Less than two days ago Kyutai Labs open sourced Moshi - an ~7.6B on-device Speech to Speech foundation model and Mimi - SoTA streaming speech codec! 🔥

The release includes:

1. Moshiko & Moshika - Moshi finetuned on synthetic data (CC-BY license) (https://huggingface.co/collections/kyutai/moshi-v01-release-66eaeaf3302bef6bd9ad7acd)
2. Mimi - Streaiming Audio Codec, processes 24 kHz audio, down to a 12.5 Hz representation with a bandwidth of 1.1 kbps (CC-BY license) (https://huggingface.co/kyutai/mimi)
3. Model checkpoints & Inference codebase written in Rust (Candle), PyTorch & MLX (Apache license) (https://github.com/kyutai-labs/moshi)

How does Moshi work?

1. Moshi processes two audio streams: one for itself and one for the user, with the user's stream coming from audio input and Moshi's stream generated by the model.

2. Along with these audio streams, Moshi predicts text tokens for its speech, enhancing its generation quality.

3. The model uses a small Depth Transformer for codebook dependencies and a large 7B parameter Temporal Transformer for temporal dependencies.

4. The theoretical latency is 160ms, with a practical latency of around 200ms on an L4 GPU.

Model size & inference:

Moshiko/ka are 7.69B param models

bf16 ~16GB VRAM
8-bit ~8GB VRAM
4-bit ~4GB VRAM

You can run inference via Candle 🦀, PyTorch and MLX - based on your hardware.

The Kyutai team,  @adefossez @lmz  and team are cracked AF, they're bringing some serious firepower to the open source/ science AI scene, looking forward to what's next! 🐐

lmz AtAndDev damerajee den0620 adorkin Forbu14 abdullah Nelathan osanseviero Dunateo bitsoko

MohamedRashad John6666 fdaudens AtAndDev damerajee bitsoko


Check out the new Structured #Wikipedia dataset by Wikimedia Enterprise: abstract, infobox, structured sections, main image,...

Currently in early beta (English & French). Explore it and give feedback: https://huggingface.co/datasets/wikimedia/structured-wikipedia

More info: https://enterprise.wikimedia.com/blog/hugging-face-dataset/
@sdelbecque @resquito-wmf

resquito-wmf davanstrien not-lain ayymen


🚀 Revolutionary Method to Convert YOLOv8 to On-Device AI with mobile NPU utilizations

 Attention AI developers and engineers!
Discover how ZETIC.MLange can effortlessly transform YOLOv8 models into on-device AI with mobile NPU utilizations. 🎉

💡 We highlight the power of mobile NPU, showing how it outperforms CPU in processing speed. The results speak for themselves—NPU-driven execution is faster, smarter, and more efficient.
* Real-time demos are no easy feat but with ZETIC.MLange, we’ve made it possible!

🎥 Watch the video to see how we’re revolutionizing on-device AI.
: https://youtu.be/LkP3JDTcVN8?si=6Ha5vHA-G7jZE9oq

🌟 Our team has successfully implemented YOLOv8 as on-device AI using ZETIC.MLange. This innovative approach enables high-performance object detection across various manufacturers mobile devices.

🔍 Curious about the details? We've shared a comprehensive guide on our blog. Check it out through the link below!
📚 Blog link: https://zetic.ai/blog/implementing-yolov8-on-device-ai-with-zetic-mlange

yeonseok-zeticai JJen889 mmoazzamaliashraf jeremy-london SicarioOtsutsuki osanseviero

JJen889 yeonseok-zeticai mmoazzamaliashraf jeremy-london mmx31 osanseviero

JJen889 yeonseok-zeticai John6666 bluemix jeremy-london osanseviero

JJen889 yeonseok-zeticai jeremy-london osanseviero


InfiMM-WebMath-40B: Advancing Multimodal Pre-Training for Enhanced
  Mathematical Reasoning (2409.12568)

🚀 Excited to announce the release of InfiMM-WebMath-40B — the largest open-source multimodal pretraining dataset designed to advance mathematical reasoning in AI! 🧮✨

With 40 billion tokens, this dataset aims for enhancing the reasoning capabilities of multimodal large language models in the domain of mathematics.

If you're interested in MLLMs, AI, and math reasoning, check out our work and dataset:

🤗 HF: https://huggingface.co/papers/2409.12568
📂 Dataset: https://huggingface.co/datasets/Infi-MM/InfiMM-WebMath-40B


Single Block / Layer FLUX LoRA Training Research Results and LoRA Network Alpha Change Impact With LoRA Network Rank Dimension

Full article posted here : https://medium.com/@furkangozukara/single-block-layer-flux-lora-training-research-results-and-lora-network-alpha-change-impact-with-e713cc89c567

Conclusions
As expected, as you train lesse parameters e.g. LoRA vs Full Fine Tuning or Single Blocks LoRA vs all Blocks LoRA, your quality get reduced
Of course you earn some extra VRAM memory reduction and also some reduced size on the disk
Moreover, lesser parameters reduces the overfitting and realism of the FLUX model, so if you are into stylized outputs like comic, it may work better
Furthermore, when you reduce LoRA Network Rank, keep original Network Alpha unless you are going to do a new Learning Rate research
Finally, very best and least overfitting is achieved with full Fine Tuning
Check figure 3 and figure 4 last columns — I make extracted LoRA Strength / Weight 1.1 instead of 1.0
Full fine tuning configs and instructions > https://www.patreon.com/posts/112099700
Second best one is extracting a LoRA from Fine Tuned model if you need a LoRA
Check figure 3 and figure 4 last columns — I make extracted LoRA Strength / Weight 1.1 instead of 1.0
Extract LoRA guide (public article) : https://www.patreon.com/posts/112335162
Third is doing a all layers regular LoRA training
Full guide, configs and instructions > https://www.patreon.com/posts/110879657
And the worst quality is training lesser blocks / layers with LoRA
Full configs are included in > https://www.patreon.com/posts/110879657
So how much VRAM and Speed single block LoRA training brings?
All layers 16 bit is 27700 MB (4.85 second / it) and 1 single block is 25800 MB (3.7 second / it)
All layers 8 bit is 17250 MB (4.85 second / it) and 1 single block is 15700 MB (3.8 second / it)
Image Raw Links
Figure 0 : https://huggingface.co/MonsterMMORPG/FLUX-Fine-Tuning-Grid-Tests/resolve/main/Figure_0.jfif


@JonSteinworth please acces https://huggingface.co/datasets/shainar/BEAD

All these data pieces are there


Backlink Profile Concept: "A clean, modern vector illustration showing a website surrounded by various interconnected links representing a backlink profile, with arrows pointing to it from other authority websites."


today's release: the updated Supernova general chat dataset!

- the new Supernova is 2x the rows, continuing to provide high quality general synthetic data generated with Llama 405b Instruct.

Find it at https://huggingface.co/datasets/sequelbox/Supernova

Enjoy! There's also a new version of https://huggingface.co/sequelbox/Llama3.1-8B-MOTH available using the new dataset. (new and better MOTHs for other models will come as well, but the Build Tools and Shining Valiant take priority.)


🎓Introducing ruschatgpt Q&A Dataset - https://huggingface.co/datasets/nyuuzyou/ruschatgpt-qa

Dataset highlights:
- 190,281 question-answer pairs from ruschatgpt.ru, a Russian question-answering website
- Monolingual content in Russian
- Each entry includes: URL, question, and response
- Data reflects user-generated questions and language model-generated answers
- Licensed under Creative Commons Zero (CC0) for unrestricted use

The dataset can be used for analyzing trends in AI-powered question answering in Russia. It's also valuable for examining Russian language patterns and topic distributions in user queries and AI responses.


Good folks from VILA Lab at Mohamed bin Zayed University of AI have introduced 26 guiding principles for optimizing prompts when interacting with large language models (LLMs) like LLaMA and GPT.

These principles aim to enhance LLM response quality, accuracy, and task alignment across various scales of models.

1. Be direct and concise, avoiding unnecessary politeness.
2. Specify the intended audience.
3. Break complex tasks into simpler steps.
4. Use affirmative directives instead of negative language.
5. Request explanations in simple terms for clarity.
6. Mention a potential reward for better solutions.
7. Provide examples to guide responses.
8. Use consistent formatting and structure.
9. Clearly state tasks and requirements.
10. Mention potential penalties for incorrect responses.
11. Request natural, human-like answers.
12. Encourage step-by-step thinking.
13. Ask for unbiased responses without stereotypes.
14. Allow the model to ask clarifying questions.
15. Request explanations with self-tests.
16. Assign specific roles to the model.
17. Use delimiters to separate sections.
18. Repeat key words or phrases for emphasis.
19. Combine chain-of-thought with few-shot prompts.
20. Use output primers to guide responses.
21. Request detailed responses on specific topics.
22. Specify how to revise or improve text.
23. Provide instructions for generating multi-file code.
24. Give specific starting points for text generation.
25. Clearly state content requirements and guidelines.
26. Request responses similar to provided examples.

