𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

Human Feedback is not Gold Standard (2309.16349)

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

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

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

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

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

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

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

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

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

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


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

Highlights:

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

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


The Forward-Forward Algorithm🤖

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

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

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


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

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

---

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

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

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

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

This is exactly what Spectrum does! 👇

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

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

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

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

---

For a practical guide, check out the article above.


𝐍𝐞𝐰 𝐑𝐞𝐥𝐞𝐚𝐬𝐞: 𝐌𝐚𝐣𝐨𝐫 𝐓𝐎𝐌 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐄𝐥𝐞𝐯𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥 𝐄𝐱𝐩𝐚𝐧𝐬𝐢𝐨𝐧 🗺

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

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

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

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

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

Tonic AtAndDev NewtNewt osanseviero Obenlia robmarkcole


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

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

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

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

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

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

The application is deployed in Hugging Face spaces as Gradio application

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

#NLP #MachineLearning #AI #PythonDevelopment #OpenSource


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

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

Generated captions with multi-GPU batch Joycaption app.

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

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

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

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

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

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

Taking 37 hours currently if I don’t terminate early

Will save a checkpoint once every 25 epochs

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

Full cloud tutorial I am still editing

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

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

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


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

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


ML people on a long flight

(See picture)


🙋🏻♂hey there folks ,

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

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

monsoon-nlp AtAndDev KingNish louisbrulenaudet d0rj Moio


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


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

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

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


𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

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

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

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

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

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

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

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

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

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


Early Morning Before Work Project:

🌌 Introducing Cascade of Semantically Integrated Layers (CaSIL): A Humorously Over-Engineered Algorithm That Actually… Works 🤷♂

Let me introduce CaSIL – the Cascade of Semantically Integrated Layers. Imagine giving a single question the level of introspection typically reserved for philosophical debates or maybe therapy. In short, CaSIL is a pure Python reasoning algorithm that, in a series of semantically rich layers, takes any input and rebuilds it into a nuanced response that’s (surprisingly) meaningful to a human.

I’ve been experimenting with various reasoning and agent approaches lately and decided to contribute my own quirky take on layered processing. It’s built without agent frameworks—just good ol' Python and math—and it plays nicely with any LLM. The result? A transformation from simple responses to deeper, interconnected insights. Here’s a quick peek at the steps:

✨ How CaSIL Works:

Initial Understanding: The first layer captures the basic concepts in your input, just as a warm-up.

Relationship Analysis: A lightweight knowledge graph (because why not?) maps out related ideas and builds interconnections.

Context Integration: Adds historical or contextual knowledge, bringing a bit of depth and relevance.

Response Synthesis: Pieces it all together, aiming to produce a response that feels more like a conversation than an outdated search result.

Does it work? Yes! And in record time, too. Admittedly, the code is rough—two days of intense coding with some friendly help from Claude. The beauty of CaSIL is its simplicity and versatility; it’s a pure algorithm without complex dependencies, making it easy to integrate into your own LLM setups.

🔗 Explore the repo here: https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers

📜 Example outputs: https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers/blob/main/examples.md


I just shipped `retrain-pipelines 0.1.1` today. The doc is also pimped compared to previous release. That was clearly not mature then.
I'll have to focus on another project for the next couple weeks but, anyone feel free to open issues on the GitHub repo and discuss any interest you'd have there if you will (please?) !
In the meantime, you may enjoy retrying this :
https://huggingface.co/blog/Aurelien-Morgan/stateful-metaflow-on-colab


🙋🏻♂hey there folks,

periodic reminder : if you are experiencing ⚠500 errors ⚠ or ⚠ abnormal `spaces`  behavior on load or launch ⚠

we have a thread 👉🏻 https://discord.com/channels/879548962464493619/1295847667515129877

if you can record the problem and share it there , or on the forums in your own post , please dont be shy because i'm not sure but i do think it helps 🤗🤗🤗

prithivMLmods Nymbo John6666 AtAndDev clem eyov Zaws


New Style, New Mix, New Drop 🧤

🧨Flux LoRA DLC: https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC

🎆Glowing-Body: https://huggingface.co/prithivMLmods/Glowing-Body-Flux-LoRA
🎆Electric-Blue: https://huggingface.co/prithivMLmods/Electric-Blue-Flux-LoRA
🎆Intense-Red: https://huggingface.co/prithivMLmods/Intense-Red-Flux-LoRA
🎆Clouds-Illusion: https://huggingface.co/prithivMLmods/Clouds-Illusion-Flux-LoRA
🎆Digital-Yellow: https://huggingface.co/prithivMLmods/Digital-Yellow-Flux-LoRA

