Looking for a combination of speed and quality? Look no further! I've created a space that merges Open WebUI's excellent interface and features with the lightning-fast performance of the Groq API. Experience top-tier models in no time. Try it out for free here:
https://huggingface.co/spaces/L-AI/groq-chat

"A big thank you to Groq for providing their fantastic API at no cost!"

Artples amosgyamfi Ripp218 snakeying ygmpkk efecelik


@MaartenGr nice post https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-quantization ("A Visual Guide to Quantization")

Would it make sense for you to publish it here too?


I am looking for anotation tool/software for video segmentations, landmarking and feature-detection.

I had bumped into xlabelAnything, however I could not run it on my machine.
 Any recommendations ?


Hey ! I'm working on a 100% synthetic Dataset Hub here (you can search for any kind of datasets an the app invents them). The link is here: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub

Question for the Community:

Which models should I use to generate images and audio samples for those datasets ? 🤗


Just made this!
https://github.com/takeraparterer/Charformer


https://huggingface.co/nevmenandr/w2v-chess

```python
import gensim
from sklearn.decomposition import PCA
import matplotlib
import matplotlib.pyplot as plt

model = gensim.models.Word2Vec.load('white_moves.model')
dict_moves = model.wv.vocab
dict_moves_appr = {}
for k in dict_moves:
    if not k.startswith('->'):
        continue
    dict_moves_appr[k] = dict_moves[k]
X = model[model.wv.vocab]
pca = PCA(n_components=2)
result = pca.fit_transform(X)
fig, ax = plt.subplots()
ax.plot(Y[:, 0], Y[:, 1], 'o')
ax.set_title('White moves')
lab = list(dict_moves_appr)
for i, lb in enumerate(lab):
    plt.annotate(lb, xy=(Y[i, 0], Y[i, 1]))
plt.show()
```
biblically accurate angel


Just published Image Captioning Editor Gradio APP - Edit Your Captions Super Easy Including Batch Editing - For Windows, RunPod and Massed Compute. Developed by me. Have lots of amazing features that all you need. Let me know if missing any features.

Gradio is amazing to develop such amazing apps in short time. Used Claude 3.5 to develop it :)

Scripts are available here : https://www.patreon.com/posts/108992085


lamagenius
Smart free open source AI powered search engine

https://hf.co/chat/assistant/66a5fc9f02b1826ba4cabd72


Hi HF Community!🤗

In the past days, OpenAI announced their search engine, SearchGPT: today, I'm glad to introduce you SearchPhi, an AI-powered and open-source web search tool that aims to reproduce similar features to SearchGPT, built upon https://huggingface.co/microsoft/Phi-3-mini-4k-instruct, llama.cpp🦙 and Streamlit.
Although not as capable as SearchGPT, SearchPhi v0.0-beta.0 is a first step toward a fully functional and multimodal search engine :)
If you want to know more, head over to the GitHub repository (https://github.com/AstraBert/SearchPhi) and, to test it out, use this HF space: https://huggingface.co/spaces/as-cle-bert/SearchPhi
Have fun!🐱

yasmineee TuringsSolutions John6666 SizorCloud louisbrulenaudet 316usman victor Ramikan-BR leeloolee Hev832 kramp Ripp218 osanseviero

adorkin Nischalj10 Ramikan-BR ucsahin ugurcanvurgun Goodiro Ripp218


Datasets are down, I offer a solution

```
git lfs install
```
```
git clone https://huggingface.co/datasets/{dataset/id}
```
```python
from datasets import load_dataset

dataset = load_dataset("id")
```

Tonic John6666 LeroyDyer Hev832 GPT007 Nymbo TuringsSolutions louisbrulenaudet sugatoray not-lain sunnyg Blane187


⚗ Find reusable synthetic data pipeline code and corresponding datasets on the @huggingface Hub.

Find your pipline and use `$ distilabel pipeline run --config "hugging_face_dataset_url/pipeline.yaml"`

Some components I used
- Embedded dataset viewer https://huggingface.co/docs/hub/main/en/datasets-viewer-embed
- Hugging Face fsspec https://huggingface.co/docs/huggingface_hub/main/en/guides/hf_file_system
- distilabel https://distilabel.argilla.io/latest/
- Gradio leaderboard by Freddy Boulton https://huggingface.co/spaces/freddyaboulton/gradio_leaderboard
- Gradio modal by Ali Abid

Space: https://huggingface.co/spaces/davidberenstein1957/distilabel-synthetic-data-pipeline-explorer

guerrat John6666 mlabonne sugatoray MaziyarPanahi louisbrulenaudet dvilasuero davidberenstein1957


Hello, HuggingFace🤗 community 🌟,

All the amazing people quantising LLMs to AWQ and GPTQ 🔧🤖

Can you please mention the perplexity you achieved 📉 OR any other metric to measure the quantisation qualitatively? 📊

The GGUF community follows this really well! 👍

And if it is not too much to ask, the script used for quantisation would be amazing! 📝

Thanks for the quants for the GPU poor! 💻


Exciting news!

After a long wait, Ikari and me finally made a new release of our last model on NeverSleep repo: Lumimaid-v0.2

This model can be used in different size, from the small Llama-3.1-8B to the gigantic Mistral-Large-123B, finetuned by us.

Try them now!

- https://huggingface.co/NeverSleep/Lumimaid-v0.2-8B
- https://huggingface.co/NeverSleep/Lumimaid-v0.2-12B
- https://huggingface.co/NeverSleep/Lumimaid-v0.2-70B
- https://huggingface.co/NeverSleep/Lumimaid-v0.2-123B

All the datasets we used will be added and credit will be given!
For the quant, we wait for fix to be applied (https://github.com/ggerganov/llama.cpp/pull/8676)
Hope you will enjoy them!

unbreaker88 AtAndDev MarinaraSpaghetti Morenosolo


I can't resist an opportunity to update an old baseline. Read a new article on my latest look at improving MobileNet-V1 and EfficientNet-B0 baselines.

https://huggingface.co/blog/rwightman/mobilenet-baselines
https://huggingface.co/timm/mobilenetv1_100.ra4_e3600_r224_in1k
https://huggingface.co/timm/efficientnet_b0.ra4_e3600_r224_in1k


At Hugging Face we have an open-source Cookbook with many applied AI recipes 📖
Here are some of the latest recipes contributed ⥥

- "Information Extraction with Haystack and NuExtract": Use Haystack and transformers to build structured data extraction pipelines using LLMs by @anakin87  https://huggingface.co/learn/cookbook/en/information_extraction_haystack_nuextract

- "Build RAG with Hugging Face and Milvus": Learn how to use Milvus with sentence transformers to build RAG pipelines https://huggingface.co/learn/cookbook/rag_with_hf_and_milvus

- "Code Search with Vector Embeddings and Qdrant": Search a codebase by building a retrieval pipeline using Qdrant and sentence transformers https://huggingface.co/learn/cookbook/code_search

- Data analyst agent: get your data’s insights in the blink of an eye ✨: great recipe by our own @m-ric showing how to build an agent that can do data analysis! 😱 https://huggingface.co/learn/cookbook/agent_data_analyst

anakin87 lazarustda AtAndDev not-lain efecelik


Barefoot developer experiment: AI-powered app creation with my poor coding skills 🧠💻

I recently discussed the "barefoot developer" concept - using AI to build apps for specific needs without coding expertise. Decided to put it to the test. 🔬

🍅⏲ The challenge: Create a menu bar Pomodoro app for my computer to boost my focus. Previous attempt? Messy.
🤯 The twist: I've never coded in Swift.
⚡ The result: 30 minutes. No joke. Elegant, functional, and shareable.

🔗 Want to try it yourself? Grab the open source code and app here:
- Code: https://github.com/fdaudens/pomodoro2
- App: https://github.com/fdaudens/pomodoro2/releases/tag/v1.0.0

Key takeaways:
🚀 AI-assisted development is evolving rapidly
🧩 Domain expertise + AI tools can yield impressive results
🌐 This approach democratizes app creation

🤔 What's your take on AI-powered development? Have you experimented with it?

#AIinDevelopment #BarefootDeveloper #OpenSource


Hi All,
In my latest blog post, I created a comprehensive guide on LLM Benchmarking.
➟ 20+ key benchmarks, from MMLU to TruthfulQA
➟ How each benchmark assesses different LLM capabilities
➟ Why benchmarking matters for real-world AI applications
➟ Future trends in AI evaluation
Read the blog here: https://wp.me/p7Qix-wO

Please let me know your thoughts, suggestions, and comments.


Try to find a better int4 algorithm for LLAMA3.1?  For the 8B model, AutoRound boasts an average improvement across 10 zero-shot tasks, scoring 63.93 versus 63.15 (AWQ). Notably, on the MMLU task, it achieved 66.72 compared to 65.25, and on the ARC-C task, it scored 52.13 against 50.94. For further details and comparisons, visit the leaderboard at https://huggingface.co/spaces/Intel/low_bit_open_llm_leaderboard.


When @MistralAI drops a blog post labelled "Large Enough," it's going to get serious! 🚀💡

- Mistral-Large-Instruct-2407, just call it Mistral-Large2, is a 123B parameters Instruct model with 128k context 🌍📚

- Multilingual in 11 languages; English 🇬🇧, French 🇫🇷, German 🇩🇪, Spanish 🇪🇸, Italian 🇮🇹, Chinese 🇨🇳, Japanese 🇯🇵, Korean 🇰🇷, Portuguese 🇵🇹, Dutch 🇳🇱, and Polish 🇵🇱. 🗣🗺

- Also highly focused on programming, trained on 80+ coding languages such as Python, Java, C, C++, Javascript, bash 💻🔧

- Supports native function calling and structured output. 🛠📊

- Released under Mistral Research License (Non-Commercial License, Research only😔)

- Open weights only🔓, no data or code released 🔒📁

Definitely firing shots at @Meta Llama3.1: 🎯🔥
MMLU - 84.0% (ML2) vs 79.3% (L3.1-70B) vs 85.2% (L3.1-405B)
GSM8K - 93% (ML2) vs 95.5% (L3.1-70B-Ins) vs 96.8% (L3.1-405B-Ins)

Also, it's kinda chunky! 📦💪
fp16/ bf16 - ~250GB VRAM
fp8/ int8 - ~125GB VRAM
int4 - ~60GB VRAM

I tried quantising it to AWQ and GPTQ, but couldn't with 30GB V-RAM. ❌🖥

Also calling out AWQ and GPTQ on not supporting multi-GPU quantisation! 🖥⚡

God sent @casperhansen has posted AWQ quantised INT4 model (68.68 GB) with the perplexity of 2.889: https://huggingface.co/casperhansen/mistral-large-instruct-2407-awq 🔥👏

Looks like open AI is going to beat OpenAI! 🏆🤖

Blog post: https://mistral.ai/news/mistral-large-2407/

Models: https://huggingface.co/mistralai/Mistral-Large-Instruct-2407


Custom Enterprise LLM/RAG with Real-Time Fine-Tuning https://mltblog.com/3WcTS9C -- Just released!

This article features an application of xLLM to extract information from a corporate corpus, using prompts referred to as “queries”. The goal is to serve the business user — typically an employee of the company or someone allowed access — with condensed, relevant pieces of information including links, examples, PDFs, tables, charts, definitions and so on, to professional queries.