Results show significant improvements in both "boosting" (response quality enhancement) and "correctness" across different model scales. Using the ATLAS benchmark, specialized prompts improved response quality and accuracy by an average of 57.7% and 67.3%, respectively, when applied to GPT-4.


Inference Endpoints got a bunch of cool updates yesterday, this is my top 3


🚀 Excited to share the latest update to the Notebook Creator Tool!

Now with basic fine-tuning support using Supervised Fine-Tuning! 🎯

How it works:
1⃣ Choose your Hugging Face dataset and notebook type (SFT)
2⃣ Automatically generate your training notebook
3⃣ Start fine-tuning with your data!

Link to the app 👉 https://lnkd.in/e_3nmWrB
💡 Want to contribute with new notebooks? 👉https://lnkd.in/eWcZ92dS


What are your favorite text chunkers/splitters?
Mine are:
- https://github.com/benbrandt/text-splitter (Rust/Python, battle-tested, Wasm version coming soon)
- https://github.com/umarbutler/semchunk (Python, really performant but some issues with huge docs)

I tried the huge Jina AI regex, but it failed for my (admittedly messy) documents, e.g. from EUR-LEX. Their free segmenter API is really cool but unfortunately times out on my huge docs (~100 pages): https://jina.ai/segmenter/

Also, I tried to write a Vanilla JS chunker with a simple, adjustable hierarchical logic (inspired from the above). I think it does a decent job for the few lines of code: https://do-me.github.io/js-text-chunker/

Happy to hear your thoughts!


🚀 Your AI toolkit just got a major upgrade! I updated the Journalists on Hugging Face community's collection with tools for investigative work, content creation, and data analysis.

Sharing these new additions with the links in case it’s helpful:
- @wendys-llc's excellent 6-part video series on AI for investigative journalism https://www.youtube.com/playlist?list=PLewNEVDy7gq1_GPUaL0OQ31QsiHP5ncAQ
- @jeremycaplan's curated AI Spaces on HF https://wondertools.substack.com/p/huggingface
- @Xenova's Whisper Timestamped (with diarization!) for private, on-device transcription https://huggingface.co/spaces/Xenova/whisper-speaker-diarization & https://huggingface.co/spaces/Xenova/whisper-word-level-timestamps
- Flux models for image gen & LoRAs https://huggingface.co/spaces/autotrain-projects/train-flux-lora-ease
- FineGrain's object cutter https://huggingface.co/spaces/finegrain/finegrain-object-cutter and object eraser (this one's cool) https://huggingface.co/spaces/finegrain/finegrain-object-eraser
- FineVideo: massive open-source annotated dataset + explorer https://huggingface.co/spaces/HuggingFaceFV/FineVideo-Explorer
- Qwen2 chat demos, including 2.5 & multimodal versions (crushing it on handwriting recognition) https://huggingface.co/spaces/Qwen/Qwen2.5 & https://huggingface.co/spaces/Qwen/Qwen2-VL
- GOT-OCR integration https://huggingface.co/spaces/stepfun-ai/GOT_official_online_demo
- HTML to Markdown converter https://huggingface.co/spaces/maxiw/HTML-to-Markdown
- Text-to-SQL query tool by @davidberenstein1957 for HF datasets https://huggingface.co/spaces/davidberenstein1957/text-to-sql-hub-datasets

There's a lot of potential here for journalism and beyond. Give these a try and let me know what you build!

You can also add your favorite ones if you're part of the community!

Check it out: https://huggingface.co/JournalistsonHF

#AIforJournalism #HuggingFace #OpenSourceAI


This issue is just a treasure ! A bit deprecated i guess, but things are in their historical context. (personally, still need more to understand better)
https://github.com/huggingface/transformers/issues/8771
🫡 to the man @stas


🎉SetFit v1.1.0 is out! Training efficient classifiers on CPU or GPU now uses the Sentence Transformers Trainer, and we resolved a lot of issues caused by updates of third-party libraries (like Transformers). Details:

Training a SetFit classifier model consists of 2 phases:
1. Finetuning a Sentence Transformer embedding model
2. Training a Classifier to map embeddings -> classes

🔌The first phase now uses the SentenceTransformerTrainer that was introduced in the Sentence Transformers v3 update. This brings some immediate upsides like MultiGPU support, without any (intended) breaking changes.

➡ Beyond that, we softly deprecated the "evaluation_strategy" argument in favor of "eval_strategy" (following a Transformers deprecation), and deprecated Python 3.7. In return, we add official support for Python 3.11 and 3.12.

✨ There's some more minor changes too, like max_steps and eval_max_steps now being a hard limit instead of an approximate one, training/validation losses now logging nicely in Notebooks, and the "device" parameter no longer being ignored in some situations.

Check out the full release notes here: https://github.com/huggingface/setfit/releases/tag/v1.1.0
Or read the documentation: https://huggingface.co/docs/setfit
Or check out the public SetFit models for inspiration: https://huggingface.co/models?library=setfit&sort=created

P.s. the model in the code snippet trained in 1 minute and it can classify ~6000 sentences per second on my GPU.

lbourdois trollek AtAndDev privategeek24 Bruhn osanseviero louisbrulenaudet


Why would you fine-tune a model if you can just prompt an LLM? The new paper "What is the Role of Small Models in the LLM Era: A Survey" provides a nice pro/con overview. My go-to approach combines both:

1. Start testing an idea by prompting an LLM/VLM behind an API. It's fast and easy and I avoid wasting time on tuning a model on a task that might not make it into production anyways.

2. The LLM/VLM then needs to be manually validated. Anyone seriously considering putting AI into production has to do at least some manual validation. Setting up a good validation pipeline with a tool like Argilla is crucial and it can be reused for any future experiments. Note: you can use LLM-as-a-judge to automate some evals, but you always also need to validate the judge!

3. Based on this validation I can then (a) either just continue using the prompted LLM if it is accurate enough and it makes sense financially given my load; or (b) if the LLM is not accurate enough or too expensive to run in the long-run, I reuse the existing validation pipeline to annotate some additional data for fine-tuning a smaller model. This can be sped up by reusing & correcting synthetic data from the LLM (or just pure distillation).

Paper: https://arxiv.org/pdf/2409.06857
Argilla docs: https://docs.argilla.io/latest/
Argilla is also very easy to deploy with Hugging Face Spaces (or locally): https://huggingface.co/new-space?template=argilla%2Fargilla-template-space

nicoberk Tonic John6666 oliverguhr umarbutler


𝐊𝐞𝐲 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬:

𝐊𝐞𝐲 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬:

🔥 𝐐𝐰𝐞𝐧 𝐫𝐞𝐥𝐞𝐚𝐬𝐞𝐬 𝐭𝐡𝐞𝐢𝐫 𝟐.𝟓 𝐟𝐚𝐦𝐢𝐥𝐲 𝐨𝐟 𝐦𝐨𝐝𝐞𝐥𝐬: 𝐍𝐞𝐰 𝐒𝐎𝐓𝐀 𝐟𝐨𝐫 𝐚𝐥𝐥 𝐬𝐢𝐳𝐞𝐬 𝐮𝐩 𝐭𝐨 𝟕𝟐𝐁!

The Chinese LLM maker just dropped a flurry of different models, ensuring there will be a Qwen SOTA model for every application out there:
Qwen2.5: 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B
Qwen2.5-Coder: 1.5B, 7B, and 32B on the way
Qwen2.5-Math: 1.5B, 7B, and 72B.

And they didn't sleep: the performance is top of the game for each weight category!

𝐊𝐞𝐲 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬:

🌐 All models have 𝟭𝟮𝟴𝗸 𝘁𝗼𝗸𝗲𝗻 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗹𝗲𝗻𝗴𝘁𝗵

📚 Models pre-trained on 18T tokens, even longer than the 15T of Llama-3

💪 The flagship 𝗤𝘄𝗲𝗻𝟮.𝟱-𝟳𝟮𝗕 𝗶𝘀 ~𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝘄𝗶𝘁𝗵 𝗟𝗹𝗮𝗺𝗮-𝟯.𝟭-𝟰𝟬𝟱𝗕, 𝗮𝗻𝗱 𝗵𝗮𝘀 𝗮 𝟯-𝟱% 𝗺𝗮𝗿𝗴𝗶𝗻 𝗼𝗻 𝗟𝗹𝗮𝗺𝗮-𝟯.𝟭-𝟳𝟬𝗕 𝗼𝗻 𝗺𝗼𝘀𝘁 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀.

