaption app.

I am showing 5 examples of what Joycaption generates on FLUX dev. Left images are the original style images from the dataset.

I used my multi-GPU Joycaption APP (used 8x A6000 for ultra fast captioning) : https://www.patreon.com/posts/110613301

I used my Gradio batch caption editor to edit some words and add activation token as ohwx 3d render : https://www.patreon.com/posts/108992085

The no caption dataset uses only ohwx 3d render as caption

I am using my newest 4x_GPU_Rank_1_SLOW_Better_Quality.json on 4X A6000 GPU and train 500 epochs — 114 images : https://www.patreon.com/posts/110879657

Total step count is being 500 * 114 / 4 (4x GPU — batch size 1) = 14250

Taking 37 hours currently if I don’t terminate early

Will save a checkpoint once every 25 epochs

Full Windows Kohya LoRA training tutorial : https://youtu.be/nySGu12Y05k

Full cloud tutorial I am still editing

Hopefully will share trained LoRA on Hugging Face and CivitAI along with full dataset including captions.

I got permission to share dataset but can’t be used commercially.

Also I will hopefully share full workflow in the CivitAI and Hugging Face LoRA pages.


Spent a few minutes to build an alternative to Character AI on top of  llama3.1 405B through SambaNova's super fast inference API

Space: https://huggingface.co/spaces/kz919/Persona-AI
API referral link: https://sambanova.ai/fast-api?api_ref=907266


ML people on a long flight

(See picture)


🙋🏻♂hey there folks ,

✒InkubaLM has been trained from scratch using 1.9 billion tokens of data for five African languages, along with English and French data, totaling 2.4 billion tokens of data. It is capable of understanding and generating content in five African languages: Swahili, Yoruba, Hausa, isiZulu, and isiXhosa, as well as English and French.

model https://huggingface.co/lelapa/InkubaLM-0.4B
demo https://huggingface.co/spaces/Tonic/Inkuba-0.4B

monsoon-nlp AtAndDev KingNish louisbrulenaudet d0rj Moio


Do you know how PCA and SVD are related?
I explained it for everyone in this post!
Go and check it out: https://x.com/alexbodner_/status/1798357519678718062?s=46


Hey everyone 🤗!
Check out this awesome new model for object segmentation!
https://huggingface.co/spaces/finegrain/finegrain-object-cutter.

We (finegrain) have trained this new model in partnership with Nfinite and some of their synthetic data, the resulting model is incredibly accurate 🚀.
It’s all open source under the MIT license (https://huggingface.co/finegrain/finegrain-box-segmenter), complete with a test set tailored for e-commerce (https://huggingface.co/datasets/finegrain/finegrain-product-masks-lite). Have fun experimenting with it!

limiteinductive Sri-Vigneshwar-DJ John6666 fdaudens TDL123 1aurent deltheil piercus Mefistofele osanseviero vincentweisser victor dsmonk


𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

The AI Scientist: Towards Fully Automated Open-Ended Scientific
  Discovery (2408.06292)

🤖 𝗧𝗵𝗲 𝗔𝗜 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁: 𝗔𝗴𝗲𝗻𝘁𝗶𝗰, 𝗳𝘂𝗹𝗹𝘆-𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗳𝗼𝗿 𝘂𝗻𝗱𝗲𝗿 $𝟭𝟱 𝗽𝗲𝗿 𝗽𝗮𝗽𝗲𝗿

Researchers have just created an AI system that 𝗰𝗮𝗻 𝗰𝗼𝗻𝗱𝘂𝗰𝘁 𝗲𝗻𝘁𝗶𝗿𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗿𝗼𝗺 𝘀𝘁𝗮𝗿𝘁 𝘁𝗼 𝗳𝗶𝗻𝗶𝘀𝗵, 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹𝗹𝘆 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗵𝗼𝘄 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗶𝗲𝘀 𝗮𝗿𝗲 𝗺𝗮𝗱𝗲.

It doesn't just assist with specific tasks - it automates the entire research process, from generating ideas to writing and reviewing papers.
1 - brainstorm novel research directions, 2- write and execute code for experiments & visualize results, get references, and even 3- write up findings in a full academic paper format!

And it can do all this for under $15 per paper! 🤯

𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:
🧠 Generates novel research ideas across multiple topics (e.g. diffusion modeling, transformers, learning dynamics aka “grokking”)
👨💻 Uses open-source coding assistant Aider to implement ideas and run experiments. This is especially important since this agentic assistant can iterate if it fails somewhere.
📊 Visualizes results and plans follow-up experiments (up to 5 rounds)
✍ Writes full academic papers, including finding references using Semantic Search API
🕵 Runs a simulated peer review process to evaluate paper quality
💰 Total cost per paper is under $15. This system can generate "hundreds of interesting, medium-quality papers" in just a week !