My custom sub-LLM designed from scratch does not rely on any Python library or API, and performs better than search tools available on the market, in terms of speed and results relevancy. It offers the user the ability to fine-tune parameters in real time, and can detect user intent to deliver appropriate output. The good performance comes from the quality of the well-structured input sources, combined with smart crawling to retrieve the embedded knowledge graph and integrate it into the backend tables. Traditional tools rely mostly on tokens, embeddings, billions of parameters and frontend tricks such as prompt engineering to fix backend issues.

To the contrary, my approach focuses on building a solid backend foundational architecture from the ground up. Tokens and embeddings are not the most important components, by a long shot. Cosine similarity and dot products are replaced by pointwise mutual information. There is no neural network, no training, and a small number of explainable parameters, easy to fine-tune.

Read more, access the code and data, at https://mltblog.com/3WcTS9C


Aspen Institute's wake-up call for journalism: Embrace AI or risk obsolescence 📰🤖

"Every new technology comes with risks—it's how the media industry responds that determines how (or whether) news providers can prevail." —
Vivian Schiller

Bonus: In need of ideas for AI projects? The report is a goldmine of real-world experiments. Here are some lesser-known innovations:

📊 Spotting patterns: Semafor uses chatbots to assess newsroom performance
📜 Extending reach: Politico summarizes state and federal legislation with AI
🎙 Transformation: Washington Post uses AI-generated voices for newsletter narration
🏀 Comprehensive coverage: Richland Source covers 10,000 Ohio high school sports games yearly with AI
🔑 Summarization: Gannett adds AI-generated bullet points to stories
🌍 Translation: Finnish broadcaster Yle built an AI tool to reach Ukrainian immigrants
🎥 Internal info: AP's Merlin tool pinpoints key video moments
🗳 Civic engagement: Spotlight PA's AI assistant answers election questions
🗣 Personalization: Baltimore Times customizes health news with AI voice readers
🎯 Targeted ads: NYT's AI tool aligns content with advertisers' focus
💼 Conversion: WSJ uses ML to boost subscription renewals

A must-read for all in media: https://aspendigital.org/wp-content/uploads/2024/07/Aspen-Digital_Here-Come-the-Robots_July-2024.pdf

#AIinJournalism #MediaInnovation #FutureofNews


😏 Hello from Project Fluently Team!

✨ Finally we can give you some details about Supple Diffusion. We worked on it for a long time and we have little left, we apologize that we had to increase the work time.

🛠 Some technical information. The first version will be the Small version (there will also be Medium, Large, Huge, possibly Tiny), it will be based on the SD1 architecture, that is, one text encoder, U-net, VAE. Now about each component, the first is a text encoder, it will be a CLIP model (perhaps not CLIP-L-path14), CLIP was specially retrained by us in order to achieve the universality of the model in understanding completely different styles and to simplify the prompt as much as possible. Next, we did U-net, U-net in a rather complicated way, first we trained different parts (types) of data with different U-nets, then we carried out merging using different methods, then we trained DPO and SPO using methods, and then we looked at the remaining shortcomings and further trained model, details will come later. We left VAE the same as in SD1 architecture.

🙌 Compatibility. Another goal of the Supple model series is full compatibility with Auto1111 and ComfyUI already at the release stage, the model is fully supported by these interfaces and the diffusers library and does not require adaptation, your usual Sampling methods are also compatible, such as DPM++ 2M Karras, DPM++ SDE and others.

🧐 Today, without demo images (there wasn’t much time), final work is underway on the model and we are already preparing to develop the Medium version, the release of the Small version will most likely be in mid-August or earlier.

😻 Feel free to ask your questions in the comments below the post, we will be happy to answer them, have a nice day!

Nymbo WaveCut YaTharThShaRma999 not-lain comarproject dreamdrop-art GPT007 louisbrulenaudet osanseviero ehristoforu

ehristoforu Nymbo diegotluz dreamdrop-art GPT007


𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝘁: 𝗱𝗿𝗼𝗽 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗳𝗶𝗹𝗲, 𝗹𝗲𝘁 𝘁𝗵𝗲 𝗟𝗟𝗠 𝗱𝗼 𝘁𝗵𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 📊⚙

Need to make quick exploratory data analysis? ➡ Get help from an agent.

I was impressed by Llama-3.1's capacity to derive insights from data. Given a csv file, it makes quick work of exploratory data analysis and can derive interesting insights.

On the data from the Kaggle titanic challenge, that records which passengers survived the Titanic wreckage, it was able by itself to derive interesting trends like "passengers that paid higher fares were more likely to survive" or "survival rate was much higher for women than men".

The cookbook even lets the agent built its own submission to the challenge, and it ranks under 3,000 out of 17,000 submissions: 👏 not bad at all!

Try it for yourself in this Space demo 👉 https://huggingface.co/spaces/m-ric/agent-data-analyst


In principle, it's possible to "abliterate" refusals in any Llama 3.1 8B models via application of a LoRA, using only mergekit.

Proof of concept below:
https://huggingface.co/grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter


Made a demo for all my Brazil XL LoRA models so far. Use it for free at https://huggingface.co/spaces/lucianosb/brazilxl-demo

Brazil XL is an initiative that brings better representations of Brazilian culture to Stable Diffusion. I started this when I noticed some keywords would not generate the desired subject on any base model, so I trained my own models and I'm sharing them with the HF community.

I'll keep updating the space as new models get trained on the following months.


We have recently merged Video-LLaVA to transformers! 🤗🎞
What makes this model different?

Demo: https://huggingface.co/spaces/llava-hf/video-llava
Model: https://huggingface.co/LanguageBind/Video-LLaVA-7B-hf

Compared to other models that take image and video input and either project them separately or downsampling video and projecting selected frames, Video-LLaVA is converting images and videos to unified representation and project them using a shared projection layer.

It uses Vicuna 1.5 as the language model and LanguageBind's own encoders that's based on OpenCLIP, these encoders project the modalities to an unified representation before passing to projection layer.

I feel like one of the coolest features of this model is the joint understanding which is also introduced recently with many models

It's a relatively older model but ahead of it's time and works very well! Which means, e.g. you can pass model an image of a cat and a video of a cat and ask questions like whether the cat in the image exists in video or not 🤩

adorkin orkut osanseviero Andyrasika SizorCloud


The Meta Llama-3.1 model series can be used for distilling and fine-tuning but this requires annotated preference data so I created a Human Feedback Collector based on Gradio that directly logs data to the Hugging Face Hub.

- Model meta-llama/Meta-Llama-3.1-8B-Instruct
- Data SFT, KTO and DPO data
- Runs on free Zero GPUs in Hugging Face Spaces
- Might need some human curation in Argilla
- Or provide some AI feedback with distilabel

https://huggingface.co/collections/davidberenstein1957/chatinterface-llm-human-feedback-collectors-66a22859c9e703d2af7500c1

fdaudens davidberenstein1957 osanseviero Dihelson Nelathan Ramikan-BR

davidberenstein1957 osanseviero NickyNicky Dihelson danielus Ramikan-BR


Bellman Swedish finetune based on llama3.1 8b is now available:
https://huggingface.co/neph1/llama-3.1-instruct-bellman-8b-swedish

More quants and fp16 are coming. Working out some issues with colab..


Super Exciting New Paper By Meta🤖🧠🚀

Discrete Flow Matching:
Introduces a new framework/algorithm for generating text/code without having to predict auto-regressively or one “word” at a time as traditional GPT models do. It generates all parts of the text/code at once.

The algorithm does this by slowly transforming random noise (source) into meaningful text (data). It learns how to transform samples along a path created between source and target using a "probability velocity" that describes how probabilities change over time. During generation, DFM starts with a random sample and iteratively updates it using this learned velocity, gradually transforming it into a sample from the target distribution. This allows for non-autoregressive generation.

They were able to scale models of up to 1.7B parameters achieving  impressive scores on HumanEval and MBPP for coding, significantly closing the gap between autoregressive models and discrete flow models.

Though in its infancy, it sure does hold a promising future as leading research scientists argue non-autoregressive methods yield better reasoning.

eek takeraparterer victor Xdotnet fatimarizwan ZeroWw Kkordik


Yet another post hailing how good Meta Llama 3.1 is? 🤔 I guess not!

While Llama 3.1 is truly impressive, especially 405B (which gives GPT-4o a run for its money! 💪)

I was surprised to see that on the Open LLM Leaderboard, Llama 3.1 70B was not able to dethrone the current king Qwen2-72B! 👑

Not only that, for a few benchmarks like MATH Lvl 5, it was completely lagging behind Qwen2-72B! 📉

Also, the benchmarks are completely off compared to the official numbers from Meta! 🤯

Based on the responses, I still believe Llama 3.1 will perform better than Qwen2 on LMSYS Chatbot Arena. 🤖 But it still lags behind on too many benchmarks! 🏃♂

Open LLM Leaderboard: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard  🌐

Hopefully, this is just an Open LLM Leaderboard error! @open-llm-leaderboard SOS! 🚨

eek osanseviero dillfrescott quyettv d8rt8v


🚀 Introducing the Model Drops Tracker! 🕵♂

Feeling overwhelmed by the AI model release frenzy? 🤯 You're not alone!

I built this simple tool to help us all keep up:
- Filter recent models from the 🤗 Hub
- Set minimum likes threshold
- Choose how recent you want to go

Try it out and let me know what you think: https://huggingface.co/spaces/fdaudens/Model-Drops-Tracker

Any features you'd like to see added?
#AIModels


🤖💡Just tried out @m-ric 's new LLaMA-3.1 70B agent for data analysis. Impressive stuff.

🚢📊 Fed it the Titanic passenger dataset with minimal instructions. The agent autonomously dug in, tested hypotheses, and reached some intriguing conclusions:

"Lower class passengers less likely to survive, slight negative correlation with age, and positive correlation between fare price and survival."

📈It even generated charts to visualize the findings!

🧠💼 Great potential for business intelligence, research, and decision-making when we can just upload datasets and let AI agents loose on them.

👉 Check it out: https://huggingface.co/spaces/m-ric/agent-data-analyst

🤔 Any particular use cases you're excited about?

#AIinDataAnalysis #MachineLearning #DataScience


Hello there,

New model released, my goal was to try finetune on the last Llama-3.1-8B-Instruct but not a small train, I wanted to do something useful.
One of the rare model that I didn't made for RP, or in the goal to uncensor it (but I did anyway kek).

The model was trained on 9M Claude conversations ONLY, giving him another writting style.

https://huggingface.co/Undi95/Meta-Llama-3.1-8B-Claude > OG release fp32, it's the epoch 2
https://huggingface.co/Undi95/Meta-Llama-3.1-8B-Claude-bf16 > Base model resharded in bf16 waiting for available quant without issues

Since it's frustrating to be censored using a local model, orthogonal activation steering was used, trying to force the model to never refuse a prompt.

https://huggingface.co/Undi95/Meta-Llama-3.1-8B-Claude-68fail-3000total > Uncensored model, refuse 68 times on 3000 toxic prompt
https://huggingface.co/Undi95/Meta-Llama-3.1-8B-Claude-39fail-3000total > Uncensored model, refuse 39 times on 3000 toxic prompt

It still refuse some prompt but the majority of them is uncensored. OAS can make a model more dumb or make the base perplexity go higher, so I didn't snipe for 0 refusal.

I don't do non-RP model a lot so any feedback is welcome, I would like to re-use this base for some others future project if needed.