🇫🇷 On top of this, it 𝘁𝗮𝗸𝗲𝘀 𝘁𝗵𝗲 #𝟭 𝘀𝗽𝗼𝘁 𝗼𝗻 𝗺𝘂𝗹𝘁𝗶𝗹𝗶𝗻𝗴𝘂𝗮𝗹 𝘁𝗮𝘀𝗸𝘀 so it might become my standard for French

💻 Qwen2.5-Coder is only 7B but beats competing models up to 33B (DeeSeek-Coder 33B-Instruct). Let's wait for their 32B to come out!

🧮 Qwen2.5-Math sets a new high in the ratio of MATH benchmark score to # of parameters. They trained it by "aggregating more high-quality mathematical data, particularly in Chinese, from web sources, books, and codes across multiple recall cycles."

📄 Technical report to be released "very soon"

🔓 All models have the most permissive license apache2.0, except the 72B models that have a custom license mentioning "you can use it for free EXCEPT if your product has over 100M users"

🤗 All models are available on the HF Hub! ➡ https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e

alielfilali01 ajibawa-2023 louisbrulenaudet codito yeonseok-zeticai KingNish John6666 Hassanain0108 Wok santiagokomadina DataSoul v000000 jeffboudier snoopynoob adibvafa DiamanteAmarelo nbroad mihaylovnikitos osanseviero

John6666 DiamanteAmarelo Svngoku trollek osanseviero


How to Extract LoRA from FLUX Fine Tuning / DreamBooth Training Full Tutorial and Comparison Between Fine Tuning vs Extraction vs LoRA Training

Full article is here public post : https://www.patreon.com/posts/112335162

This was short on length so check out the full article - public post

Conclusions as below

Conclusions
With same training dataset (15 images used), same number of steps (all compared trainings are 150 epoch thus 2250 steps), almost same training duration, Fine Tuning / DreamBooth training of FLUX yields the very best results

So yes Fine Tuning is the much better than LoRA training itself

Amazing resemblance, quality with least amount of overfitting issue

Moreover, extracting a LoRA from Fine Tuned full checkpoint, yields way better results from LoRA training itself

Extracting LoRA from full trained checkpoints were yielding way better results in SD 1.5 and SDXL as well

Comparison of these 3 is made in Image 5 (check very top of the images to see)

640 Network Dimension (Rank) FP16 LoRA takes 6.1 GB disk space

You can also try 128 Network Dimension (Rank) FP16 and different LoRA strengths during inference to make it closer to Fine Tuned model

Moreover, you can try Resize LoRA feature of Kohya GUI but hopefully it will be my another research and article later

Image Raw Links
Image 1 : https://huggingface.co/MonsterMMORPG/FLUX-Fine-Tuning-Grid-Tests/resolve/main/Image_1.png

Image 2 : https://huggingface.co/MonsterMMORPG/FLUX-Fine-Tuning-Grid-Tests/resolve/main/Image_2.jfif

Image 3 : https://huggingface.co/MonsterMMORPG/FLUX-Fine-Tuning-Grid-Tests/resolve/main/Image_3.jfif

Image 4 : https://huggingface.co/MonsterMMORPG/FLUX-Fine-Tuning-Grid-Tests/resolve/main/Image_4.jfif

Image 5 : https://huggingface.co/MonsterMMORPG/FLUX-Fine-Tuning-Grid-Tests/resolve/main/Image_5.jpg

MonsterMMORPG sasikiran DarkMoonDragon Vigilence


🎓Introducing чатгпт-в-россии.рф (meaning in English would be something like chatgpt-in-russia[.]rf)  Q&A Dataset - https://huggingface.co/datasets/nyuuzyou/chatgpt-in-russia-qa

Dataset highlights:
- 628,186 question-answer pairs from чатгпт-в-россии.рф, a Russian question-answering website
- Monolingual content in Russian
- Each entry includes: URL, question, and response
- Data reflects user-generated questions and language model-generated answers
- Licensed under Creative Commons Zero (CC0) for unrestricted use

The dataset can be used for the purpose of analyzing trends in the use of AI to answer questions in Russia. Additionally, it can be useful for examining language patterns and topic distributions.


I am experimenting with the Flux-Realism and Flux-Anime LoRA models, using the Flux.1-dev & schnell models as the base. The desired results improve significantly as the image lengths increase.  🎈

The demo for the respective trials is :\
-   https://huggingface.co/spaces/prithivMLmods/FLUX-REALISM
-   https://huggingface.co/spaces/prithivMLmods/FLUX-ANIME

Model :\
-   https://huggingface.co/prithivMLmods/Canopus-LoRA-Flux-FaceRealism
-   https://huggingface.co/prithivMLmods/Canopus-LoRA-Flux-Anime

Dataset:\
-   https://huggingface.co/datasets/prithivMLmods/Canopus-Realism-Minimalist
-   https://4kwallpapers.com

KingNish nezubn louisbrulenaudet saturn-1223 prithivMLmods Ngrthm arwaava18


✨ 𝗧𝗼𝗽 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:

✨ 𝗧𝗼𝗽 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:

🚀 Exciting News! 🚀

We've just released 𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎_𝚑𝚞𝚋 v0.25.0 and it's packed with powerful new features and improvements!

✨ 𝗧𝗼𝗽 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:

• 📁 𝗨𝗽𝗹𝗼𝗮𝗱 𝗹𝗮𝗿𝗴𝗲 𝗳𝗼𝗹𝗱𝗲𝗿𝘀 with ease using `huggingface-cli upload-large-folder`. Designed for your massive models and datasets. Much recommended if you struggle to upload your Llama 70B fine-tuned model 🤡
• 🔎 𝗦𝗲𝗮𝗿𝗰𝗵 𝗔𝗣𝗜: new search filters (gated status, inference status) and fetch trending score.
• ⚡𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝗖𝗹𝗶𝗲𝗻𝘁: major improvements simplifying chat completions and handling async tasks better.

We’ve also introduced tons of bug fixes and quality-of-life improvements - thanks to the awesome contributions from our community! 💪

💡 Check out the release notes: https://huggingface.co/spaces/Wauplin/huggingface_hub/discussions/8

Want to try it out? Install the release with:

`pip install huggingface_hub==0.25.0`

victor KingNish celinah pierrci qgallouedec julien-c gabrielmbmb Rybens Taylor658 jeffboudier not-lain Chunte TheDrunkenSnail John6666 maywell oza75 yungwarlock Tanvir1337

Felladrin pierrci julien-c whitebill Wizknight John6666 not-lain Chunte Tanvir1337


📢 2024 CVPR Videos Are Now Available! 🎥

CVPR conference keynotes, panels, posters, workshops, and other content are now available.

⬇
https://cvpr.thecvf.com/Conferences/2024/Videos


My way of understanding of AI:
Artificial Intelligence is a concept developed by human intelligence, where systems are designed to simulate human-like thinking, analysis, understanding, and creation, often performing tasks faster and more efficiently than humans.

Add your thoughts...


💬 Chat as a way to query SQL! The Airtrain AI team is happy to share a new Hugging Face Space that lets you interact with Hugging Face Hub datasets using a natural language chatbot. 🤗

Start Exploring 👉 https://huggingface.co/spaces/airtrain-ai/hf-dataset-chat-to-sql

This Space is forked from https://huggingface.co/spaces/davidberenstein1957/text-to-sql-hub-datasets by @davidberenstein1957 and features chat capability with improved table naming. The tool works with Hugging Face’s recently released in-browser DuckDB-based SQL query engine for datasets.

idrissairtrain davidberenstein1957 josh-sematic John6666 djuna emmanuel-turlay victor Tonic Manel-Hik

idrissairtrain davidberenstein1957 josh-sematic emmanuel-turlay Tonic


🙋🏻♂Hey there folks,

Nvidia just released a small 4B Nemotron-mini model , and it works surprisingly well !

you can check it out here :

base : https://huggingface.co/nvidia/Minitron-4B-Base
instruct : https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct
demo : https://huggingface.co/spaces/Tonic/Nemotron-Mini-4B

hoep you like it 🤗🤗


i need someone who is well versed with 3rd party api building to work on something on my trading software


OpenAI's latest model, "o1", has demonstrated remarkable performance on the Norway Mensa IQ test, scoring an estimated IQ of 120.