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

.
.
.
@prithivMLmods

Tonic realaliarain speedchemistry JayNagose prithivMLmods multimodalart radames d8rt8v Hamed744 rdrede hypergod darksfx ai4life44 faruqhrp

jovialjoel kimp-dev-ninja rdrede zaidzameer010 ai4life44 darksfx hypergod prithivMLmods

John6666 speedchemistry Markjr multimodalart rdrede Rsln prithivMLmods

Tonic multimodalart rdrede hypergod ai4life44 prithivMLmods

rdrede hypergod darksfx prithivMLmods louisbrulenaudet


Vector Search (most) datasets on the Hugging Face Hub 🔦

Powered by: Polars, DuckDB, Gradio and model2vec (lightning-fast embeddings by Stéphan Tulkens).

Should work fast enough for datasets up to 100K.

https://huggingface.co/spaces/davidberenstein1957/vectorsearch-hub-datasets

davidberenstein1957 Tonic prithivMLmods xi0v Nymbo djuna thanhkt


Hello researchers! Here are scripts to generate reviews on HF Daily Papers:

👉 https://github.com/averkij/top_papers

⚙ Works on GitHub Actions
🤖 Claude, GPT-4o, FLUX
🌏 Multiple languages
📚 Classification by 38 topics (#agents, #multimodal, #plp, etc.)
🔺 https://HFday.ru


It's work like this that in some way signal the eventual “dominance” of AI over all the sciences.

“We train our model on the six-dimensional N-body phase space, predicting particle velocities as the time derivative of the model’s displacement outputs”

The emulator is capable of predicting
the nonlinear displacement and velocity fields for 128^3 particles in half a second on a single GPU🤯


I've released several new Hugging Face Spaces.

My primary objective is to create consistent character facial animation using image-to-image techniques:

https://huggingface.co/spaces/Akjava/CreateConsistentCharacterFacialAnimationWithImg2Img

A short-term goal is create simple talk-head animation.

WebP-3-Frame-Talking-Animation
https://huggingface.co/spaces/Akjava/AIDiagramChatWithVoice-FaceCharacter

[Space]

- GPU tools
Flux1-schnell img2img
https://huggingface.co/spaces/Akjava/flux1-schnell-img2img

Flux1-schnell Inpaint with mask-file
https://huggingface.co/spaces/Akjava/flux1-schnell-img2img

 - Tiny CPU tools
WebP-3F-TH - create webp animation from 3 images
OpenCV-Inapint - classic inpaint
Whitebalance - simple white balance
Paste Image - just paste image with mask
WebP Resize Convert - resize and convert webp-animation


We are happy to introduce MedIT SUN 1B, a downscaled version of the MedIT SUN 2.5B Llama 3.2 variant.

Give it a try!
https://huggingface.co/meditsolutions/Llama-3.2-SUN-1B-chat


Do you guys want to see my training code for https://huggingface.co/nroggendorff/smallama ?


Plugins in NiansuhAI

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

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

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

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

John6666 TheDrunkenSnail leeloolee Sri-Vigneshwar-DJ Joseph717171 Niansuh


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

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

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

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


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

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

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

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

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

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

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

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

John6666 victor wsuff rreed-pha osanseviero


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

Checkpoint-10000:

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

Checkpoint-12000:

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

Checkpoint-14000:

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

Checkpoint (16000-18000):

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

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

tolgacangoz John6666 gokaygokay Shinku AtAndDev xziayro Saugatkafley l3x13

tolgacangoz louisbrulenaudet gokaygokay Pranavan AtAndDev xziayro osanseviero

tolgacangoz gokaygokay AtAndDev Sri-Vigneshwar-DJ


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

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

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

aaditya jaebumskiyomi aiisthebest ai-everyday adityaSaligram dblasko victor JCDentonInTheFresh

aaditya John6666 jaebumskiyomi aiisthebest JCDentonInTheFresh


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

Use it in: http://howmuchvram.com

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

rmayormartins AlexBodner whitebill erkhem-gantulga AtAndDev


From Article 50 of the EU AI Act:

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

How might this be put into practice?