Professional Threads Post Writer
https://hf.co/chat/assistant/66a0ee6fc397ecb70cee100d


Llama 3.1 405B Instruct beats GPT-4o on MixEval-Hard

Just ran MixEval for 405B, Sonnet-3.5 and 4o, with 405B landing right between the other two at 66.19

The GPT-4o result of 64.7 replicated locally but Sonnet-3.5 actually scored 70.25/69.45 in my replications 🤔 Still well ahead of the other 2 though.

Sammple of 1 of the eval calls here: https://wandb.ai/morgan/MixEval/weave/calls/07b05ae2-2ef5-4525-98a6-c59963b76fe1

Quick auto-logging tracing for openai-compatible clients and many more here: https://wandb.github.io/weave/quickstart/


Hi HF community!🤗
Hope y'all are as excited as me for the release of Llama 3.1! 🦙
Following the release, I built a space exploiting  HF Inference API, thanks to a recipe you can find in this awesome GitHub repo (https://github.com/huggingface/huggingface-llama-recipes/): you can now run Llama-3.1-405B customizing its system instructions and other parameters, for free! 😇
Follow this link: https://huggingface.co/spaces/as-cle-bert/Llama-3.1-405B-FP8 and let the fun begin!🍕

Nymbo hg-crown victor Taylor658 osanseviero louisbrulenaudet


JUST RELEASED: Fireplace 2 for Llama 3.1 8b Instruct!

Fireplace 2 is an 'expansion pack' of structured outputs you can request during your chat, using special request tokens to let Llama know you're looking for specific types of responses:
Inline function calls
SQL queries
JSON objects
Data visualization with matplotlib

https://huggingface.co/ValiantLabs/Llama3.1-8B-Fireplace2


Meta Researchers: How many compute hours should we use to train Llama 3.1?
Mr. Zuck: Yes! 🤖💪

Good folks at @AIatMeta did not just release the models but also published a 92-page detailed paper 📄 on their findings and technical aspects of the models and their training process!

Generally, we just gobble up these weights and forget the compute infrastructure used to train these models. 🖥🚀

Here are some interesting findings about the computing infrastructure of Llamas:

- Llama 1 and 2 models were trained on @Meta 's AI Research SuperCluster. Llama 3 was migrated to Meta’s production clusters! 📊

- That's 16,000 H100 GPUs, with each GPU featuring 700W TDP and 80GB HBM3, arranged in Meta’s Grand Teton AI server platform. 🖥🔋

- What about storing checkpoints? Used Tectonic, a distributed file system, for storage, with capacities reaching 240 PB and peak throughput of 7 TB/s. 💾📈

- Meta's mad lads saved each GPU’s model state, ranging from 1 MB to 4 GB per GPU, for recovery and debugging. 🛠🔍

If this sounds big, well, they document the humungous challenges that come with it:

- In the 54-day training period, there were 466 job interruptions. 🕒🔄

- About 78% of unexpected interruptions were attributed to confirmed or suspected hardware issues. Mostly GPUs! 💥🖥

- Saving all checkpoints is cool until you do it for the 300B+ parameters model. The bursty nature of checkpoint writes, essential for state-saving during training, periodically saturated the storage fabric, impacting performance. 📉💾

- With all this, effective training time—measured as the time spent on useful training over the elapsed time—was higher than 90%. ⏱📊

I think this is the stuff that movies can be made on! 🎬🌟

Paper: https://ai.meta.com/research/publications/the-llama-3-herd-of-models/


🚀 Exciting news! We've just launched "Thundermoon" - the latest version of Moondream, our open-source vision language model! 🌙

Key improvements in this release:
1. Massive leap in OCR capabilities
2. Enhanced document understanding
3. Significant boosts across key metrics:
 * DocVQA: 61.9 (↑103%)
 * TextVQA: 60.2 (↑5.2%)
 * GQA: 64.9 (↑2.9%)

What does this mean? Moondream can now tackle complex document analysis tasks with unprecedented accuracy for a model of its size. From deciphering handwritten notes to interpreting data tables, the applications are vast.

Check out the image for a glimpse of Moondream in action, effortlessly extracting insights from a 1944 sugar industry document!

Why it matters:
* Democratizing AI: As an open-source project, we're making advanced vision AI accessible to all developers.
* Efficiency: Proving that smaller models can deliver big results.
* Real-world impact: From historical document analysis to modern business intelligence, the potential use cases are exciting.

Curious to try it out? Try out the live demo here! https://moondream.ai/playground

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I just had a masterclass in open-source collaboration with the release of Llama 3.1 🦙🤗

Meta dropped Llama 3.1, and seeing firsthand the Hugging Face team working to integrate it is nothing short of impressive. Their swift integration, comprehensive documentation, and innovative tools showcase the power of open-source teamwork.

For the curious minds:

📊 Check out independent evaluations: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard

🧠 Deep dive into the tech: https://huggingface.co/blog/llama31

👨🍳 Try different recipes (including running 8B on free Colab!): https://github.com/huggingface/huggingface-llama-recipes

📈 Visualize open vs. closed LLM progress: https://huggingface.co/spaces/andrewrreed/closed-vs-open-arena-elo

🤖 Generate synthetic data with distilabel, thanks to the new license allowing the use of outputs to train other LLMs https://huggingface.co/blog/llama31#synthetic-data-generation-with-distilabel

💡 Pro tip: Experience the 405B version for free on HuggingChat, now with tool-calling capabilities! https://huggingface.co/chat/

#OpenSourceAI #AIInnovation

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Any idea if this "scheduled"/"dynamic" batch size is available in HF Trainers ? I've never seen it personally


🚨 Launching The Visual Haystacks (VHs) Benchmark: the first "visual-centric" Needle-In-A-Haystack (NIAH) benchmark to assess LMMs' capability in long-context visual retrieval and reasoning.

Check it out!
https://huggingface.co/datasets/tsunghanwu/visual_haystacks
https://visual-haystacks.github.io/
https://arxiv.org/abs/2407.13766
https://github.com/visual-haystacks/vhs_benchmark


I just launched an exciting new multiplayer app powered by GPT-4o, enabling collaborative AI-driven queries in a single shared session!

### 🔗 Try It Out!  👉 Check out the GPT-4o Multiplayer App
Experience the future of collaborative AI by visiting our space on Hugging Face: https://huggingface.co/spaces/awacke1/ChatStreamlitMultiplayer

🎉 This innovative tool lets you and your team reason over:

###📝 Text
###🖼 Image
###🎵 Audio
###🎥 Video

## 🔍 Key Features

### Shared Contributions
Collaborate in real-time, seeing each other's inputs and contributions.
Enhances teamwork and fosters a collective approach to problem-solving.

### Diverse Media Integration
Seamlessly analyze and reason with text, images, audio, and video.
Breakthrough capabilities in handling complex media types, including air traffic control images and audio.

## 🛠 Real-World Testing
This morning, we tested the app using images and audio from air traffic control—a challenge that was nearly impossible to handle with ease just a few years ago. 🚁💬

🌱 The Future of AI Collaboration
We believe AI Pair Programming is evolving into a new era of intelligence through shared contributions and teamwork. As we continue to develop, this app will enable groups to:

Generate detailed text responses 📝
Collaborate on code responses 💻
Develop new AI programs together 🤖


We https://mii-llm.ai just released a new LLM Italian benchmark and a set of evaluation: MMLU-PRO-ITA

Thanks to @efederici who released https://huggingface.co/datasets/efederici/MMLU-Pro-ita a machine translated version of MMLU-PRO and thanks to a community shared computational effort we published  in the "Eval Aggiuntive" tab of https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard the results on Italian open source LLMs.

If you want to deepen read the blog article on hf https://huggingface.co/blog/giux78/mmlu-pro-ita


[New tool] Follow interesting ML persons 👩🎨 👨🎤 👩🏫 with Followgraph

https://huggingface.co/spaces/severo/followgraph

Please try it and tell me if it helped you discover high-quality content 👍 👎

I repurposed "Followgraph for Mastodon" (https://followgraph.vercel.app/).

My new follows: @TheBloke @mlabonne @teknium @KnutJaegersberg  @SkalskiP @AmelieSchreiber @lbourdois @ceyda @andrewyng @Pclanglais @karpathy

And you?

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🙋🏻♂ Hey there folks

made a demo for Nvidia Minitron on an A100.

Minitron is a family of small language models (SLMs) obtained by pruning NVIDIA's Nemotron-4 15B model. We prune model embedding size, attention heads, and MLP intermediate dimension, following which, we perform continued training with distillation to arrive at the final models.

Deriving the Minitron 8B and 4B models from the base 15B model using our approach requires up to 40x fewer training tokens per model compared to training from scratch; this results in compute cost savings of 1.8x for training the full model family (15B, 8B, and 4B). Minitron models exhibit up to a 16% improvement in MMLU scores compared to training from scratch, perform comparably to other community models such as Mistral 7B, Gemma 7B and Llama-3 8B, and outperform state-of-the-art compression techniques from the literature. Please refer to our arXiv paper for more details.

Minitron models are for research and development only.

source : https://huggingface.co/nvidia/Minitron-8B-Base
demo : https://huggingface.co/spaces/Tonic/Minitron

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🔥Thrilled to release our 8B version of Symbol-LLM-Instruct !

It follows the two-stage training strategy proposed in the original paper and is continually optimized on LLaMA3-Chat-8B model.

Symbol-LLM was accepted by ACL'24 main conference ! See you in Thailand !

Paper link: https://arxiv.org/abs/2311.09278
Paper Title: Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models

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How to create custom LLMs from scratch

See my new podcast on this topic, at https://mltblog.com/3xS1bf5

Despite GPT, Claude, Gemini, LLama and the other host of LLMs that we have access to, a variety of organizations are still exploring their options when it comes to custom LLMs. Logging in to ChatGPT is easy enough, and so is creating a ‘custom’ openAI GPT, but what does it take to create a truly custom LLM? When and why might this be useful, and will it be worth the effort?


LazyLLM - Unusual Colab (Apple & Meta) Yields Impactful Work

LLM inference typically consists of two stages: prefilling/tokenizing and decoding. In the prefilling stage, the model processes the entire input prompt, computing and caching key-value (KV) pairs for each token, which can be time-consuming for long prompts. This is followed by the decoding stage, where the model generates tokens sequentially, reusing the cached KVs.

LazyLLM introduces a dynamic token pruning technique. Instead of computing KVs for all tokens during prefilling, LazyLLM selectively processes only the most important tokens based on attention scores, deferring less important ones to later steps if needed. It uses progressive token pruning across transformer layers and introduces an Aux Cache to store hidden states of pruned tokens.

This approach significantly reduces the time-to-first-token (TTFT) and overall generation time while maintaining accuracy across various tasks. LazyLLM outperforms baseline techniques like random token dropping and static pruning, and can be easily integrated into existing LLMs without fine-tuning, offering a practical solution for accelerating LLM inference, especially in long context scenarios.

IN SIMPLE TERMS
When you prompt a large language model (LLM), it usually looks at every single word/subword (or tokens) in your prompt before generating a response. This can be time consuming, especially for prompts with very long texts. This paper introduces a new technique that solves this problem by being more selective. Instead of looking at every word right away, it only focuses on the most important words first. It decides which words are important based on how much attention the model gives them. If it needs other words later, it can go back and look at them then. This approach is like skimming a text for key information before reading it in detail.

Read More: https://arxiv.org/pdf/2407.14057


SNN Image Diffusion V2

Billionaires have been made for less than this. This is only one of the things it can it do. It can do API calls, function calls, optimize poker and blackjack odds, anything that is an optimization problem. It costs fractions of a penny and requires fractions of the compute of an LLM model. It can even communicate two ways with an LLM model.