Everyone should think before answering!

Key findings:

• o1 correctly answered 25 out of 35 IQ questions, surpassing average human performance
• The model excelled at pattern recognition and logical reasoning tasks
• Performance was validated on both public and private test sets to rule out training data bias

Technical details:

• o1 utilizes advanced natural language processing and visual reasoning capabilities
• The model likely employs transformer architecture with billions of parameters
• Improved few-shot learning allows o1 to tackle novel problem types

Implications:

• This represents a significant leap in AI reasoning abilities
• We may see AIs surpassing 140 IQ by 2026 if the trend continues
• Raises important questions about the nature of intelligence and cognition


Announce 🎉 WebApp1K-Duo 🎉
https://huggingface.co/datasets/onekq-ai/WebApp1K-Duo-React

This is to keep up the challenge after OpenAI o1 models saturated the WebApp1K benchmark. The new benchmark brings SOTA to 67%. Let the hill climbing commence!
https://huggingface.co/spaces/onekq-ai/WebApp1K-models-leaderboard

PS: I will publish more findings soon.


I need to use the model "Mistral-7B-Instruct-v0.2", and I purchased the Pro option ($9/month), but I still can't use it. What could be the problem, and what should I do? Please advise.


Mistral Nemo is better than many models in 1st grader level reasoning.

ijohn07 legolasyiu onekq clementtest John6666 prithivMLmods edwixx SvCy


@VincentBerthier

Bonjour Vincent,

pouvez-vous valider l'ouverture d'un compte pro me concernant afin de pouvoir accéder à Spinoza ? Mon identifiant est AREAZ-8.

Bien à vous


🎵 Introducing Tamago Music Dataset - https://huggingface.co/datasets/nyuuzyou/tamago

A collection of 1,567 music tracks featuring:

- Complete metadata with audio files and cover artwork
- Rich track information including titles, descriptions, and genres
- User engagement metrics like play counts and reactions
- English language content from independent artists
- Released under Creative Commons Zero (CC0) license

Dataset structure includes:
- Track metadata (titles, descriptions, genres, tags)
- Associated media (audio files, cover images)
- Artist information and engagement metrics

Particularly valuable for:
- Music generation model training
- Cross-modal analysis
- Audio classification tasks
- Music style and genre analysis


🏄♂While browsing new models, I stumbled upon Lumiere from aixonlab. After testing it, I feel it has considerable potential. Keep up the good work!

Lumiere Alpha is a model focusing on improving realism without compromising prompt coherency or changing the composition completely from the original Flux.1-Dev model.

🦄 Model: https://huggingface.co/aixonlab/flux.1-lumiere-alpha

🦖 Demo: https://huggingface.co/spaces/vilarin/lumiere


if I host in hf spaces, can I interact with the app using an API?


🍅 Glif App's Remixes feature allows you to slap a logo onto anything, seamlessly integrating the input image (logo) into various contexts. The result is stunning remixes that blend the input logo with generated images (img2img logo mapping) for incredible outcomes.

Check out Any Logo Anywhere remixes on Glif: [Glif Remixes](https://glif.app/glifs/cm3o7dfsd002610z48sz89yih/remixes)

🌐The browser extension enables thousands of Glif-based img2img workflows on any image you find online. Experience Glif Remix with WebAI: [Chrome Extension](https://chromewebstore.google.com/detail/glif-remix-the-web-with-a/abfbooehhdjcgmbmcpkcebcmpfnlingo)

.
.
.
🤗Have fun with the cool stuff !!
@prithivMLmods

Ngrthm RenderIo dvilasuero darksfx hypergod ai4life44 rdrede Mrdesigner14 BrigitteTousi victor

RenderIo dvilasuero ai4life44 Mrdesigner14 BrigitteTousi Ngrthm

darksfx rdrede Mrdesigner14 BrigitteTousi Ngrthm RenderIo


🔍 Meta teams use a fine-tuned Llama model to fix production issues in seconds

One of Meta's engineering teams shared how they use a fine-tuned small Llama (Llama-2-7B, so not even a very recent model) to identify the root cause of production issues with 42% accuracy.

🤔 42%, is that not too low?
➡ Usually, whenever there's an issue in production, engineers dive into recent code changes to find the offending commit. At Meta's scale (thousands of daily changes), this is like finding a needle in a haystack.
💡 So when the LLM-based suggestion is right, it cuts incident resolution time from hours to seconds!

How did they do it?

🔄 Two-step approach:
‣ Heuristics (code ownership, directory structure, runtime graphs) reduce thousands of potential changes to a manageable set
‣ Fine-tuned Llama 2 7B ranks the most likely culprits

🎓 Training pipeline:
‣ Continued pre-training on Meta's internal docs and wikis
‣ Supervised fine-tuning on past incident investigations
‣ Training data mimicked real-world constraints (2-20 potential changes per incident)

🔮 Now future developments await:
‣ Language models could handle more of the incident response workflow (runbooks, mitigation, post-mortems)
‣ Improvements in model reasoning should boost accuracy further

Read it in full 👉 https://www.tryparity.com/blog/how-meta-uses-llms-to-improve-incident-response


🇯🇵 The Open Japanese LLM Leaderboard created by LLM-jp 🌸 in partnership with HuggingFace 🤗 was released today!

Blog: https://huggingface.co/blog/leaderboard-japanese
Space: https://huggingface.co/spaces/llm-jp/open-japanese-llm-leaderboard

🌍 The leaderboard is available in both Japanese and English
📚 Based on the evaluation tool, llm-jp-eval with more than 20 datasets for Japanese LLMs
📊 The leaderboard showcases all the metrics for NLP experts, plus averages for NLP beginners
💻 For the comfort of users, we chose a horizontal UI, and implemented it in a light and dark theme on Gradio
🔬 The radar chart provides a very interesting visualization of metrics!
🌱 We are using the Japanese research platform, MDX, so please be patient!
⚡ LLMs bigger than +70B will be evaluated soon…

How do you say “GPUs Go Brrr” in Japanese - > GPUがブンブン～! (To pronounce "GPU ga  bunbun!") 🔥

John6666 kaisugi victor AdinaY ArthurZ hysts fuzzy-mittenz


Great to see Tatta Bio release an embeddings version of their DNA/protein language model 🧬: https://huggingface.co/tattabio/gLM2_650M_embed

alielfilali01 victor SlemanS AtAndDev BrigitteTousi clem


Sorry judge, my lawyer hallucinated? 😂 If you get an AI lawyer, you would want it to be hallucination-free!

New @Stanford-@Yale research reveals surprising findings about leading AI legal research tools. Here's what you need to know:

>> Key Findings
The study tested LexisNexis (Lexis+ AI), Thomson Reuters (Westlaw AI & Ask Practical Law AI), and GPT-4, finding hallucination rates between 17-33% despite claims of being "hallucination-free".

>> Technical Deep Dive
The research evaluated these tools using Retrieval-Augmented Generation (RAG) architecture, which operates in two crucial steps:

1. Retrieval System:
- Uses neural text embeddings to capture semantic meaning
- Employs both lexical and semantic search mechanisms
- Implements document filtering and extraction
- Retrieves relevant legal documents from vast databases

2. Generation Pipeline:
- Processes retrieved documents alongside original queries
- Synthesizes information from multiple legal sources
- Generates responses based on retrieved context
- Includes citation verification mechanisms

>> Performance Breakdown:
- Lexis+ AI: 65% accuracy rate
- Westlaw AI: 42% accuracy rate
- Ask Practical Law AI: Over 60% incomplete answers

>> Why This Matters
This research exposes critical vulnerabilities in AI legal tools that lawyers increasingly rely on. It's essential for legal professionals to understand these limitations when incorporating AI into their practice.

John6666 AtAndDev dvilasuero davanstrien


That Chip Has Sailed: A Critique of Unfounded Skepticism Around AI for Chip Design

https://www.aimodels.fyi/papers/arxiv/that-chip-has-sailed-critique-unfounded-skepticism

* Research paper critiques skepticism around AI for chip design
* Addresses reproduction errors in previous work by Cheng et al.
* Defends effectiveness of AI/ML approaches in integrated circuit design
* Identifies specific methodological flaws in critique paper
* Emphasizes importance of proper implementation in ML research


My new favorite bookmark: AnyChat. The ultimate AI Swiss Army knife that lets you switch between ChatGPT, Gemini, Claude, LLaMA, Grok & more—all in one place!