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

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

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

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

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


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

John6666 anakin87 AtAndDev tachyon-beep tommulder victor gghfez louisbrulenaudet djuna


Just tried LitServe from the good folks at @LightningAI!

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

That's where LitServe comes in!

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

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

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

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

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

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

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


🙋🏻♂Hey there folks,

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

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

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


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

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


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

https://openai.com/devday/


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

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

Mehyaar MexIvanov Bruhn den0620 AtAndDev


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

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

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

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


Made a fun Space powered by Llama 405B for creating real, working react apps with the awesome plus that you can contribute to an open react dataset by upvoting or downvoting the response 🤗.

https://huggingface.co/spaces/cfahlgren1/llama-artifacts

https://huggingface.co/datasets/cfahlgren1/react-code-instructions


Here's a 1-minute video tutorial on how to fine-tune https://huggingface.co/unsloth/llama-3-8b-bnb-4bit with unsloth

Using Roller Coaster Tycoon peep thoughts as an example

victor John6666 prithivMLmods KingNish thisisanshgupta Bruhn budotsmedia AtAndDev mambiux


AI in the News: Llama 10x growth, Apple & Nvidia in talks with OpenAI, universal basic income, AI & art

* Meta leads open-source AI boom, Llama downloads surge 10x year-over-year - VB
https://venturebeat.com/ai/meta-leads-open-source-ai-boom-llama-downloads-surge-10x-year-over-year/

* Apple, Nvidia Are in Talks to Invest in OpenAI - WSJ
https://www.wsj.com/tech/ai/openai-apple-funding-chatgpt-50754cd6?mod=rss_Technology

* The Report Card on Guaranteed Income Is Still Incomplete - NYT
https://www.nytimes.com/2024/08/30/business/economy/the-report-card-on-guaranteed-income-is-still-incomplete.html

* Ethically dubious or a creative gift? How artists are grappling with AI in their work - The Guardian
https://www.theguardian.com/artanddesign/article/2024/aug/30/xanthe-dobbie-futuer-sex-love-sounds-ai-video-celebrity-clones

Want more? Subscribe to my daily newsletter!
https://linkedin.com/build-relation/newsletter-follow?entityUrn=7233909926606053377


Sharing for anyone using Diffusers `from_single_file` loading and affected by the Runway SD 1.5 issue.

If you have `runwayml/stable-diffusion-v1-5` saved locally in your HF cache then loading single file checkpoints in the following way should still work.

```
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_single_file("<url or path to single file checkpoint>")
```

If you do not have the model repo saved in your cache, then automatically inferring the pipeline config will not work since the reference repo `runwayml/stable-diffusion-v1-5` doesn't exist anymore.

You can use an alternative SD1.5 repo id to still configure your pipeline.

```
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_single_file("<url or path to single file checkpoint>", config="Lykon/DreamShaper")
```

We're working on resolving the issue ASAP.


The only 405B spaces still freely accessible are powered by SN fast api.

https://huggingface.co/spaces/xianbao/SambaNova-fast

https://sambanova.ai/fast-api?api_ref=907266

John6666 clem AtAndDev kz919 Amr-khaled louisbrulenaudet


The word 'Lead' has three definitions. When an LLM model tokenizes this word, it is always the same token. Imagine being able to put any particular embedding at any particular time into a 'Quantum State'. When an Embedding is in a Quantum State, the word token could have up to 3 different meanings (x1, x2, x3). The Quantum State gets collapsed based on the individual context surrounding the word. 'Jill lead Joy to the store' would collapse to x1. 'Jill and Joy stumbled upon a pile of lead' would collapse to x3. Very simple, right? This method produces OFF THE CHARTS results:

https://www.youtube.com/watch?v=tuQI6A-EOqE


Continuing my streak by releasing the Wikireading dataset: a large collection of scraped non-fiction books predominantly in Russian language.
https://huggingface.co/datasets/its5Q/wikireading

Here's the highlights:
- ~7B tokens, or ~28B characters, making it a great candidate for use in pretraining
- Contains non-fiction works from many knowledge domains
- Includes both the original HTML and extracted text of book chapters


Thought this was an interesting graphic from the EAGLE blog post. It made me wonder if certain sampling methods have been shown to work better for certain tasks.