Hi HuggingFacers!🤗

Good news concerning https://huggingface.co/spaces/as-cle-bert/smolLM-arena, the chat arena where you can compare some of the Small Language Models (<1.7B) on the Hub and cast your vote to choose the best!📱
The space now has a new interface with chatbots instead of textboxs, it runs faster and it also comes with usage instructions :)
Have fun!🍕

prithivMLmods nicolay-r Ramikan-BR cahlen osanseviero quyettv louisbrulenaudet


Introducing Whisper Diarization: Multilingual speech recognition with word-level timestamps and speaker segmentation, running 100% locally in your browser thanks to 🤗 Transformers.js!

Tested on this iconic Letterman interview w/ Grace Hopper from 1983!
- Demo: https://huggingface.co/spaces/Xenova/whisper-speaker-diarization
- Source code: https://huggingface.co/spaces/Xenova/whisper-speaker-diarization/tree/main/whisper-speaker-diarization

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"By the end of this blog post, you will have
- learnt all the new goodies accompanying the latest macOS release
- AND successfully run a 7B parameter model using less than 4GB of memory on your Mac."

Game-changer for local AI? Can't wait to try this!

Brillant work by @pcuenq  @osanseviero  @reach-vb  @FL33TW00D-HF

Check it out: https://huggingface.co/blog/mistral-coreml #apple


🎉 Ghost 8B Beta Released: Game-Changing Language Model
--
Ghost 8B Beta is a groundbreaking language model developed with a clear vision: to deliver exceptional multilingual support, superior knowledge capabilities, and all while remaining cost-effective. This model comes in two context length variations, 8k and 128k, ensuring flexibility for various tasks. Moreover, it boasts built-in multilingual functionality, making it a powerful tool for global communication and understanding.
--
* See detailed article: https://huggingface.co/blog/lamhieu/ghost-8b-beta-released-game-changing-language-mode
* Model card: https://huggingface.co/ghost-x/ghost-8b-beta
* Official website: https://ghost-x.org/docs/models/ghost-8b-beta

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Hey everyone 🤗!
Check out this cool little reproduction of the Clarity Upscaler (https://github.com/philz1337x/clarity-upscaler) using refiners (https://github.com/finegrain-ai/refiners): https://huggingface.co/spaces/finegrain/enhancer

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Questions about data, synthetic data, human feedback and data quality?

Argilla has moved its community from Slack to the Hugging Face Discord server!

When part of the Hugging Face Discord, you can select “Channels & roles” and select “Argilla” along with any of the other groups that are interesting to you. “Argilla” will cover anything about argilla and distilabel, and it will give you access to 1) #argilla-distilabel-general, for all general discussions and news, and 2) #argilla-distilabel-help, for any usage-focused questions.


ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG
  Capabilities (2407.14482)

🚀 Good folks at @nvidia just dropped: "ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities" 🧠💡

In the past few months, the open LLM community has made significant progress in releasing open models (Llama-3-70B-Instruct (@Meta -AI) 🦙, QWen2-72BInstruct (@AlibabaGroup ) 🌐, Nemotron-4-340B-Instruct (@nvidia ) ⚙, and Mixtral-8x22BInstruct-v0.1 (@MistralAI ) 🌪) that are at par with proprietary models! 📈

But top models like GPT-4 are still outperforming them in certain domains! 🔝💪

This led us to having domain-focused open-LLMs (DeepSeek-Coder-V2 for coding and math 👨💻➕, ChatQA 1.5 for conversational QA and retrieval-augmented generation (RAG) 💬🔍, and InternVL 1.5 for vision-language tasks 🖼🗣)

The challenge that ChatQA 2 focuses on is of context length and RAG! 📏🔗

These are the two capabilities essential for LLMs to process large volumes of information that cannot fit into a single prompt and are complementary to each other, depending on the downstream tasks and computational budgets. 🧩📊

The solution is a detailed continued training recipe to extend the context window of Llama3-70B-base from 8K to 128K tokens, along with a three-stage instruction tuning process to enhance the model's instruction-following, RAG performance, and long-context understanding capabilities. 🔄🔧

📄 Paper: https://huggingface.co/papers/2407.14482

The interesting thing to notice from benchmarks was, how good QWen 2 is out of the box! 👏✨


Intelligence is all you need for roleplay.
Roleplay is overlooked as a special case of chain-of-thought, where context must be attended to and inferred state of the world and embodied minds must be persisted and evolved along credible narrative lines. LLMs are also being tasked to function as gamemasters. It's a challenging task which points to potential future benchmarks. The fact that the largest commercial LLMs are adept in generating text for roleplay intuitively implies that model intelligence is sufficient so long as it can generalize properly and pay attention to context without becoming confused.
This recent merge of mine composed using 3 academic fine-tunes, none of which were intended for roleplay, has survived the gauntlet of a Reddit post and appears to be a particularly strong 8B model when it comes to roleplay coherence.
https://huggingface.co/grimjim/llama-3-Nephilim-v3-8B (bf16 weights)
https://huggingface.co/grimjim/llama-3-Nephilim-v3-8B-GGUF (select quants)

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LivePortrait AI: Transform Static Photos into Talking Videos. Now supporting Video-to-Video conversion and Superior Expression Transfer at Remarkable Speed

A new tutorial is anticipated to showcase the latest changes and features in V3, including Video-to-Video capabilities and additional enhancements.

This post provides information for both Windows (local) and Cloud installations (Massed Compute, RunPod, and free Kaggle Account).

🔗 Windows Local Installation Tutorial ⤵
▶ https://youtu.be/FPtpNrmuwXk

🔗 Cloud (no-GPU) Installations Tutorial for Massed Compute, RunPod and free Kaggle Account ⤵
▶ https://youtu.be/wG7oPp01COg

The V3 update introduces video-to-video functionality. If you're seeking a one-click installation method for LivePortrait, an open-source zero-shot image-to-animation application on Windows, for local use, this tutorial is essential. It introduces the cutting-edge image-to-animation open-source generator Live Portrait. Simply provide a static image and a driving video to create an impressive animation in seconds. LivePortrait is incredibly fast and adept at preserving facial expressions from the input video. The results are truly astonishing.

With the V3 update adding video-to-video functionality, those interested in using LivePortrait but lacking a powerful GPU, using a Mac, or preferring cloud-based solutions will find this tutorial invaluable. It guides you through the one-click installation and usage of LivePortrait on #MassedCompute, #RunPod, and even a free #Kaggle account. After following this tutorial, you'll find running LivePortrait on cloud services as straightforward as running it locally. LivePortrait is the latest state-of-the-art static image to talking animation generator, surpassing even paid services in both speed and quality.

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Hi HF Community!🤗

As you may know, small language models like the SmolLM series have been on the rise recently: although it may not be completely fair to compare them with larger models, my thought  was that we could build a space where these SLMs could compete against each other in a chat arena, and here is what came out: https://huggingface.co/spaces/as-cle-bert/smolLM-arena 🚀
Even though there might be some little issues and hiccups due to GPU resources allocation, this space offers you the possibility to compare and play around with several Small Language Models, coming also with a leaderboard page (make sure to refresh it for the latest updates!)👑
Have fun!🍕


Introducing OpenCHAT mini: a lightweight, fast, and unlimited version of OpenGPT 4o.

https://huggingface.co/spaces/KingNish/OpenCHAT-mini2

It has unlimited web search, vision and image generation.

Please take a look and share your review. Thank you! 🤗

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Annotated History of Modern AI and Deep Learning (2212.11279)

Reading https://huggingface.co/papers/2212.11279 by Jürgen Schmidhuber.

From the abstract: "A modern history of AI will emphasize breakthroughs outside of the focus of traditional AI text books, in particular, mathematical foundations of today's NNs such as the chain rule (1676), the first NNs (linear regression, circa 1800), and the first working deep learners (1965-)."


You can now find the OBIS - Ocean Biodiversity Information System, on Hugging Face with 128M rows, via the Datasets package stream 🤗

The datasets are integrated, allowing seamless search and mapping by species name, higher taxonomic level, geographic area, depth, time, and environmental parameters. OBIS originates from the Census of Marine Life (2000-2010) and was adopted as a project under IOC-UNESCO’s International Oceanographic Data and Information (IODE) programme in 2009.

Collectively, they have provided over 45 million observations of nearly 120,000 marine species, ranging from bacteria to whales, from the surface to 10,900 meters depth, and from the tropics to the poles.

Link to the dataset: https://huggingface.co/datasets/louisbrulenaudet/obis


New Trends in LLM: Overview with Focus on xLLM

Read full article and download PowerPoint presentation at https://mltblog.com/3KqlNO7

If you ever wondered how xLLM is different from other LLM and RAG architectures, what are the foundational changes that make it appealing to fortune 100 companies, and what are the innovations being copied by competitors, read on. In this article, I share the latest trends and provide a high-level summary of xLLM, describing the ground-breaking technologies that make it unique, faster, and better for professional users and experts. In particular, I share my PowerPoint presentation on the topic.

Search is becoming hot again, this time powered by RAG and LLMs rather than PageRank.  New LLMs may not use transformers, and energy-efficient implementations are gaining popularity, with an attempt to lower GPU usage, and thus costs. Yet all but xLLM still rely on Blackbox neural networks.

Great evaluation metrics remain elusive and will remain so probably forever: in the end, LLMs, just like clustering, are part of unsupervised learning. Two users looking at a non-trivial dataset will never agree on what the “true” underlying cluster structure is. Because “true” is meaningless in this context. The same applies to LLMs. With some exceptions: when used for predictive analytics, that is, supervised learning, it is possible to tell which LLM is best in absolute terms (to some extent; it also depends on the dataset).


I can do Time Series Predictions with Swarm Algorithms! When all you know how to use is a hammer, everything looks like a nail. An LLM model is a hammer. It is not a deity. It has computational and mathematical limitations. Very big ones. Swarm Algorithms do not have this same problem. They are like a screwdriver. The screwdriver is not better than the hammer, both are useful. Why are LLM models bad at things like Time Series Predictions and Function Calls? Because those are jobs better fit for a screwdriver as opposed to a hammer.


SpreadsheetLLM: Encoding Spreadsheets for Large Language Models (2407.09025)

Remember when you had a few hundred rows of data that could easily be opened in Excel. 📊

Well, we are far from that with billion-parameter LLMs trained on trillions of tokens. 🌐

@Microsoft  wants to bridge that using "SpreadsheetLLM": Encoding Spreadsheets for Large Language Models. 🤖📈

While it sounds simple, Spreadsheets, with their extensive two-dimensional grids, various layouts, and diverse formatting options, present notable challenges for large language models (LLMs). 🚧

They initially propose a vanilla serialization approach that incorporates cell addresses, values, and formats. However, this approach is limited by LLMs' token constraints, making it impractical for most applications. ⛔

Solution... A SheetCompressor, an innovative encoding framework that compresses spreadsheets effectively for LLMs. 🔧

It comprises three modules:
1⃣ Structural-anchor-based compression
2⃣ Inverse index translation
3⃣ Data-format-aware aggregation

It significantly improves performance in spreadsheet table detection task, outperforming the vanilla approach by 25.6% in GPT4's in-context learning setting. 🏆

Sounds exciting, sadly no code, models OR datasets are released. 🙁

Moreover, there is a lot of research in encoding 2D position embeddings and this work has not been benchmarked against that! 🧐

Paper: https://huggingface.co/papers/2407.09025


Wanted to share some brief comparison of early training of the two-stage PixArt e-diffi pipeline.