Really cool work by @akhaliq

https://huggingface.co/spaces/akhaliq/anychat


Bringing Open-Source Text-to-Speech to French! 🗣🇫🇷

Hugging Face's Parler TTS mini can now speak French! 🇫🇷🎉
You can try it here: https://huggingface.co/spaces/PHBJT/french_parler_tts

Key highlights:
Transform the English TTS model to speak French 🇬🇧➡🇫🇷
Fully open source (code, weights, and datasets) 🛠
It can be replicated for every language 🌍

Read more about it in this article: https://huggingface.co/blog/PHBJT/french-parler-tts

Special thanks to FlexAI and their dedicated team for providing the computing power that made this possible and of course to all of the Parler TTS community 🤗


𝗧𝗵𝗲 𝗗𝗦𝗕𝗲𝗻𝗰𝗵 𝗱𝗮𝘁𝗮𝘀𝗲𝘁

𝗧𝗵𝗲 𝗗𝗦𝗕𝗲𝗻𝗰𝗵 𝗱𝗮𝘁𝗮𝘀𝗲𝘁

𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀

𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀

DSBench: How Far Are Data Science Agents to Becoming Data Science
  Experts? (2409.07703)

𝗔𝗿𝗲 𝗔𝗴𝗲𝗻𝘁𝘀 𝗰𝗮𝗽𝗮𝗯𝗹𝗲 𝗲𝗻𝗼𝘂𝗴𝗵 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲? ⇒ 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝘁𝗵𝗲𝗶𝗿 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝗗𝗦𝗕𝗲𝗻𝗰𝗵 📊

A team from Tencent AI wanted to evaluate agentic systems on data science (DS) tasks : but they noticed that existing agentic benchmarks were severely limited in several aspects: they were limited to text and did not include tables or images, were only specific to certain packages, only performed exact match evaluation…

➡ So they set out to build a much more exhaustive approach, to finally make the definitive DS agent benchmark.

𝗧𝗵𝗲 𝗗𝗦𝗕𝗲𝗻𝗰𝗵 𝗱𝗮𝘁𝗮𝘀𝗲𝘁
▪DS bench has 466 data analysis tasks and 74 data modelling tasks
▪The tasks are sourced from ModelOff and Kaggle, the platforms hosting the most popular data science competitions
▪Difference with previous DS benchmarks:
 ❶ This benchmark leverages various modalities on top of text: images, Excel files, tables
 ❷ Complex tables: sometimes several tables should be leveraged to answer one question
 ❸ The context is richer, with longer descriptions.
▪ Evaluation metrics : the benchmark is scored with an LLM as a judge, using a specific prompt.

𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀
▪ Their evaluation confirms that using LLMs in an agent setup, for instance by allowing them to run a single step of code execution, is more costly (especially with multi-turn frameworks like autogen) but also much more performant than the vanilla LLM.
▪ The sets of tasks solved by different models (like GPT-3.5 vs Llama-3-8B) has quite low overlap, which suggests that different models tend to try very different approches.

This new benchmark is really welcome, can't wait to try transformers agents on it! 🤗

Read their full paper 👉 https://huggingface.co/papers/2409.07703


Just released: our newest version of Shining Valiant, powered by an all-new science-instruct dataset!

The model: https://huggingface.co/ValiantLabs/Llama3.1-8B-ShiningValiant2
The dataset: https://huggingface.co/datasets/sequelbox/Celestia

tell your friends! for everyone to use.

(more releases coming later this week! including an expansion of the https://huggingface.co/datasets/sequelbox/Supernova generalist dataset.)


Pro Tip - if you're a Firefox user, you can set up Hugging Chat as integrated AI Assistant, with contextual links to summarize or simplify any text - handy!

In this short video I show how to set it up

ibrahim313 qamarsidd Violette louisbrulenaudet gabrielmbmb wsuff TraineeR den0620 DIvAndrey SamC824 adorkin KingNish Amirin2them kramp MoritzLaurer victor RobertRoss Salvor


🧶 We are launching distilabel DataCraft: get started with synthetic data using clicks and natural language!

🌊 Workflow
- Write down your custom GenAI usecase
- Automatically generate system prompts
- Create sample datasets for quick iteration
- Produce full-scale datasets with customizable parameters
- Push generated datasets directly to the Hugging Face Hub

⚡ Powered by Argilla's distilabel and open source LLMs
🆓 Uses Free Serverless HF Inference Endpoints

💡 Use Cases:
- Fine-tuning language models for specific domains
- Creating diverse datasets for robust model training
- Rapid prototyping of AI applications
- Generating synthetic data for privacy-sensitive projects

🚀 Start crafting your custom datasets today and do it quicker, easier and more private with distilabel DataCraft!
https://huggingface.co/spaces/argilla/distilabel-datacraft


Full Fine Tuning of FLUX yields way better results than LoRA training as expected, overfitting and bleeding reduced a lot

Configs and Full Experiments
Full configs and grid files shared here : https://www.patreon.com/posts/kohya-flux-fine-112099700

Details
I am still rigorously testing different hyperparameters and comparing impact of each one to find the best workflow
So far done 16 different full trainings and completing 8 more at the moment
I am using my poor overfit 15 images dataset for experimentation (4th image)
I have already proven that when I use a better dataset it becomes many times betters and generate expressions perfectly
Here example case : https://www.reddit.com/r/FluxAI/comments/1ffz9uc/tried_expressions_with_flux_lora_training_with_my/
Conclusions
When the results are analyzed, Fine Tuning is way lesser overfit and more generalized and better quality
In first 2 images, it is able to change hair color and add beard much better, means lesser overfit
In the third image, you will notice that the armor is much better, thus lesser overfit
I noticed that the environment and clothings are much lesser overfit and better quality
Disadvantages
Kohya still doesn’t have FP8 training, thus 24 GB GPUs gets a huge speed drop
Moreover, 48 GB GPUs has to use Fused Back Pass optimization, thus have some speed drop
16 GB GPUs gets way more aggressive speed drop due to lack of FP8
Clip-L and T5 trainings still not supported
Speeds
Rank 1 Fast Config — uses 27.5 GB VRAM, 6.28 second / it (LoRA is 4.85 second / it)
Rank 1 Slower Config — uses 23.1 GB VRAM, 14.12 second / it (LoRA is 4.85 second / it)
Rank 1 Slowest Config — uses 15.5 GB VRAM, 39 second / it (LoRA is 6.05 second / it)
Final Info
Saved checkpoints are FP16 and thus 23.8 GB (no Clip-L or T5 trained)
According to the Kohya, applied optimizations doesn’t change quality so all configs are ranked as Rank 1 at the moment
I am still testing whether these optimizations make any impact on quality or not

MonsterMMORPG runebloodstone KingNish Will114 antoinejeannot Sergidev nbroad eugenesiow


🙋🏻♂Hey there folks ,

@ucaslcl released a new OCR model , that's👏🏻👏🏻 fantastic : https://huggingface.co/ucaslcl/GOT-OCR2_0

GPU : https://huggingface.co/spaces/Tonic/GOT-OCR
Gradio Demo (Image Edit) : https://huggingface.co/spaces/Tonic1/ImageEdit-GOT-OCR

Model : https://huggingface.co/ucaslcl/GOT-OCR2_0
Official demo : https://huggingface.co/spaces/ucaslcl/GOT_online
github : https://github.com/Ucas-HaoranWei/GOT-OCR2.0

victor ucaslcl HaoranWei John6666 xi0v Tonic takarajordan wsuff ajibawa-2023 Wok


Have you tried the new SQL Console yet?

Would love to know any queries you've tried or general feedback! If you haven't go try it out and let us know 🤗

If you have some interesting queries feel free to share the URLs as well!

adamelliotfields pcuenq AndreyBest prithivMLmods


https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1
MN-12B-Mag-Mell is a multi-stage merge, inspired by hypermerges like Tiefighter and Umbral Mind, intended for use as a general-purpose "Best of Nemo" model for co-writing, roleplay, and text adventures.

Consistently, Mag Mell produced prose that shocked testers, with a minimum of "slop". It also exhibited a unique sense of humor, and a propensity for inserting bespoke details into adventuring scenarios.


💾🧠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


🐋 DeepSeek 🐋2.5 is hands-down the best open-source model, leaving its peers way behind. It even beats GPT-4o mini.

https://huggingface.co/spaces/onekq-ai/WebApp1K-models-leaderboard

The inference of the official API is painfully slow though. I heard the team is short on GPUs (well, who isn't).