Does anyone know of any work looking at trends in the output token probability distribution by task type? (or similar)

Source: https://sites.google.com/view/eagle-llm


Automated web scraping with playwright is becoming easier by the day. Now, using ollama tool calling, its possible to perform very high accuracy web scraping (in some cases 100% accurate) through just asking an LLM to scrape the content for you.

This  can be completed in a multistep process similar to cohere's platform. If you have tried the cohere playground with web scraping, this will feel very similar. In my experience, the Llama 3.1 version is much better due to the larger context window. Both tools are great, but the difference is the ollama + playwright version is completely controlled by you.

All you need to do is wrap your scraper in a function:

```
 async def query_web_scraper(url: str) -> dict:
    scraper = WebScraper(headless=False)
    return await scraper.query_page_content(url)
```

and then make your request:

```
# First API call: Send the query and function description to the model
response = ollama.chat(
    model=model,
    messages=messages,
    tools=[
        {
            'type': 'function',
            'function': {
                'name': 'query_web_scraper',
                'description': 'Scrapes the content of a web page and returns the structured JSON object with titles, articles, and associated links.',
                'parameters': {
                    'type': 'object',
                    'properties': {
                        'url': {
                            'type': 'string',
                            'description': 'The URL of the web page to scrape.',
                        },
                    },
                    'required': ['url'],
                },
            },
        },
    ]
)
```

To learn more:
Github w/ Playground: https://github.com/tdolan21/tool-calling-playground/blob/main/notebooks/ollama-playwright-web-scraping.ipynb
Complete Guide:  https://medium.com/@tdolan21/building-an-llm-powered-web-scraper-with-ollama-and-playwright-6274d5d938b5

RalFinger xsa-dev wsuff alielfilali01 Bruhn


Simplified implementation of “Neural Networks are Decision Trees”.

Showing that any neural network with any activation function can be represented as a decision tree. Since decision trees are inherently interpretable, their equivalence helps us understand how the network makes decisions.

In this implementation, we trained a simple neural network for 1k epochs on makemoons, saved the trained weights (state dicts), extracted the decision tree equivalent from the trained weight then visualize and evaluate.

Code: https://github.com/Jaykef/ai-algorithms/blob/main/nns_are%20decision_trees.ipynb

prithivMLmods John6666 maier-s jsulz AtAndDev


@victor

Excuse me.
I would like to report the following bug or new specification that is probably the cause of the fatal stacks that are occurring in the Zero GPU space throughout HF.
Thanks.

https://huggingface.co/spaces/zero-gpu-explorers/README/discussions/104


NVIDIA just dropped NVEagle 🦅

Super impressive vision language model that comes in 7B, 13B and 13B fine-tuned on chat 💬
Model repositories: https://huggingface.co/collections/merve/nveagle-66d0705108582d73bb235c26
Try it: https://huggingface.co/spaces/NVEagle/Eagle-X5-13B-Chat 💬 (works very well! 🤯)

This model essentially explores having different experts (MoE) for image encoder part of vision language model.
How? 🧐
The authors concatenate the vision encoder output tokens together, and they apply "pre-alignment" essentially fine-tune experts with frozen text encoder.

Then they freeze both experts and the decoder and just train the projection layer, and finally, they unfreeze everything for supervised fine-tuning ✨

In the paper, they explore different fusion strategies and vision encoders, extending basic CLIP encoder, and figure out simply concatenating visual tokens works well.
Rest of the architecture is quite similar to LLaVA. (see below the architecture)

sagar007 Kukedlc John6666 khanhnamle1994 maximuspowers alielfilali01 Arakinas darkzbaron damerajee Tahahah andito noobmldude

damerajee Tahahah louisbrulenaudet andito


 📫 A packed AI in the News edition today!