On the left, we have the full stage 1 model generating all 50 steps on its own. This model is not trained at all on the final 400 timesteps of the schedule. On the right, we have the combined pipeline where stage 1 output is fed into stage 2.

Currently, the difference is rather minimal - but the small details are reliably improved.

In the watercolour example, the full generation (right side) has the texture of the watercolour paper, and the partial generation (left side) has a more flat digital art look to it.

For the blacksmith robot, the sparks emitted from the operation have a more natural blend to it. The robot's clothing appears to be undergoing some interesting transformation due to the undertrained state of the weights.

The medieval  battle image has improved blades of grass, settling dust particles, and fabric in the flag.

The stage 2 model being trained does not seem to resolve any global coherence issues despite having 400 steps in its schedule, but it still noticeably changes the local coherence, eg. the consistency of fabrics and metals can be improved through stage 2 fine-tuning.

The stage 1 model is the workhorse of the output, as expected with the 600 timesteps in its schedule. Additional fine-tuning of this model will improve the overall global coherence of the outputs. I wish I could say it will not impact fine details, but a lot of that does seem to be carried forward.

As noted, these models are undertrained due to a lack of compute. But they are a promising look toward what an e-diffi PixArt might be capable of.

Does anyone want to build this out fully with me?


Check this out on Poe
StableDiffusionXL Prompt Generator
: https://poe.com/sdpai


We Got a Job Offer in SECourses Discord Channel Related to AI (Stable Diffusion)

This is kind of announcement sharing. I think the offer looks decent.

For who doesn’t know our channel here : https://discord.com/servers/software-engineering-courses-secourses-772774097734074388

The job offer is in ai-related-job-offers sub-channel


I like training LoRAs

https://huggingface.co/blog/nroggendorff/create-diffusers-dataset

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Websites slam doors on AI data harvesting 🚪🔒

New study "Consent in Crisis: The Rapid Decline of the AI Data Commons" reveals a rapid decline in open web access.

Key findings from 14,000 web domains audit:
- +5% of three common data sets (C4, RefinedWeb and Dolma) now fully restricted, +25% of the highest-quality sources now fully restricted
- 45% of C4 restricted by Terms of Service

Noteworthy trends:
🚫🔄 OpenAI banned 2x more than any other company
📰🔐 News sites leading restrictions: 45% of tokens off-limits

Two quotes in the NYT piece to ponder:

“Unsurprisingly, we’re seeing blowback from data creators after the text, images and videos they’ve shared online are used to develop commercial systems that sometimes directly threaten their livelihoods.” — @yjernite

“Major tech companies already have all of the data. Changing the license on the data doesn’t retroactively revoke that permission, and the primary impact is on later-arriving actors, who are typically either smaller start-ups or researchers.” — @stellaathena

👉 Dive into the research: https://www.dataprovenance.org/consent-in-crisis-paper
👉 Read the NYT story: https://www.nytimes.com/2024/07/19/technology/ai-data-restrictions.html

#AIEthics #DataPrivacy


InSPyReNet Background Removal

I've built a space for fast background removal.

- https://huggingface.co/spaces/gokaygokay/Inspyrenet-Rembg

- https://github.com/plemeri/InSPyReNet

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First AI Journalism Lab cohort just wrapped - endless inspiration for newsrooms:
- Ludwig Siegele built an AI style checker for The Economist
- Rodney Gibbs created a tool helping small newsrooms analyze stories through user needs
- Monsur Hussain developed AI trend monitoring system for fact-checking WhatsApp claims
- David Cohn built a system for analyzing audience engagement
- Clare Spencer crafted video personas with AI

The insights on adoption during the discussion were fascinating - their approach really resonated with me. Instead of forcing AI tools onto teams, they emphasized getting skeptics involved early in testing and creating safe spaces for open discussion. Start small with enthusiastic participants, build a community of internal AI champions, and focus on solving specific problems rather than pushing for adoption.

As a coach, I also learned a lot. My 5 key takeaways:
- Newsrooms are bursting with AI x journalism innovation
- Internal alignment > technical challenges. Strong dev/PM relationships = magic
- Early prototyping + user involvement = better adoption. Set realistic expectations & embrace feedback
- Cross-newsroom collaboration supercharges innovation
- Great products can emerge in weeks with proper scoping

See the projects: https://www.youtube.com/watch?v=5PMxMDfDI_0&

Kudos to Kyle Plantz, Nikita Roy, Craig Newmark Graduate School of Journalism at CUNY for making it happen!


The most upvoted papers from the Chinese community on the Daily Papers - October🔥
https://huggingface.co/collections/zh-ai-community/trending-papers-october-6723995e5d93f8e480928b8c


xLLM: New Generation of Large Language Models for Enterprise

Read full article at https://mltblog.com/4ftTko9

In this article, you will find my PowerPoint presentation describing the most recent features of xLLM, a CPU-based, full context, secure multi-LLM with real-time fine-tuning & explainable AI. It includes several new diagrams describing the innovative architecture, upcoming developments, new features and different use cases.

Content

➡Enterprise use case: corporate corpus of a Fortune 100 company.
➡Original version dealing with large websites such as Wolfram and Wikipedia. Comparison with OpenAI.
➡xLLM for clustering and predictive analytics. Use case: unstructured text (articles) from a media company.
➡Integration of our game-changing NoGAN tabular data synthesizer, and state-of-the-art model evaluation technology.
➡Integration of external tools, for instance to solve math problems.
➡Upcoming version for auto-indexing and cataloging large repositories.
➡Demo: enterprise xLLM in action, featuring the modern user interface (full web API, not just a prompt box) with command menu and numerous options not found in other LLMs, including debugging, suggested prompts, choice of agents, and fine-tuning in real time.
➡Relevancy score displayed to the user, for each returned item. I call it the new PageRank for RAG/LLM, using a technology radically different from Google search. See picture.

New startup coming soon!

We will be launching soon (January) a new startup focusing on GenAI at scale for Enterprises; xLLM will be part of the offer with exclusive features. We are looking for early adopters to partner with us on the Journey. The co-founder and CEO, to be announced soon, is Senior Director of GenAI at a Fortune 100 company, where the first version of Enterprise xLLM was implemented. More to come!

Read more, and access the PPT, at https://mltblog.com/4ftTko9


🎓 Introducing PPT4Web Educational Materials Dataset - https://huggingface.co/datasets/nyuuzyou/ppt4web

Dataset highlights:
- 182,405 presentations from ppt4web.ru, a platform for storing and viewing presentations covering a wide range of educational materials
- Primarily in Russian, with content in English, Kazakh, Ukrainian, and Belarusian
- Each entry includes: URL, title, download URL, and filepath
- Contains original PPTX files (converted from PPT for consistency) in addition to metadata
- Data covers a broad spectrum of educational topics and subjects
- Dedicated to the public domain under Creative Commons Zero (CC0) license

The dataset can be used for analyzing educational presentation content across various subjects in multiple languages, text classification tasks, and information retrieval systems. It's particularly valuable for examining trends in education, teaching methodologies, and presentation materials used across different academic disciplines. The inclusion of original files allows for in-depth analysis of presentation formats and structures commonly used in educational settings, providing insights into the diverse range of subjects and teaching approaches.


remember boys and girls, always keep all your data, its never a waste of time!


allanctan-ai YoelRidgway prithivMLmods John6666 Haleshot davidaparicio AtAndDev clem


I have been seeing a specific type of AI hype more and more, I call it, releasing research expecting that no one will ever reproduce your methods, then overhyping your results. I test the methodology of maybe 4-5 research papers per day. That is how I find a lot of my research. Usually, 3-4 of those experiments end up not being reproduceable for some reason. I am starting to think it is not accidental.

So, I am launching a new series where I specifically showcase a research paper by reproducing their methodology and highlighting the blatant flaws that show up when you actually do this. Here is Episode 1!

https://www.youtube.com/watch?v=JLa0cFWm1A4


New Droppings🥳

😶🌫Collection: https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be

🥳Demo Here: https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC  with more than 100+ Flux LoRA's

🪨Fluid Dramatic Neon: https://huggingface.co/prithivMLmods/Castor-Dramatic-Neon-Flux-LoRA
🪨Past & Present Blend: https://huggingface.co/prithivMLmods/Past-Present-Deep-Mix-Flux-LoRA
🪨Tarot Cards Refreshed Themes: https://huggingface.co/prithivMLmods/Ton618-Tarot-Cards-Flux-LoRA
🪨Amxtoon Character Mix Real-Anime: https://huggingface.co/prithivMLmods/Ton618-Amxtoon-Flux-LoRA
🪨Epic Realism Flux v1: https://huggingface.co/prithivMLmods/Ton618-Epic-Realism-Flux-LoRA
🪨Mock-up Textures: https://huggingface.co/prithivMLmods/Mockup-Texture-Flux-LoRA
.
.
.
@prithivMLmods 🤗

Goekdeniz-Guelmez AtAndDev Aryanne rdrede kimleang123 darksfx hypergod s3nh


New feature 🔥
Image models and LoRAs now have little previews 🤏

If you don't know where to start to find them, I invite you to browse cool LoRAs in the profile of some amazing fine-tuners: @artificialguybr, @alvdansen, @DoctorDiffusion, @e-n-v-y, @KappaNeuro @ostris

GPT007 osanseviero ayush7 John6666 Blane187 bghira artificialguybr Rsln edpresque DoctorDiffusion cbensimon OmbelineM


Chameleon: Mixed-Modal Early-Fusion Foundation Models (2405.09818)

Chameleon 🦎 by Meta is now available in Hugging Face transformers 😍
A vision language model that comes in 7B and 34B sizes 🤩
But what makes this model so special?

Demo: https://huggingface.co/spaces/merve/chameleon-7b
Models: https://huggingface.co/collections/facebook/chameleon-668da9663f80d483b4c61f58

keep reading ⥥

Chameleon is a unique model: it attempts to scale early fusion 🤨
But what is early fusion?
Modern vision language models use a vision encoder with a projection layer to project image embeddings so it can be promptable to text decoder (LLM)

Early fusion on the other hand attempts to fuse all features together (image patches and text) by using an image tokenizer and all tokens are projected into a shared space, which enables seamless generation 😏

Authors have also introduced different architectural improvements (QK norm and revise placement of layer norms) for scalable and stable training and they were able to increase the token count (5x tokens compared to Llama 3 which is a must with early-fusion IMO)

This model is an any-to-any model thanks to early fusion: it can take image and text input and output image and text, but image generation are disabled to prevent malicious use.

One can also do text-only prompting, authors noted the model catches up with larger LLMs (like Mixtral 8x7B or larger Llama-2 70B) and also image-pair prompting with larger VLMs like IDEFICS2-80B (see paper for the benchmarks https://huggingface.co/papers/2405.09818)
Thanks for reading!

sumandas atahanuz Nymbo ucsahin GPT007 fdaudens osanseviero alkzar90 dblasko Svngoku gokaygokay mikestaub abdulbasit-nubytes


Sparse MoE (SMoE) has an unavoidable drawback: the performance of SMoE heavily relies on the choice of hyper-parameters, such as the number of activated experts per token (top-k) and the number of experts.

Also, identifying the optimal hyper-parameter without a sufficient number of ablation studies is challenging. As the size of the models continues to grow, this limitation could result in a significant waste of computational resources, and in turn, could hinder the efficiency of training MoE-based models in practice.