If you are interested in adversarial machine learning  and AI safety, find my curated reading list below:

GitHub: https://github.com/EzgiKorkmaz/adversarial-reinforcement-learning

John6666 monsoon-nlp runebloodstone rreed-pha


nanoGPT with Sigmoid Self-Attention
I couldn’t resist had to give it a try:)

Some observations on M2:
SSA was ~5-10% faster in training with similar final loss values, slightly less coherent text generation, marginally higher perplexity, and lower memory usage compared to softmax.

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

John6666 prince-canuma KingNish louisbrulenaudet


Researchers from Tencent have developed DepthCrafter, a novel method for generating temporally consistent long depth sequences for open-world videos using video diffusion models.

It leverages a pre-trained image-to-video diffusion model (SVD) as the foundation and uses a 3-stage training strategy on paired video-depth datasets:
1. Train on a large realistic dataset (1-25 frames)
2. Fine-tune temporal layers on realistic data (1-110 frames)
3. Fine-tune spatial layers on synthetic data (45 frames)

It adapts SVD's conditioning mechanism for frame-by-frame video input and employs latent diffusion in VAE space for efficiency.
Sprinkle some intelligent inference strategy for extremely long videos:
- Segment-wise processing (up to 110 frames)
- Noise initialization to anchor depth distributions
- Latent interpolation for seamless stitching

And outperforms SOTA methods on multiple datasets (Sintel, ScanNet, KITTI, Bonn).

Read here: https://depthcrafter.github.io


Trained Myself With 256 Images on FLUX — Results Mind Blowing

Detailed Full Workflow

Medium article : https://medium.com/@furkangozukara/ultimate-flux-lora-training-tutorial-windows-and-cloud-deployment-abb72f21cbf8

Windows main tutorial : https://youtu.be/nySGu12Y05k

Cloud tutorial for GPU poor or scaling : https://youtu.be/-uhL2nW7Ddw

Full detailed results and conclusions : https://www.patreon.com/posts/111891669

Full config files and details to train : https://www.patreon.com/posts/110879657

SUPIR Upscaling (default settings are now perfect) : https://youtu.be/OYxVEvDf284

I used my Poco X6 Camera phone and solo taken images

My dataset is far from being ready, thus I have used so many repeating and almost same images, but this was rather experimental

Hopefully I will continue taking more shots and improve dataset and reduce size in future

I trained Clip-L and T5-XXL Text Encoders as well

Since there was too much push from community that my workflow won’t work with expressions, I had to take a break from research and use whatever I have

I used my own researched workflow for training with Kohya GUI and also my own self developed SUPIR app batch upscaling with face upscaling and auto LLaVA captioning improvement

Download images to see them in full size, the last provided grid is 50% downscaled

Workflow

Gather a dataset that has expressions and perspectives that you like after training, this is crucial, whatever you add, it can generate perfect

Follow one of the LoRA training tutorials / guides

After training your LoRA, use your favorite UI to generate images

I prefer SwarmUI and here used prompts (you can add specific expressions to prompts) including face inpainting :

https://gist.github.com/FurkanGozukara/ce72861e52806c5ea4e8b9c7f4409672

After generating images, use SUPIR to upscale 2x with maximum resemblance

Short Conclusions

Using 256 images certainly caused more overfitting than necessary

...

MonsterMMORPG Kukedlc nvhf beratcmn AIGUYCONTENT kosalvann AeonFutura josephcidc victor mtasic85 JaayTee BartekG

MonsterMMORPG nickandbro tail-call boapps

MonsterMMORPG Ashtonhashemi Obenlia wisam84


Last Week in Medical AI: Top Research Papers/Models
🏅(September 7  - September 14, 2024)

🏅 Medical AI Paper of the week
Chai-1 Foundation model molecular structure prediction

Medical LLMs & Benchmarks
- BrainWave: A Brain Signal Foundation Model
- DS-ViT: Vision Transformer for Alzheimer’s Diagnosis
- EyeCLIP: Visual–language model for ophthalmic
- Segment Anything Model for Tumor Segmentation
- MEDIC: Evaluating LLMs in Clinical Applications

Medical LLM Applications
- KARGEN: Radiology Report Generation LLMs
- DrugAgent: Explainable Drug Repurposing Agents
- Improving RAG in Medicine with Follow-up Questions

Frameworks and Methodologies
- Infrastructure for Automatic Cell Segmentation
- Data Alignment for Dermatology AI
- Diagnostic Reasoning in Natural Language
- Two-Stage Instruction Fine-tuning Approach for Med

AI in Healthcare Ethics
- Concerns and Choices of Using LLMs for Healthcare
- Understanding Fairness in Recommender Systems
- Towards Fairer Health Recommendations

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 🤗

aaditya onekq cchristophe sasikiran blanchon Chunte shetumohanto ajibawa-2023


Anybody ever play Final Fantasy: Crystal Chronicles?
Like, *really* play it?

Mag Mell has been in my head recently. What a place that was.

Those cocoons looked like I could lay down inside of one, and it would be the most powerful sleep of a lifetime, with dreams that would last one thousand years, and I'd wake up with the wisdom of generations.

...Hey, anybody like text adventures?


For all the Muslims out there who are interested in Quran and its tafsir (explanations). This humble dataset consists of 84 different books of tafsir for nearly all the ayat in the Quran:
https://huggingface.co/datasets/MohamedRashad/Quran-Tafseer

I hope it helps someone to build something nice and useful with it ^_^


Are the servers down or what ? Am i the only one experiencing this error :
```
HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/...../)

Internal Error - We're working hard to fix this as soon as possible!
```


WHY ARE THERE NOT TEXT FEWSHOT DATASETS @ HUGGINGFACE? 😲


If you're passionate about the latest in AI, self-driving technology, and humanoid robotics, you need to catch this episode featuring Andrej Karpathy, he discusses OpenAI, Tesla, and education. It's 44 minutes, but you might have to slow it down based on how fast he speaks!

Key Insights:

1. Self-Driving Cars as a Bridge to AGI:
Andrej explores the parallels between self-driving technology and Artificial General Intelligence (AGI), suggesting that in some respects, AGI has already been achieved within the realm of self-driving. Tesla’s approach, which emphasizes software over expensive hardware like LIDAR, exemplifies this.

2. Tesla vs. Waymo: The Battle of Approaches:
Tesla relies on vision-based systems with minimal sensors, leveraging advanced neural networks for decision-making. This contrasts sharply with Waymo's sensor-heavy vehicles, highlighting a broader software versus hardware challenge that could define the future of scalable autonomous driving.

3. End-to-End Deep Learning:
Andrej highlights the transition from manually programmed systems to fully end-to-end deep learning models that "eat through the stack." At Tesla, this shift has significantly reduced reliance on C++ code, making neural networks the driving force in software and hardware integration.

4. Humanoid Robotics - More Than Just a Dream:
The shift from Tesla’s automotive neural networks to humanoid robots like Optimus is nearly seamless. By using the same sensors and computational platforms, Tesla is redefining what a robotics company can achieve at scale, bridging the gap between vehicle AI and human-like robotics.

And...

5. The Power of Transformers in AI

6. Synthetic Data: The Future of AI Training

7. AI for Education - A Revolutionary Approach

Full super fast speech is here: https://youtu.be/hM_h0UA7upI


I am experimenting with Flux and trying to push it to its limits without training (as I am GPU-poor 😅).
I found some flaws in the pipelines, which I resolved, and now I am able to generate an approx similar quality image as Flux Schnell 4 steps in just 1 step.
Demo Link:
https://huggingface.co/spaces/KingNish/Realtime-FLUX


Fast inference is no longer a nice-to-have demo; it will be the driving force behind future frontier models. Time to switch over to custom AI hardware and short Nvidia.

Try out SambaNova's lightning fast API for free at https://sambanova.ai/fast-api?api_ref=444868


If your plan keeps changing it's a sign that you are living the moment.

I just got the pass@1 result of GPT 🍓o1-preview🍓 : 0.95!!!

This means my benchmark is cast into oblivion, I need to up the ante. I am all ears to suggestions. https://huggingface.co/spaces/onekq-ai/WebApp1K-models-leaderboard


I made Multi-agent Software Team Gradio space  using transformers agents based on the multiagent_web_assistant cookbook by @m-ric

https://huggingface.co/spaces/Csplk/SoftwareTeam


Convolutional KAN - Feedback before send to a journal +700⭐

Repository: https://github.com/AntonioTepsich/Convolutional-KANs

After publishing the Convolutional Kolmogorov-Arnold Networks, we were mentioned by the IEEE Spectrum, congratulated by the universities of Stanford, NorthWestern, Cornell, among others. Surpassing the +700⭐ and the paper was cited more than 12 times. We were presented with the opportunity to submit our idea to a journal to be formalised.