𝗦𝘁𝗶𝗹𝗹 𝗻𝗼𝘁 𝗿𝗲𝗮𝗱𝘆 𝘁𝗼 𝗳𝗶𝗹𝗹 𝗜𝗖𝗟𝗥 𝘄𝗶𝘁𝗵 𝗽𝗮𝗽𝗲𝗿𝘀:
🔁 Ideas generated in one domain tend to be repetitive across different runs, and even different language model
👀 Does not use vision capabilities to fix visual issues in plots
💭 Models occasionally hallucinate entire results tables
⇒ Only few of the generated papers would actually meet the threshold for acceptance at a top AI conference

👉 Read their paper: https://huggingface.co/papers/2408.06292

wsuff Sri-Vigneshwar-DJ John6666 Bruhn sugatoray KingNish louisbrulenaudet toshihikochen alielfilali01


Plugins in NiansuhAI

Plugin Names:
1. WebSearch: Searches the web using search engines.
2. Calculator: Evaluates mathematical expressions, extending the base Tool class.
3. WebBrowser: Extracts and summarizes information from web pages.
4. Wikipedia: Retrieves information from Wikipedia using its API.
5. Arxiv: Searches and fetches article information from Arxiv.
6. WolframAlphaTool: Provides answers on math, science, technology, culture, society, and everyday life.

These plugins currently support the GPT-4O-2024-08-06 model, which also supports image analysis.

Try it now: https://huggingface.co/spaces/NiansuhAI/chat

Similar to: https://hf.co/chat

John6666 TheDrunkenSnail leeloolee Sri-Vigneshwar-DJ Joseph717171 Niansuh


🤩 Amazing day. AWPortrait-FL finally here!
🦖 AWPortrait-FL is finetuned on FLUX.1-dev using the training set of AWPortrait-XL and nearly 2,000 fashion photography photos with extremely high aesthetic quality.

🤗Model: https://huggingface.co/Shakker-Labs/AWPortrait-FL

🙇Demo: https://huggingface.co/spaces/vilarin/flux-labs

orrinin samapika dillfrescott John6666 wanghaofan ijohn07 Bruhn ajibawa-2023 gshreyash tanfar lunarflu louisbrulenaudet ShakkerAi-Labs linoyts Despina KingNish victor sasikiran Pranavan nbroad Sri-Vigneshwar-DJ privategeek24 traltyaziking TDL123 koochikoo25 Taylor658 AtAndDev huangy1 tayyabmehar27 Mefistofele ibrahim313 awplanet


Understanding the json format response with HF's Serverless Inference API 🤗

As it stands, there seems to be an inconsistency with the OpenAI documentation on the question of implementing the JSON response format using the InferenceClient completion API.

After investigating the InferenceClient source code, I share the official solution using a JSON Schema. This consolidates the structure of the response and simplifies parsing as part of an automated process for extracting metadata, information:
```python
from huggingface_hub import InferenceClient

client = InferenceClient("meta-llama/Meta-Llama-3-70B-Instruct")

messages = [
    {
        "role": "user",
        "content": "I saw a puppy a cat and a raccoon during my bike ride in the park. What did I saw and when?",
    },
]

response_format = {
    "type": "json",
    "value": {
        "properties": {
            "location": {"type": "string"},
            "activity": {"type": "string"},
            "animals_seen": {"type": "integer", "minimum": 1, "maximum": 5},
            "animals": {"type": "array", "items": {"type": "string"}},
        },
        "required": ["location", "activity", "animals_seen", "animals"],
    },
}

response = client.chat_completion(
    messages=messages,
    response_format=response_format,
    max_tokens=500,
)

print(response.choices[0].message.content)
```
As a reminder, json mode is activated with the OpenAI client as follows:
```python
response = client.chat.completions.create(
     model="gpt-3.5-turbo-0125",
     messages=[...],
     response_format={"type": "json_object"}
)
```
One question remains unanswered, however, and will perhaps be answered by the community: it seems that an incompatibility persists for list of dictionaries generation, and currently, the production of simple dictionaries seems to be the only functional option.

John6666 victor wsuff rreed-pha osanseviero


I am training a controlnet model for Flux. And some of my experiences:

Checkpoint-10000:

https://x.com/kadirnar_ai/status/1829831750471606668

Checkpoint-12000:

https://x.com/kadirnar_ai/status/1829889524962640001

Checkpoint-14000:

https://x.com/kadirnar_ai/status/1829989622878744711

Checkpoint (16000-18000):

https://x.com/kadirnar_ai/status/1830179551407665654

Dataset:  https://huggingface.co/datasets/kadirnar/fluxdev_controlnet_16k
GPU: 1xA100(80GB)
GPU Hours: 65

tolgacangoz John6666 gokaygokay Shinku AtAndDev xziayro Saugatkafley l3x13

tolgacangoz louisbrulenaudet gokaygokay Pranavan AtAndDev xziayro osanseviero

tolgacangoz gokaygokay AtAndDev Sri-Vigneshwar-DJ


Last Week in Medical AI: Top Research Papers/Models
🏅 (August 25 - August 31, 2024)

- MultiMed: Multimodal Medical Benchmark
- A Foundation model for generating chest X-ray images
- MEDSAGE: Medical Dialogue Summarization
-  Knowledge Graphs for Radiology Report Generation
- Exploring Multi-modal LLMs for Chest X-ray
- Improving Clinical Note Generation
...