(READ MORE ↓↓↓) Now, our DynMoE addresses these challenges! 🙌 DynMoE incorporates:
(1) a novel gating method that enables each token to automatically determine the number of experts to activate.

(2) An adaptive process automatically adjusts the number of experts during training. Extensive numerical results across Vision, Language, and Vision-Language tasks demonstrate the effectiveness of our approach to achieve competitive performance compared to GMoE for vision and language tasks, and MoE-LLaVA for vision-language tasks, while maintaining efficiency by activating fewer parameters.

Our code is available at https://github.com/LINs-lab/DynMoE, also see the checkpoints at https://huggingface.co/collections/LINs-lab/dynmoe-family-665ed5a331a7e84463cab01a

surajkachate123 GPT007 osanseviero Ramikan-BR hllj danielus flflow kenshinn Yongxin-Guo


Since it is release season, at PleIAs we announce our first suite of specialized language models for document processing tasks (OCR correction, text segmentation, bibliographic extraction) and the release of the largest multimodal dataset of financial document Finance Commons: https://huggingface.co/blog/Pclanglais/finance-commons-bad-data-toolbox

LLM research is currently focused on quality data. We went on the opposite direction and voluntarily trained models on bad data. Far from degrading models, it made them more resilient to text sources commonly used in production.

Having a wider range of real life data proved critical for this project. A few months after the release of Common Corpus, we expanded our pool of "training data commons" with a major multimodal ressource: document released as open financial data.  Finance commons comprises 17 billion tokens and 1.25 PDF corporate documents released by the SEC, WTO, AMF, EU Tenders In a multiple languages with a large variety of document layouts and challenging sources to train more robust models.

With HuggingFace compute support, we release an entire pipeline to process bad data sources and make them usable in production for LLMOps or simply retrieval: https://huggingface.co/spaces/PleIAs/PleIAs-Editor

This approach is based on our new series of specialized models for document processing, the "bad data toolbox" comprising:
*OCRonos, the best available model to date for OCR correction. https://huggingface.co/PleIAs/OCRonos
*Segmentext, a pure semantic small model for text segmentation, working without any visual reference. https://huggingface.co/PleIAs/Segmentext
*Bibtexer, a small model for bibliographic data extraction acting as a "reversed-Zotero." https://huggingface.co/PleIAs/BibTexer


Since new TTS (Text-to-Speech) systems are coming out what feels like every day, and it's currently hard to compare them, my latest project has focused on doing just that.

I was inspired by the https://huggingface.co/spaces/TTS-AGI/TTS-Arena (definitely check it out if you haven't), which compares recent TTS system using crowdsourced A/B testing.

I wanted to see if we can also do a similar evaluation with objective metrics and it's now available here:
https://huggingface.co/spaces/ttsds/benchmark
Anyone can submit a new TTS model, and I hope this can provide a way to get some information on which areas models perform well or poorly in.

The paper with all the details is available here: https://arxiv.org/abs/2407.12707

ZeroWw osanseviero John6666 Gatozu35 ayymen Ramikan-BR Dihelson MarinaraSpaghetti mrfakename Korakoe kramp

krushnabhosle Gatozu35 Ramikan-BR Dihelson Korakoe


Thrilled to share some AI insights for journalism! 📊🤖

Just wrote a guest post on @ndiakopoulos's blog about Hugging Face on Sheets tool.

Why it matters:
🔌 Brings AI power directly to spreadsheets
📈 Huge potential for data journalism
🚫💻 No coding required!

If you've read Nicholas' "Automating the News" (a must-read!), you'll appreciate how this tool fits into the evolving landscape of AI in journalism.

Read here: https://generative-ai-newsroom.com/bringing-open-source-models-to-spreadsheets-c440fc4818b4

#AIJournalism #DataJournalism


🇷🇸 New Benchmark for Serbian Language 🇷🇸

@DjMel and I recently released a new benchmark for Serbian language that measures General Knowledge of LLMs. We had to parse over 20 years of university entrance exams for University of Belgrade, so the dataset is of high quality.

🥇 OAI models still hold the podium places with a significant gap compared to open-source models
🤔 https://huggingface.co/Qwen/Qwen2-7B-Instruct and https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct models show promising results considering they weren't trained on Serbian language
📈 Best open-source model seems to be https://huggingface.co/Stopwolf/Mustra-7B-Instruct-v0.2, a merge between https://huggingface.co/gordicaleksa/YugoGPT and https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
📉 Some models like https://huggingface.co/google/gemma-2-9b-it turned out to be a disappointment with random guessing-like accuracy

Take a look at the whole results at the dataset page:
https://huggingface.co/datasets/DjMel/oz-eval

P.S. If you have any constructive criticism or ideas for improvement, feel free to use dataset's Discussions page!


This is the week of small AI language models!

ZeroWw rumbleFTW ZachZimm osanseviero GPT007 chenduo jpacifico thesven AtAndDev Fredithefish

Bharath182004 YaTharThShaRma999 rumbleFTW osanseviero GPT007 jessicagab AtAndDev louisbrulenaudet Fredithefish


🚀 Just released version 0.24.0 of the 𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎_𝚑𝚞𝚋 Python library!

Exciting updates include:
⚡ InferenceClient is now a drop-in replacement for OpenAI's chat completion!

✨ Support for response_format, adapter_id , truncate, and more in InferenceClient

💾 Serialization module with a save_torch_model helper that handles shared layers, sharding, naming convention, and safe serialization. Basically a condensed version of logic scattered across safetensors, transformers , accelerate

📁 Optimized HfFileSystem to avoid getting rate limited when browsing https://huggingface.co/datasets/HuggingFaceFW/fineweb

🔨 HfApi & CLI improvements: prevent empty commits, create repo inside resource group, webhooks API, more options in the Search API, etc.

Check out the full release notes for more details:
https://huggingface.co/spaces/Wauplin/huggingface_hub/discussions/7
 👀

lhoestq plaguss clem Nymbo not-lain Goekdeniz-Guelmez


I have invented a method that is better than Diffusion. A company got a billion dollar valuation yesterday for less than what I am currently giving the world for free. I am starting to suspect that is the issue, I am giving it away for free. I meant it to be a gift to the world, but no one will even look at it. I am changing the licensing soon. It will no longer be free. At the moment, you can view exactly how Swarm Neural Networks can do everything Reverse Diffusion can do, for far less money. It can even make API calls.

SNN Image Generator: https://huggingface.co/spaces/TuringsSolutions/SNN-Image-Generator

SNN Function Caller (Controlled By TinyLlama): https://huggingface.co/spaces/TuringsSolutions/Qwen-2.0.5B-Swarm-Function-Caller

victor Augustindroid LeroyDyer Kkordik den0620 Joseph717171


#  Offensive Physical  Security Reconnaissance Planning Automation with public facing RTSP streams and Moondream

After some late night casual hacking about on VLMs for criminal attack vector reconnaissance automaton  experiments using Moondream (as usual)  based image-text-text with pre defined text prompts that are tuned for extracting weakness or customer identity and monitory  based   theft physical red team engagement reconnaissance and vector of malicious or criminal activity Working on a space. Thanks again for such a wonderful blessing of super power  image-text-to-text  model with minimal computational power needed @vikhyatk

I have started actually implementing a custom little tool with both static html space sand python gradio spaces  on the go which I shall share as  hf spaces when done  them.

---

https://huggingface.co/spaces/vikhyatk/moondream2

https://huggingface.co/vikhyatk/moondream2


Excited to share my "Focus Mode" Playlist, code name "The AI/ML Researcher's Playlist" :)

No lyrics, no beat, just a harmonious sequence of piano melody that will take you places beyond your reasoning/thinking prowess, trust me I’ve been there lol 🎹🎶

Thanks to an amazing pianist and composer on instagram @andreavanzo_composer who played all the songs in this playlist.

Currently have a total of 16 songs, I will keep adding more when I find them.

Full playlist: https://youtu.be/2ccxanKmzZY?si=x6weX2AgY5Zpadfw


GraphRAG-Ollama-UI

I've been working on a local version of Microsoft's GraphRAG that uses Ollama for everything. It's got a new interactive UI built with Gradio that makes it easier to manage data, run queries, and visualize results. It's not fully featured or set up to harness the entire GraphRAG library yet but it allows you to run all the standard commands for Indexing/Processing and chatting with your graph. Some key features:

Uses local models via Ollama for LLM and embeddings

3D graph visualization of the knowledge graph using Plotly

File management through the UI (upload, view, edit, delete)

Settings management in the interface

Real-time logging for debugging

https://github.com/severian42/GraphRAG-Ollama-UI

lucianosb Saugatkafley 200-cooller hubhamgupta AlbertSadday2891 Ruby-NewLeaf taufiqdp victor den0620


#ICLM 2024 is almost there 🔥🔥🔥 PM if you will be in Vienna next week, Glad to catchup with the  Hugging Face community there!

I would like to contribute 🎁 by releasing the sixth Knowledge Vault, with 100 lectures visualized from the last 10 years of ICML from 2014 to 2024, (10 from 2024 will be included after the conference) including knowledge graphs for all the Invited Lectures and some extras, with almost 3000 topics represented using AI.

You can explore it here:
🌏 https://theendofknowledge.com/Vaults/6/ICML-2015-2024.html

And you can learn more about the Vaults here:
📝https://www.linkedin.com/pulse/knowledge-vaults-david-vivancos-lbjef/

And previous Vaults relevant to the #huggingface community are:

🌏 [ @lexfridman 2018-2024 Interviews] https://theendofknowledge.com/Vaults/1/Lex100-2024.html

🌏 [ICLR 2014-2023] https://theendofknowledge.com/Vaults/2/ICLR2014-2023.html

🌏 [AIForGood 2017-2024] https://theendofknowledge.com/Vaults/4/AIForGood2017-2024.html

🌏 [CVPR 2015-2024] https://theendofknowledge.com/Vaults/5/CVPR-2015-2024.html

Hope you like them!

And great to see you all at #icml2024 @clem @thomwolf @julien-c and team

clem victor Rusvasul osanseviero louisbrulenaudet julien-c


Using the new viewer iframe support for the datasets viewer, I built a simple Space https://huggingface.co/spaces/davanstrien/collection_dataset_viewer to quickly explore all the datasets inside a collection.

The collection is loaded from an environment variable, so you can duplicate this Space to create a Space for exploring datasets in another collection!

Alexandro14 yjernite John6666 lucianosb Ramikan-BR Nymbo osanseviero


palmer-004 becomes 🔥turbo🔥  now is half the size, twice the speed and the best overall 0.5b language model in huggingface.