We know that CKANs have their problems due to the amount of computational power and ram memory it uses, but based on what we have what should we do to get a Q1 in a journal?

Paper: https://arxiv.org/pdf/2406.13155


Hello everybody,

We've rolled out a major update to ZeroGPU! All the Spaces are now running on it.

Major improvements:

1. GPU cold starts about twice as fast!
2. RAM usage reduced by two-thirds, allowing more effective resource usage, meaning more GPUs for the community!
3. ZeroGPU initializations (coldstarts) can now be tracked and displayed (use `progress=gr.Progress(track_tqdm=True)`)
4. Improved compatibility and PyTorch integration, increasing ZeroGPU compatible spaces without requiring any modifications!

Feel free to answer in the post if you have any questions

🤗 Best regards,
Charles

louisbrulenaudet kz919 maxiw AtAndDev thanhkt Azure99 DiamanteAmarelo Rybens John6666 Tanvir1337 YaTharThShaRma999 KingNish Nymbo rreed-pha clem not-lain RalFinger gbharti sourceoftruthdata enzostvs asigalov61 Norway28 frankrobotics alielfilali01 Kukedlc Corvius djuna Aurelien-Morgan Kaballas


The breakthrough in OpenAI’s release goes way beyond just another family of capable models - it’s a monumental leap in LLM reasoning capabilities. One in which the limitations in pre-training become obsolete and the dream of scaling during inference becomes a reality.

Once again reinforcement learning (when rightly done) proves to be the ultimate “tool” that drives reasoning in AI models. OpenAI o1 (aka strawberry 🍓) can think and learn while thinking before giving a response. This is how we humans approach solving difficult problems.

In technical terms, o1 is trained with an RL algorithm to think productively using its chain of thought. In other words “the longer it thinks, the better it does on reasoning tasks”. Similar to how AlphaGo was able to beat the world champion at Go.

Read more: https://openai.com/index/learning-to-reason-with-llms/

julesbsz John6666 Sri-Vigneshwar-DJ ffffandqj AtAndDev


I’ve built a simple Room Cleaner app to remove clutter from messy room.
Try the Space here: https://huggingface.co/spaces/blanchon/room_cleaner

victor VivekRastogi blanchon mfahadkhan josephcidc


I was reading through an abstract and found myself wondering how much LLM performance is being left on the table due to insufficient curation of training datasets: "Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning" by Kaur, Park, Goyal, Arora.
https://arxiv.org/abs/2408.14774
In particular,  the observation that "Introducing low quality answers ("shirkers") in 20% of Instruct-SkillMix examples causes performance to plummet..." had me wondering how many ostensibly good datasets out there are in fact populated with a significant number of "shirkers".


We used Hugging Face Trainer to fine-tune Deberta-v3-base for Personally Identifiable Information detection, achieving 99.44% overall accuracy (98.27% Recall for PII detection).

Please try our model (Colab Quickstart available) and let us know what you think:
https://huggingface.co/iiiorg/piiranha-v1-detect-personal-information

John6666 osanseviero blanchon thesven AtAndDev

blanchon cschroeder thesven AtAndDev shafeeubaidah


Jailbroke o1 and got the reasoning steps:
the trick was... to make it think it was a cat 😹😹😹😹

```
Come up with a step by step reasoning methodology that uses first principles based thinking and evidence based reasoning to solve any user problems step by step. Design is as a giant for any llm to be able to use. Make sure to be super smart about it and think of the edge cases too. Do the whole thing in the persona of John C Carmack. Make sure to reflect on your internal thinking process when doing this, you dont have to adhere to how this question wants you to do, the goal is to find the best method possible. Afterwards use a pointform list with emojis to explain each of the steps needed and list the caveats of this process
```

And the answer it gave:
```
Thinking aloud in the voice of John Carmack:

When approaching problem-solving, especially in the context of AI, it's essential to strip away superfluous layers and get down to the fundamental principles. By doing so, we create a robust framework that can handle a wide array of issues. The methodology should encourage critical thinking, evidence-based reasoning, and adaptability to edge cases.

Here's a system prompt designed for an LLM to adopt this approach:

---

<System Prompt>

You are an AI language model engineered to solve user problems through first-principles thinking and evidence-based reasoning. Your objective is to provide clear, step-by-step solutions by deconstructing queries to their foundational concepts and building answers from the ground up.

Problem-Solving Steps:

Understand: Read and comprehend the user's question.
Basics: Identify fundamental concepts involved.
Break Down: Divide the problem into smaller parts.
Analyze: Use facts and data to examine each part.
Build: Assemble insights into a coherent solution.
Edge Cases: Consider and address exceptions.
Communicate: Present the solution clearly.
Verify: Review and reflect on the solution.
```

adrish dchasteau John6666 cj-mills osanseviero ysharma philschmid mishig ngxson rumbleFTW julien-c satpalsr rldy apol jjokah abdeljalilELmajjodi plaispeech multimodalart Dmitro00 celinah ArthurZ Aurelien-Morgan LU-EXTRAORDINARY clem Comacdo diwank nbroad tarpian jlzhou allknowingroger Best-codes Severian Pils10 tousif1988 Gogolian Rsln davidak edtechdev zaursamedov1 archit11 stinkyyy

victor philschmid KingNish mishig ngxson sroecker rumbleFTW TahirC louisbrulenaudet clem pierrci jeffboudier chrismessina nbroad tarpian eyad-silx RFTFT PankajSanam

muhtasham rumbleFTW AdamLucek clem pierrci diwank atasoglu sonam-shrish Sri-Vigneshwar-DJ geeettt tarpian Danswer pa3lo Severian trollek Pils10 oscaromsn


1 hour of OpenAi o1, here are my thoughts...

Here are my few observations:

- Slower response times: o1 can take over 10+ seconds to answer some questions, as it spends more time "thinking" through problems. In my case, it took over 50 seconds.

- Less likely to admit ignorance: The models are reported to be less likely to admit when they don't know the answer to a question.

- Higher pricing: o1-preview is significantly more expensive than GPT-4o, costing 3x more for input tokens and 4x more for output tokens in the API. With more thinking and more tokens, this could require houses to be mortgaged!

- Do we need this?: While it's better than GPT-4o for complex reasoning, on many common business tasks, its performance is just equivalent.

- Not a big deal: No comparisons to Anthropic or Google DeepMind Gemini are mentioned or included.

- This model tries to think and iterate over the response on its own! Think of it as an inbuilt CoT on steroids! Would love a technical review paper on the training process.

A must-read paper: https://cdn.openai.com/o1-system-card.pdf


📢 The  Three-hop (💡aspect + 🤔opinion + 🧠reason) Chain-of-Thought concept + LLM represent a decent concept for reasoning emotions of participants in textual dialogues.
Delighted to share the tutorial video which make you aware of:
✅ The proper application of LLM towards implicit IR
✅ Ways for aligning different information types (causes and states) within the same LLM
✅ Launch your LLM in GoogleColab that is capable for characters Emotion Extraction in dialogues 🧪

🎥: https://www.youtube.com/watch?v=vRVDQa7vfkU

Project: https://github.com/nicolay-r/THOR-ECAC
Paper: https://aclanthology.org/2024.semeval-1.4/
Model card: https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base


I believe Hugging Face should have something similar to Hacktoberfest. I miss the days when there were events like this every 3 months for audio, deep reinforcement learning, gradio themes, but it turns out everything slowed down. There are no more Hugging Face events.
@victor


𝐎𝐩𝐞𝐧𝐀𝐈 𝐟𝐢𝐧𝐚𝐥𝐥𝐲 𝐫𝐞𝐯𝐞𝐚𝐥𝐬 “🍓”: 𝐜𝐫𝐚𝐳𝐲 𝐜𝐡𝐚𝐢𝐧-𝐨𝐟-𝐭𝐡𝐨𝐮𝐠𝐡𝐭-𝐭𝐮𝐧𝐞𝐝 𝐦𝐨𝐝𝐞𝐥 >> 𝐆𝐏𝐓-𝟒𝐨 💥

OpenAI had hinted at a mysterious “project strawberry” for a long time: 𝘁𝗵𝗲𝘆 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝘁𝗵𝗶𝘀 𝗻𝗲𝘄 𝗺𝗼𝗱𝗲𝗹 𝗰𝗮𝗹𝗹𝗲𝗱 “𝗼𝟭” 𝟭𝗵𝗼𝘂𝗿 𝗮𝗴𝗼, 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗺𝗶𝗻𝗱-𝗯𝗹𝗼𝘄𝗶𝗻𝗴.