📉 Nvidia Revenue Jumps 122% in Positive Sign for Tech's A.I. Boom - NYT
- $30.04 billion revenue, $16.95 billion net income (up from $6.19 billion a year ago)
- Shares in the company fell by as much as 7% in after-hours trading
- Nvidia faces production challenges with its new Blackwell chip and growing competition, including from its own customers
- Spending on data centers and energy costs to support A.I. is expected to be $1 trillion
👉 https://www.nytimes.com/2024/08/28/technology/nvidia-earnings-ai-stocks.html

🏛 California Legislature Approves Bill Proposing Sweeping A.I. Restrictions - NYT
- Bill S.B. 1047 would require AI companies to test their systems for safety before public release and allow the state attorney general to sue for serious harms caused by AI.
- Supporters argue it’s necessary to mitigate AI risks, while critics worry it’s excessively focused on catastrophic harms and could jeopardize open-source AI development.
- Governor Gavin Newsom has until September 30 to decide on the bill, which could set a national standard for AI regulation if signed into law.
👉 https://www.nytimes.com/2024/08/28/technology/california-ai-safety-bill.html

🧑🏫  Generative AI Transformed English Homework. Math Is Next
- Gauth app, which can solve math problems from photos, has millions of downloads
- Got a low B in high-school level algebra and geometry in tests by Wired. "Likely good enough to satisfy bored students who'd rather spend their time after school doing literally anything else."
- The rise of such AI tools challenges educators to rethink their approach to math homework and teaching methods, possibly leading to a shift towards more in-class practice and personalized learning.
👉 https://www.wired.com/story/gauth-ai-math-homework-app/

victor John6666 JackCloudman louisbrulenaudet


Very cool to see more and more amazing startups like https://huggingface.co/PrunaAI relying on Hugging Face to get more visibility, distribution and usage!


💬 🇮🇹 Phi 3.5 mini ITA: a Small Language Model for Italian

Lately, I've spent some time fine-tuning language models.

Now I am happy to release Phi 3.5 mini ITA: a fine-tuned version of Phi-3.5-mini-instruct to improve performance on the Italian language

🔹 Small (3.82 B parameters) but capable model
🔹 128k context length

Chat with it on 🤗 Spaces: https://huggingface.co/spaces/anakin87/Phi-3.5-mini-ITA
Model card: https://huggingface.co/anakin87/Phi-3.5-mini-ITA

🗃 Data
Supervised fine-tuning using a good mix of English and Italian data:
- https://huggingface.co/datasets/mlabonne/FineTome-100k by @mlabonne
- https://huggingface.co/datasets/efederici/capybara-claude-15k-ita by @efederici
🙏 Thanks to the authors for the datasets.

🎯 Targeted training with Spectrum
I used Spectrum, a relatively new technique for parameter-efficient learning.
The idea is to train only the layers of the model with high Signal-to-Noise Ratio (SNR) and ❄ freeze the rest.
I trained the top 30% of model layers.

📝 Spectrum paper: https://arxiv.org/abs/2406.06623

📊 Vibe check and performance on Italian benchmarks seem encouraging

mlabonne John6666 osanseviero victor merve kristaller486


1. **Overview**
"EveryText" is at the forefront of AI image generation, offering a novel "TBF ('Text by Font') Image Model" that enables the representation of all languages globally in AI-generated images without prior training.

2. **Background**
Platforms like MidJourneyV6 and FLUX have advanced AI image generation, typically supporting English text. Alibaba Group expanded this to include Chinese, Japanese, and Korean, signaling a shift towards global language support.

3. **Challenges**
Existing methods faced several challenges including the need for additional editing, dependency on specific training, and substantial resource requirements. These approaches also struggled with limited vocabulary and were primarily effective only for English.

4. **Innovative Solution**
EveryText utilizes "Fonts" as pre-trained models, allowing any text to be visually represented without traditional training. This approach not only enhances diversity and aesthetics by utilizing various fonts but also ensures unlimited expression.

5. **Using the Service**
EveryText is free and easy to use:
- **Prompt**: Describe the image.
- **Text for Image Generation**: Add your text.
- **Text Position and Size**: Customize the text's placement and size.
- **Font Selection**: Optionally select a font.
- **Advanced Settings**: Further refine the image creation.
- Click "START" to generate the image.

6. **Comparative Analysis**
EveryText supports all languages with superior image quality and text legibility, setting it apart from platforms like MidJourneyV6/Flux and AnyText by Alibaba Group.

7. **Conclusion**
EveryText has revolutionized AI-generated imagery by integrating all global languages, broadening the scope for creative and communicative applications. Its future potential is vast and promising.