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


🤯 Ghost 8B Beta emerges as a clear leader, surpassing even proprietary models like xAI Grok 1, OpenAI GPT 3.5, and Mistral Mixtral 8x7B. This dominance extends to its parity with Mistral Medium, further solidifying its position as a top-tier language model. Furthermore, Ghost 8B Beta stands out as one of only three models employing the zero-shot method for evaluation, alongside Claude 2 and Claude 3, showcasing its unique capabilities and potential for groundbreaking applications.
---
💬 Chat with the model here:
- Playground with Ghost 8B Beta (β, 8k): https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k
- Playground with Ghost 8B Beta (β, 128k): https://huggingface.co/spaces/lamhieu/ghost-8b-beta-128k
- Official website: https://ghost-x.org/docs/models/ghost-8b-beta/

ZeroWw abidlabs tuanio reach-vb Dihelson Alexandro14 amosgyamfi cnmoro nicolollo GPT007 louisbrulenaudet


No on really pays attention to voyage ai , but probably you should 😉
https://huggingface.co/voyageai


Decensored Gemma2-27b

https://huggingface.co/TheDrummer/Big-Tiger-Gemma-27B-v1


Micrograd in pure C🤕
Port of Karpathy's micrograd in pure C.
Yo C does not negotiate with memory 😂
Code: https://github.com/Jaykef/micrograd.c

reach-vb jeffboudier abidlabs nbroad not-lain den0620 DIvAndrey


I've made a creative version of Tile Upscaler

- https://huggingface.co/spaces/gokaygokay/TileUpscalerV2

- https://github.com/gokayfem/Tile-Upscaler

- New tiling strategy
- Now it's closer to Clarity Upscaler
- It has more parameters to play and it has more room to fail because of that
- You should try different resolutions, strength and controlnet strength

Original Tile Upscaler
- https://huggingface.co/spaces/gokaygokay/Tile-Upscaler

reach-vb osanseviero jeffboudier abidlabs Blane187 louisbrulenaudet Alexandro14 John6666 HaiQinQin Wok Itsjustkev saxash jgitsolutions

John6666 reach-vb osanseviero abidlabs HaiQinQin kramp mah002 Wok


What an eventful day in Open Source LLMs today:

Mistral released Codestral Mamba 🐍
> Beats DeepSeek QwenCode, best model < 10B, competitive with Codestral 22B
> Mamba 2 architecture - supports up to 256K context
> Apache 2.0 licensed, perfect for local code assistant
> Transformers & llama.cpp integration upcoming!

Model checkpoint: https://huggingface.co/mistralai/mamba-codestral-7B-v0.1

Hugging Face dropped SmolLM 🤏
> Beats MobileLLM, Qwen 0.5B, Phi 1.5B and more!
> 135M, 360M, and 1.7B param model checkpoints
> Trained on 600B high-quality synthetic + FineWeb Edu tokens
> Architecture: Llama + GQA + 2048 ctx length
> Ripe for fine-tuning and on-device deployments.
> Works out of the box with Transformers!

Model checkpoints: https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966

Mistral released Mathstral 7B ∑
> 56.6% on MATH and 63.47% on MMLU
> Same architecture as Mistral 7B
> Works out of the box with Transformers & llama.cpp
> Released under Apache 2.0 license

Model checkpoint: https://huggingface.co/mistralai/mathstral-7B-v0.1

Pretty dope day for open source ML. Can't wait to see what the community builds with it and to support them further! 🤗

What's your favourite from the release today?

reach-vb Whiteshadow12 osanseviero dvilasuero louisbrulenaudet nawed JackCloudman ajibawa-2023 Alexandro14 tharrmeehan Mdubbya lucianosb WOOSAH nbroad


What could have been

"{{bos_token}}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '<|user|>\n' + message['content'] + '<|end|>\n' }}{% elif message['role'] == 'assistant' %}{{ '<|bot|>\n' + message['content'] }}{% if loop.last %}{{ '<|end|>' + eos_token }}{% else %}{{ '<|end|>\n' }}{% endif %}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"


𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬

𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬

𝐏𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬

𝐏𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬

Small models, BIG impact: SmolLM is here! 🚀🔬

We're launching a series of small but mighty language models:
🏎 Super fast - runs on laptops, phones, you name it!
📏 3 sizes: 130M, 350M, and 1.5B parameters
🥇 Outperforms same size models from Meta, Microsoft, and Qwen
🔓 Fully open-source: datasets, training code, models

𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬
- Trained on FineWeb-Edu and Cosmopedia v2 (largest synthetic pre-training dataset)
- No cloud needed - run locally for privacy and energy efficiency
- Everything is public, from data curation to training steps

𝐏𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬
- On-device autocomplete
- Local request parsing
- Custom fine-tuning for specific needs without the need for expensive GPUs

𝐆𝐨 𝐝𝐞𝐞𝐩𝐞𝐫
👉 Check it out: https://huggingface.co/collections/HuggingFaceTB/smollm-models-6695016cad7167254ce15966
👉 Run the 360M model in your browser, 100 % private: https://huggingface.co/spaces/HuggingFaceTB/SmolLM-360M-Instruct-WebGPU
👉 Read the blog explaining everything in detail: huggingface.co/blog/smollm

Kudos to the stellar team who worked on this project: @loubnabnl @anton-l @eliebak @lvwerra

YaTharThShaRma999 sgarbi sivan22 mdouglas nroggendorff John6666 reach-vb plaguss gabrielmbmb osanseviero jeffboudier AyoubChLin holooo raveninrhythm Ramikan-BR clem yjernite louisbrulenaudet

surfhb reach-vb osanseviero FaultyEntry ucsahin raveninrhythm Ramikan-BR clem yjernite


Cool things this week from @huggingface!

🌎AI math olympiad winner NuminaMath is here!
🤗Announcing New Hugging Face and Keras NLP integration
✨UI overhaul to HF tokens!
🧊 Embed our dataset viewer on any webpage!

https://huggingface.co/blog/winning-aimo-progress-prize
https://huggingface.co/blog/keras-nlp-integration
https://huggingface.co/settings/tokens
https://x.com/julien_c/status/1812099420726456457

Check out the full list on our discord! 👇
https://discord.com/invite/JfAtkvEtRb

reach-vb plaguss osanseviero jeffboudier not-lain yjernite louisbrulenaudet Blane187 apehex


The community Journalists on HuggingFace recently launched a tool (https://huggingface.co/spaces/JournalistsonHF/text-to-image-bias) to compare biases across several text-to-image models. I forked my own to evaluate the SDXL models I made.

## SinteticoXL Bias:

https://huggingface.co/spaces/lucianosb/sinteticoXL-bias


🔴⭐ New addition to the existing concept space! 🔴⭐

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

🚀 Tried the Duotone Canvas with the image generator. Unlike the duotone filter in the Canva app, which applies hue and tints in RGBA, this feature applies duotones based purely on the provided prompt to personalize the generated image.

🚀 These tones also work with the gridding option, which already exists in the space.

🚀 The application of tones depends on the quality and detail of the prompt given. The palette may be distorted in some cases.

🚀It doesn't apply like a hue or tint in RGBA (as shown in canva app below); it is purely based on the prompts passed.

🏞 Check out the space: https://huggingface.co/spaces/prithivMLmods/IMAGINEO-4K
🏜Collection: https://huggingface.co/collections/prithivMLmods/collection-zero-65e48a7dd8212873836ceca2

```
huggingface.co/spaces/prithivMLmods/IMAGINEO-4K
```
🏞What you can do with this space:
✅ Compose Image Grid
👉🏻 "2x1", "1x2", "2x2", "2x3", "3x2", "1x1"
✅ Apply styles
✅ Set up Image tones
✅ Apply filters & adjust quality

.
.
.
Thanks for reading!
- @prithivMLmods


Introducing Plugins in NiansuhAI (on July 20, 2024)

Plugin Names:
1. WebSearch: Tool for searching the web using search engines.
2. Calculator: Helps evaluate mathematical expressions; extends the base Tool class.
3. WebBrowser: Interacts with web pages to extract information or summarize content.
4. Wikipedia: Retrieves data from Wikipedia using its API.
5. Arxiv: Searches and fetches article information from Arxiv.
6. WolframAlphaTool: Answers questions on Math, Science, Technology, Culture, Society, and Everyday Life.

Similar to https://hf.co/chat

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IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans
  from Monocular Videos via Explicit Ray Tracing (2312.05210)

🚀🕺🌟 New Research Alert - CVPR 2024 (Avatars Collection)! 🌟💃🚀
📄 Title: IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing 🔝

📝 Description: IntrinsicAvatar is a method for extracting high-quality geometry, albedo, material, and lighting properties of clothed human avatars from monocular videos using explicit ray tracing and volumetric scattering, enabling realistic animations under varying lighting conditions.

👥 Authors: Shaofei Wang, Božidar Antić, Andreas Geiger, and Siyu Tang

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

🔗 Paper: https://huggingface.co/papers/2312.05210

🌐 Github Page: https://neuralbodies.github.io/IntrinsicAvatar/
📁 Repository: https://github.com/taconite/IntrinsicAvatar

📺 Video: https://www.youtube.com/watch?v=aS8AIxgVXzI

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

🚀 WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers

🚀 ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers

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

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

🔍 Keywords: #IntrinsicAvatar #InverseRendering #MonocularVideos #RayTracing #VolumetricScattering #3DReconstruction #MachineLearning  #ComputerVision #DeepLearning  #AI #CVPR2024

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- 6 languages: 🇨🇳 🇺🇸 🇯🇵 🇰🇷 🇩🇪 🇫🇷

- 6 languages: 🇨🇳 🇺🇸 🇯🇵 🇰🇷 🇩🇪 🇫🇷

Exciting news for audio AI enthusiasts! 🎙🌍

The Emilia dataset dropped last week, and it's a cool one:
- 101k+ hours of high-quality audio
- 6 languages: 🇨🇳 🇺🇸 🇯🇵 🇰🇷 🇩🇪 🇫🇷
- Diverse content: talk shows, interviews, debates, sports commentary, audiobooks

This dataset could improve multilingual speech generation and recognition. Opens up many possibilities for global media, language learning, and accessibility!

Explore it: https://huggingface.co/datasets/amphion/Emilia

#AIAudio

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🔥 🔥🔥
Excited to announce WizardLM new Paper: Auto Evol-Instruct!

🐦 Twitter: https://x.com/WizardLM_AI/status/1812857977122202087

📃 Paper: https://arxiv.org/pdf/2406.00770

🤖 1. Fully AI-Powered Pipeline

Auto Evol-Instruct automatically involves an iterative process of optimizing an Evol-Instruct V1 into an optimal one. The pipeline consists of two critical stages: Evol Trajectory Analysis, where the optimizer LLM analyzes the issues and failures exposed in instruction evolution performed by the evol LLM, and Evolving Method Optimization, where the optimizer LLM addresses these issues to progressively develop an effective evolving method. The optimal evolving method is then used to convert the entire instruction dataset into more diverse and complex forms, facilitating improved instruction tuning.

📈2. Scaling Evol-Instruct with Arena Learning

With Auto Evol-Instruct, the evolutionary synthesis data of WizardLM-2 has scaled up from WizardLM-1 to dozens of domains, covering tasks in all aspects of large language models. This allows Arena Learning to train and learn from an almost infinite pool of high-difficulty instruction data, fully unlocking all the potential of Arena Learning.

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🚀 Meet Our New Line of Efficient and Accurate Zero-Shot Classifiers! 🚀

The new architecture brings better inter-label understanding and can solve complex classification tasks at a single forward pass.

Key Applications:
✅ Multi-class classification (up to 100 classes in a single run)
✅ Topic classification
✅ Sentiment analysis
✅ Event classification
✅ Prompt-based constrained classification
✅ Natural Language Inference
✅ Multi- and single-label classification

https://huggingface.co/collections/knowledgator/gliclass-6661838823756265f2ac3848
https://huggingface.co/spaces/knowledgator/GLiClass_SandBox
https://huggingface.co/knowledgator/gliclass-base-v1.0-lw


@seyonec It's a great help to experiment with ChemBERTa.

BTW, There are several models that handle SMILES in the model repository. Can you kindly recommend the one with the best performance in handling hERG dataset?
https://paperswithcode.com/dataset/herg

Best,
Joo-Haeng Lee, Pebblous Inc.
http://pebblous.ai


@theroot srry i accidentally tried to join your group-of-one, pls ignore :) but ur work seems super interesting!