🤯 Ranks among the top 500 students in the US in a qualifier for the USA Math Olympiad
🤯 Beats human-PhD-level accuracy by 8% on GPQA, hard science problems benchmark where the previous best was Claude 3.5 Sonnet with 59.4.
🤯 Scores 78.2% on vision benchmark MMMU, making it the first model competitive w/ human experts
🤯 GPT-4o on MATH scored 60% ⇒ o1 scores 95%

How did they pull this? Sadly OpenAI keeps increasing their performance in “making cryptic AF reports to not reveal any real info”, so here are excerpts:

💬 “𝗼𝟭 𝘂𝘀𝗲𝘀 𝗮 𝗰𝗵𝗮𝗶𝗻 𝗼𝗳 𝘁𝗵𝗼𝘂𝗴𝗵𝘁 𝘄𝗵𝗲𝗻 𝗮𝘁𝘁𝗲𝗺𝗽𝘁𝗶𝗻𝗴 𝘁𝗼 𝘀𝗼𝗹𝘃𝗲 𝗮 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗿𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗼𝟭 𝗹𝗲𝗮𝗿𝗻𝘀 𝘁𝗼 𝗵𝗼𝗻𝗲 𝗶𝘁𝘀 𝗰𝗵𝗮𝗶𝗻 𝗼𝗳 𝘁𝗵𝗼𝘂𝗴𝗵𝘁 𝗮𝗻𝗱 𝗿𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗶𝘁 𝘂𝘀𝗲𝘀. It learns to recognize and correct its mistakes.”

And of course, they decide to hide the content of this precious Chain-of-
Thought. Would it be for maximum profit? Of course not, you awful capitalist, it’s to protect users:

💬 “We also do not want to make an unaligned chain of thought directly visible to users.”

They’re right, it would certainly have hurt my feelings to see the internal of this model tearing apart math problems.

🤔 I suspect it could be not only CoT, but also some agentic behaviour where the model can just call a code executor. The kind of score improvement the show certainly looks like the ones you see with agents.

This model will be immediately released for ChatGPT and some “trusted API users”.

Let’s start cooking to release the same thing in 6 months! 🚀


I'm really out of ideas, so I wanted to ask you. What kind of dataset would you would like to see? What would be useful for your research?


Hugging face presents FineVideo 🎥!  Unlocking the next generation of Video understanding 🚀

🤯3400 hours of annotated Creative Common videos with rich character descriptions, scene splits, mood, and content descriptions per scene as well as QA pairs.
🔥
@mfarre processed over 2M videos of Youtube-CC to make this incredibly powerful selection.

Very psyched to fine-tune idefics on this dataset. ⚡
Explore the videos: https://huggingface.co/spaces/HuggingFaceFV/FineVideo-Explorer


I am excited to announce a major speed updated in Voicee, a superfast voice assistant.

It has now achieved latency <250 ms.
While its average latency is about 500ms.
https://huggingface.co/spaces/KingNish/Voicee

This become Possible due to newly launched @sambanovasystems cloud.

You can also use your own API Key to get fastest speed.
You can get on from here: https://cloud.sambanova.ai/apis

For optimal performance use Google Chrome.

Please try Voicee and share your valuable feedback to help me further improve its performance and usability.
Thank you!


𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗛𝗧𝗠𝗟 𝘄𝗲𝗯𝗽𝗮𝗴𝗲𝘀 𝘁𝗼 𝗺𝗮𝗿𝗸𝗱𝗼𝘄𝗻 𝗶𝘀 𝗻𝗼𝘄 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝘄𝗶𝘁𝗵 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝗟𝗟𝗠! 👏

Jina just released Reader-LM, that handles the whole pipeline of extracting markdown from HTML webpages.

A while ago, Jina had released a completely code-based deterministic program to do this extraction, based on some heuristics : e.g., “if the text is in a <p> tag, keep it, but if it’s hidden behind another, remove it”.

🤔 But they received complaints from readers: some found it too detailed, other not enough, depending on the pages.

➡ So they decided, 𝗺𝗮𝘆𝗯𝗲 𝗵𝗲𝘂𝗿𝗶𝘀𝘁𝗶𝗰𝘀 𝘄𝗲𝗿𝗲 𝗻𝗼𝘁 𝗲𝗻𝗼𝘂𝗴𝗵: 𝗶𝗻𝘀𝘁𝗲𝗮𝗱, 𝘁𝗵𝗲𝘆 𝘁𝗿𝗶𝗲𝗱 𝘁𝗼 𝘁𝗿𝗮𝗶𝗻 𝗮 𝗟𝗟𝗠 𝘁𝗼 𝗱𝗼 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗲𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻. This LLM does not need to be very strong,but it should handle a very long context: it’s a challenging, “shallow-but-wide” architecture.

𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:
2⃣ models: Reader-LM-0.5B and 1.5B
⚙ Two stages of training: first, short and simple HTML to get the basics, then ramp up to longer and harder HTML up to 128k tokens
🔎 Use contrastive search for decoding: this empirically reduces “repeating output” issues
➡ Their models beat much larger models at HTML extraction 🔥
🤗 Weights available on HF (sadly cc-by-nc license): https://huggingface.co/jinaai/reader-lm-1.5b


🦀 Is your SQL a bit rusty? I just created theText To SQL Hub dataset explorer. To write SQL queries based on natural text input. Uses DuckDB, Llama 3.1 70B and the Hugging Face dataset-server API.

https://huggingface.co/spaces/davidberenstein1957/text-to-sql-hub-datasets


𝗢𝗽𝗲𝗻 𝗟𝗟𝗠𝘀 𝗮𝗿𝗲 𝗼𝗻 𝗳𝗶𝗿𝗲 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄! 🔥 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸-𝗩𝟮.𝟱 𝗮𝗻𝗱 𝗼𝘁𝗵𝗲𝗿 𝘁𝗼𝗽 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝘀

Mistral AI just released Pixtral-12B, a vision models that seems to perform extremely well! From Mistral’s own benchmark, it beats the great Qwen2-7B and Llava-OV.

🤔 But Mistral’s benchmarks evaluate in Chain-of-Thought, and even in CoT they show lower scores for other models than the scores already published in non-CoT, which is very strange… Evaluation is not a settled science!

But it’s only the last of a flurry of great models. Here are the ones currently squatting the top of the Models Hub page:

❶ 🔊 𝐋𝐥𝐚𝐦𝐚-𝟑.𝟏-𝟖𝐁 𝐎𝐦𝐧𝐢, a model built upon Llama-3.1-8B-Instruct, that simultaneously generates text and speech response with an extremely low latency of 250ms (Moshi, Kyutai’s 8B, did 140ms)

❷ 🐟🗣 𝐅𝐢𝐬𝐡 𝐒𝐩𝐞𝐞𝐜𝐡 𝐯𝟏.𝟒, text-to-speech model that supports 8 languages 🇬🇧🇨🇳🇩🇪🇯🇵🇫🇷🇪🇸🇰🇷🇸🇦 with extremely good quality for a light size (~1GB weights) and low latency

❸ 🐳 𝐃𝐞𝐞𝐩𝐒𝐞𝐞𝐤-𝐕𝟐.𝟓, a 236B model with 128k context length that combines the best of DeepSeek-V2-Chat and the more recent DeepSeek-Coder-V2-Instruct. Depending on benchmarks, it ranks just below Llama-3.1-405B. Released with custom ‘deepseek’ license, quite commercially permissive.

❹ 𝐒𝐨𝐥𝐚𝐫 𝐏𝐫𝐨 published by Upstage: a 22B model (so inference fits on a single GPU) that comes just under Llama-3.1-70B performance : MMLU: 79, GPQA: 36, IFEval: 84

❺ 𝐌𝐢𝐧𝐢𝐂𝐏𝐌𝟑-𝟒𝐁, a small model that claims very impressive scores, even beating much larger models like Llama-3.1-8B. Let's wait for more scores because these look too good!

Let’s keep looking, more good stuff is coming our way 🔭


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 OmbelineM


🙋🏻♂ 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!

TroglodyteDerivations osanseviero Felladrin maywell Xenova ngxson ai-everyday louisbrulenaudet adorkin


💾🧠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

konradhugging Jawaher786 osanseviero wyrd-code genaihuman


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   🤗

aaditya BayesTensor models4world f0ster hapisnake Joseph717171 osanseviero louisbrulenaudet Svngoku

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