Check the full thread : https://x.com/OpenlifesciAI/status/1829984701324448051

aaditya jaebumskiyomi aiisthebest ai-everyday adityaSaligram dblasko victor JCDentonInTheFresh

aaditya John6666 jaebumskiyomi aiisthebest JCDentonInTheFresh


💾🧠How much VRAM will you need for training your AI model? 💾🧠
Check out this app where you convert:
Pytorch/tensorflow summary -> needed VRAM
or
Parameter count -> needed VRAM

Use it in: http://howmuchvram.com

And everything is open source! Ask for new functionalities or contribute in:
https://github.com/AlexBodner/How_Much_VRAM
If it's useful to you leave a star 🌟and share it to someone that will find the tool useful!

rmayormartins AlexBodner whitebill erkhem-gantulga AtAndDev


From Article 50 of the EU AI Act:

"2. Providers of AI systems, including general-purpose AI systems, generating synthetic audio, image, video or text content, shall ensure that the outputs of the AI system are marked in a machine-readable format and detectable as artificially generated or manipulated."

How might this be put into practice?

I'm interested to know how content might be deemed as being "detectable" as artificially generated. I wonder if this will require an image be detectable as AI generated if it was copied out of the site / application it was created on?

Some sort of a watermark? LSB Stegranography? I wonder if openAI are already sneaking something like this into DALL-E images.

Some sort of hash, which allowing content to be looked up, and verified as AI generated?

Would a pop up saying "this output was generated with AI"? suffice? Any ideas? Time is on the system provider's side, at least for now, as from what I can see this doesn't come into effect until August 2026.

src: https://artificialintelligenceact.eu/article/50/


I found this paper to be thought-provoking: "Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling" by Bansal, Hosseini, Agarwal,  Tran, and Kazemi.
https://arxiv.org/abs/2408.16737
The direct implication is that smaller models could be used to create cost-effective synthetic datasets. And on that note, in the Gemma terms of use, Google explicitly claims no rights on outputs generated from those models, which means one is free to synthgen from the Gemma line. Meta's Llama 3 licence forbids synthetic generation of outputs if used to improve other models. Relevant Mistral, Qwen, and Yi models under the Apache 2.0 license are unrestricted for this purpose.

John6666 anakin87 AtAndDev tachyon-beep tommulder victor gghfez louisbrulenaudet djuna


Just tried LitServe from the good folks at @LightningAI!

Between llama.cpp and vLLM, there is a small gap where a few large models are not deployable!

That's where LitServe comes in!

LitServe is a high-throughput serving engine for AI models built on FastAPI.

Yes, built on FastAPI. That's where the advantage and the issue lie.

It's extremely flexible and supports multi-modality and a variety of models out of the box.

But in my testing, it lags far behind in speed compared to vLLM.

Also, no OpenAI API-compatible endpoint is available as of now.

But as we move to multi-modal models and agents, this serves as a good starting point. However, it’s got to become faster...

GitHub: https://github.com/Lightning-AI/LitServe


🙋🏻♂Hey there folks,

just published a demo for Salesforce's new Function Calling Model `Salesforce/xLAM`

- https://huggingface.co/spaces/Tonic/Salesforce-Xlam-7b-r
- https://huggingface.co/spaces/Tonic/On-Device-Function-Calling

just try em out, and it comes with `on-device`version too ! cool ! 🚀


new synthetic general chat dataset! meet Supernova, a dataset using prompts from UltraFeedback and responses from Llama 3.1 405b Instruct:  https://huggingface.co/datasets/sequelbox/Supernova

new model(s) using the Supernova dataset will follow next week, along with Other Things. (One of these will be a newly updated version of Enigma, utilizing the next version of https://huggingface.co/datasets/sequelbox/Tachibana with approximately 2x the rows!)


Very excited to have made the list and been invited to OpenAI DevDay 2024 at the London event 30 October! Looking forward to seeing what the future of AI dev holds, connecting with other professionals in the field, and advocating for open source AI!

https://openai.com/devday/


💾🧠Want to know how much VRAM you will need for training your model? 💾🧠
Now you can use this app in which you can input a torch/tensorflow summary or the parameters count and get an estimate of the required memory!
Use it in: howmuchvram.com

Also, everything is Open Source so you can contribute in repo: https://github.com/AlexBodner/How_Much_VRAM
Leave it a star⭐

Mehyaar MexIvanov Bruhn den0620 AtAndDev


Shakker-Labs brings an amazing LoRA trained on FLUX.1-dev for blended realistic illustration by Muertu 😍 the front character is in illustration style, while the background is realistic. 🤩

🤙Model: https://huggingface.co/Shakker-Labs/FLUX.1-dev-LoRA-blended-realistic-illustration

🙇♂My space for demo: https://huggingface.co/spaces/vilarin/flux-lab-light

orrinin YaTharThShaRma999 John6666 chris-fung djuna alielfilali01 ngxson Felladrin wanghaofan AtAndDev keakohv louisbrulenaudet