Updated https://huggingface.co/blog/nroggendorff/train-with-llama-architecture so you can "train" your own tokenizer from your dataset.


New model drop...🥁

FROSTING LANE REDUX

The v1 of this model was released during a big model push, so I think it got lost in the shuffle. I revisited it for a project and realized it wasn't inventive enough around certain concepts, so I decided to retrain.

https://huggingface.co/alvdansen/frosting_lane_redux

I think the original model was really strong on it's own, but because it was trained on fewer images I found that it was producing a very lackluster range of facial expressions, so I wanted to improve that.

The hardest part of creating models like this, I find, is maintaining the detailed linework without without overfitting. It takes a really balanced dataset and I repeat the data 12 times during the process, stopping at the last 10-20 epochs.

It is very difficult to predict the exact amount of time needed, so for me it is crucial to do epoch stops. Every model has a different threshold for ideal success.

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RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models (2407.06938)

🔥🎭🌟 New Research Alert - ECCV 2024 (Avatars Collection)! 🌟🎭🔥
📄 Title: RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models 🔝

📝 Description: RodinHD generates high-fidelity 3D avatars from portrait images using a novel data scheduling strategy and weight consolidation regularization to capture intricate details such as hairstyles.

👥 Authors: Bowen Zhang, @yiji, @chunyuwang, Ting Zhang, @jiaolong, Yansong Tang, Feng Zhao, Dong Chen, and Baining Guo

📅 Conference: ECCV, 29 Sep – 4 Oct, 2024 | Milano, Italy 🇮🇹

📄 Paper: https://huggingface.co/papers/2407.06938

🌐 Github Page: https://rodinhd.github.io/
📁 Repository: https://github.com/RodinHD/RodinHD

📺 Video: https://www.youtube.com/watch?v=ULvHt7dZx-Q

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

🚀 WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers

🚀 ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers

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

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

🔍 Keywords: #RodinHD #3DAvatars #DiffusionModels #HighFidelity #PortraitTo3D #MachineLearning  #ComputerVision #DeepLearning  #AI #ECCV2024


Responsibly building AI also means knowing its impact on the environment and the hidden carbon costs associated with it🌱
If you're interested in the subject, you can check out my latest community article: https://huggingface.co/blog/as-cle-bert/is-ai-carbon-footprint-worrisome
Where I try to unravel AI's carbon footprint and potential solutions to reduce it🌻
Enjoy!🤗


Introducing: 'Synthetic Math Phi'! Literally just press a button, and receive 10 random math problems and 10 random answers, outputted in JSON format and ready to be fed back to an LLM model.

This app is inspired by the research in this research paper. Their findings show that in order to increase mathematical reasoning of models, we need to give them more math. Quality is kind of not the important factor (for math only). Not enough math exists currently to train the models, that is the bottleneck. So, press a button and generate some math for free!

MIT licensed, here is the Github: https://github.com/RichardAragon/Synthetic-Math-Phi


Check out this SD3 Inpainting space I've created:
https://huggingface.co/spaces/Kkordik/inpainting-sd3-sketch-pad


Large models are surprisingly bad storytellers.

I asked 8 LLMs to "Tell me a bedtime story about bears and waffles."

Claude 3.5 Sonnet and GPT-4o gave me the worst stories: no conflict, no moral, zero creativity.

In contrast, smaller models were quite creative and wrote stories involving talking waffle trees and bears ostracized for their love of waffles.

Here you can see a comparison between Claude 3.5 Sonnet and NeuralDaredevil-8B-abliterated. They both start with a family of bears but quickly diverge in terms of personality, conflict, etc.

I mapped it to the hero's journey to have some kind of framework. Prompt engineering can definitely help here, but it's still disappointing that the larger models don't create better stories right off the bat.

Do you know why smaller models outperform the frontier models here?

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🎉 The Ghost 8B Beta model outperforms prominent models such as Llama 3 8B Instruct, GPT 3.5 Turbo in the lc_winrate score. In addition, it also outperforms Claude 3 Opus, Claude 3 Sonnet, GPT-4, and Mistral Large when comparing the winrate score of AlpacaEval 2.0.

Ghost 8B Beta is a large language model developed with goals that include excellent multilingual support, superior knowledge capabilities, and cost-effectiveness. The model comes in two context length versions, 8k and 128k, along with multilingual function tools support by default.
The languages supported are 🇺🇸 English, 🇫🇷 French, 🇮🇹 Italian, 🇪🇸 Spanish, 🇵🇹 Portuguese, 🇩🇪 German, 🇻🇳 Vietnamese, 🇰🇷 Korean and 🇨🇳 Chinese.

Explore the Potential:
To learn more about this groundbreaking language model, visit the official website or explore the online demo platforms:
- Ghost 8B Beta (β, 8k) on Spaces: https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k.
- Ghost 8B Beta (β, 128k) on Spaces: https://huggingface.co/spaces/lamhieu/ghost-8b-beta-128k
- Official website: https://ghost-x.org/docs/models/ghost-8b-beta

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Use GPT-4, GPT-4 Turbo Preview, GPT-3.5 Turbo, BingAI, and other models. The interface is similar to ChatGPT, with a speedy API endpoint.

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


Announcing the second open model in our Aura series of media models at @fal: https://hf.co/fal/AuraFlow

Try it using diffusers or ComfyUI from publicly available weights, and read more about it in our blog https://blog.fal.ai/auraflow.

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Hello!

I've been in the lab synthesizing captions, with my trusty sidekick Blip, and along the way I had an interesting idea. I thought of designing an incredibly simple model that accepts simple instruction pairs, adjective noun pairs specifically, and outputs 2d vertices.

The current implementation has been implemented by myself then ran over with Claude, not because I am incompetent, but because I recognize tools written by experts may have more technique than my newbie self.

As with all projects, this will be updated with proportion to the feedback received, if someone's using it and wants to keep using it, i'm happy to keep working on anything. Thanks, all! 🤗

`-<3`

https://colab.research.google.com/gist/SMeyersMrOvkill/8d4686db803f6c5f43fafc1c94b1c8c6/polypathdelement.ipynb


Started a list of LLMs used to make HackerNoon happen:
https://huggingface.co/collections/HackerNoon/models-used-in-hackernoon-publishing-system-668c56a0d10c3be5d338b805


🚀 I'm excited to announce that huggingface_hub's InferenceClient now supports OpenAI's Python client syntax! For developers integrating AI into their codebases, this means you can switch to open-source models with just three lines of code. Here's a quick example of how easy it is.

Why use the InferenceClient?
🔄 Seamless transition: keep your existing code structure while leveraging LLMs hosted on the Hugging Face Hub.
🤗 Direct integration: easily launch a model to run inference using our Inference Endpoint service.
🚀 Stay Updated: always be in sync with the latest Text-Generation-Inference (TGI) updates.

More details in https://huggingface.co/docs/huggingface_hub/main/en/guides/inference#openai-compatibility

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Have you looked at the Gemini Nano local LLM?
Gradio-Lite, the in-browser ver. of Gradio, gives it a rich interface using only Python code, even for such an in-browser AI app!

Try out a chat app that runs completely inside your browser 👇
https://www.gradio.app/playground?demo=Hello_World&code=IyBOT1RFOiBHZW1pbmkgTmFubyBtdXN0IGJlIGVuYWJsZWQgaW4geW91ciBicm93c2VyLiBTZWUgYXJ0aWNsZXMgbGlrZSBodHRwczovL3dyaXRpbmdtYXRlLmFpL2Jsb2cvYWNjZXNzLXRvLWdlbWluaS1uYW5vLWxvY2FsbHkKaW1wb3J0IGdyYWRpbyBhcyBncgpmcm9tIGpzIGltcG9ydCBzZWxmICAjIFB5b2RpZGUgcHJvdmlkZXMgYWNjZXNzIHRvIHRoZSBKUyBzY29wZSB2aWEgYGpzYCBtb2R1bGUuIFNlZSBodHRwczovL3B5b2RpZGUub3JnL2VuL3N0YWJsZS91c2FnZS9hcGkvcHl0aG9uLWFwaS5odG1sI3B5dGhvbi1hcGkKCiMgSW5pdGlhbGl6ZSBQcm9tcHQgQVBJCnNlc3Npb24gPSBOb25lCnRyeToKICAgIGNhbl9haV9jcmVhdGUgPSBhd2FpdCBzZWxmLmFpLmNhbkNyZWF0ZVRleHRTZXNzaW9uKCkKICAgIGlmIGNhbl9haV9jcmVhdGUgIT0gIm5vIjoKICAgICAgICBzZXNzaW9uID0gYXdhaXQgc2VsZi5haS5jcmVhdGVUZXh0U2Vzc2lvbigpCmV4Y2VwdDoKICAgIHBhc3MKCgpzZWxmLmFpX3RleHRfc2Vzc2lvbiA9IHNlc3Npb24KCgphc3luYyBkZWYgcHJvbXB0KG1lc3NhZ2UsIGhpc3RvcnkpOgogICAgc2Vzc2lvbiA9IHNlbGYuYWlfdGV4dF9zZXNzaW9uCiAgICBpZiBub3Qgc2Vzc2lvbjoKICAgICAgICByYWlzZSBFeGNlcHRpb24oIkdlbWluaSBOYW5vIGlzIG5vdCBhdmFpbGFibGUgaW4geW91ciBicm93c2VyLiIpCgogICAgc3RyZWFtID0gc2Vzc2lvbi5wcm9tcHRTdHJlYW1pbmcobWVzc2FnZSkKICAgIGFzeW5jIGZvciBjaHVuayBpbiBzdHJlYW06CiAgICAgICAgeWllbGQgY2h1bmsKCgpkZW1vID0gZ3IuQ2hhdEludGVyZmFjZShmbj1wcm9tcHQpCgpkZW1vLmxhdW5jaCgp

Note: Gemini Nano is currently only available on Chrome Canary, and you need to opt-in.
Follow the "Installation" section in https://huggingface.co/blog/Xenova/run-gemini-nano-in-your-browser .


RegMix: Data Mixture as Regression for Language Model Pre-training (2407.01492)

Still following your human intuition to mix corpora from different sources for pre-training 🧠? Everyone says that data mixture has a big impact on model performance, but how - and why🕵? Did you know that web corpora are actually highly impactful for downstream tasks 🏆?

Check out our preprint "RegMix: Data Mixture as Regression for Language Model Pre-training" 📄

🔬 In this paper, we've proposed an automatic data mixture method RegMix that achieves a 6.3% improvement over human selection on the widely used HellaSwag benchmark - and it only needs a 2% extra training FLOPs! 📈

📄 Paper: https://huggingface.co/papers/2407.01492
💻 Code: https://github.com/sail-sg/regmix
📊 Collection: https://huggingface.co/collections/sail/regmix-data-mixture-as-regression-6682b6caab37b9442877f0ce
 🎮 Demo: https://huggingface.co/spaces/sail/RegMix


Hi everyone 🤗

You can easily use the new Model Explorer from Google (https://github.com/google-ai-edge/model-explorer) here: https://huggingface.co/spaces/1aurent/model-explorer

Unfortunately, it doesn't look like it can easily be preloaded with some models, so you'll have to bring your own. Here is a quick selection of models you can use:
- https://huggingface.co/openai-community/gpt2/blob/main/64-8bits.tflite
- https://huggingface.co/qualcomm/ResNet50/blob/main/ResNet50.tflite
- https://huggingface.co/qualcomm/VIT/blob/main/VIT.tflite