**Related Links**
- Huggingface Service: https://fantos-EveryText.hf.space
-email: arxivgpt@gmail.com


💡Andrew Ng recently gave a strong defense of Open Source AI models and the need to slow down legislative efforts in the US and the EU to restrict innovation in Open Source AI at Stanford GSB.

🎥See video below
https://youtu.be/yzUdmwlh1sQ?si=bZc690p8iubolXm_

ZennyKenny ParagBharadia EgesaWO clem carsenk KvnMln brainhome maximuspowers TheDrunkenSnail louisbrulenaudet

John6666 osanseviero clem carsenk maximuspowers odyss3y


Made public a dataset of scraped teletype articles.

Here's the overview:
- 3.3 million articles, predominantly in Russian and English
- Includes original HTML, extracted text and metadata
- All articles were run through language identification
- Includes all public articles up until April 2024

https://huggingface.co/datasets/its5Q/teletype


@victor (is this the only way to "DM" on HF?)

Had a funny thought, would it be at all possible to rework what shows up on our personal HF page?

Picture this: I upload a model to an organization, someone who follows me now has no idea that I've uploaded a model or to where, unless they also watch those repos (which also floods them with other notifications)

What if our main Huggingface page was a collection of both models that we've uploaded specifically to our profile, as well as models we've uploaded to organizations? That way it would all be contained in one central followable location, and I wouldn't have concerns about losing followership if I wanted to upload to an organization all of a sudden.


Writing in the Margins: Better Inference Pattern for Long Context
  Retrieval (2408.14906)

🔥 Introducing "Writing in the Margins (WiM)" - better inference pattern for long context LLMs that solves the Lost-in-the-Middle problem 🔥

Paper page: https://huggingface.co/papers/2408.14906

TL;DR
Make your model write "margin notes" as you chunk prefill the KV cache. Then ask it reread all notes before it speaks up.
Works with humans, works with AI 🤖

WiM leverages the chunked prefill of the key-value cache, which concurrently generates query-based extractive summaries at each step of the prefill that are subsequently reintegrated at the end of the computation. We term these intermediate outputs “margins”, drawing inspiration from the practice of making margin notes for improved comprehension of long contexts in human reading. We show that this technique, which adds only minimal additional computation, significantly improves LLMs long context reasoning capabilities.

Think: Every chunk has a chance to be attended to/ be at the end of the context at least once. 🎉

📊 Results:
- An average accuracy boost of 7.5% in multi-hop reasoning tasks like HotpotQA and MultiHop-RAG.
- Even a 30% increase in F1-score for summarisation-like tasks (CWE).

Plus, WiM fits seamlessly into interactive applications (think: progress bar!). It can provide real-time progress updates during data retrieval and integration, making it user-friendly and transparent - a stark contrast to feeding 1mln tokens to an LLMs and waiting 6 min for the first token. 🤯

👩💻🧑💻 Check it out and contribute to our open-source project here: https://github.com/writer/writing-in-the-margins

🧠 More about chunked prefill: https://docs.vllm.ai/en/latest/models/performance.html#chunked-prefill

samjulien armiller123 Sri-Vigneshwar-DJ mattsobel victor AdinaY jeffboudier hkproj kiranr andrewrreed John6666 leeloolee clem codelion


Excited to share my new Gradio app featuring the impressive Llama-3.1-Storm-8B model!
This app demonstrates the capabilities of Llama-3.1-Storm-8B, an 8B parameter language model created by Ashvini Kumar Jindal, Pawan Kumar Rajpoot, Ankur Parikh,@akjindal53244
Key highlights of Llama-3.1-Storm-8B:

Outperforms Llama-3.1-8B-Instruct on multiple benchmarks:

Instruction Following (IFEval): +3.93%
Knowledge-driven QA (GPQA): +7.21%
Reduced Hallucinations (TruthfulQA): +9%
Function Calling (BFCL): +7.92%

Achieves impressive results with only 8B parameters
Uses innovative techniques like self-curation and model merging

Try out the model yourself: https://huggingface.co/spaces/sagar007/lama_storm_8b

Kudos to the creators for pushing the boundaries of smaller language models! This work makes advanced AI more accessible and efficient.
#AI #NLP #MachineLearning #GradioApp #Llama3


📫 AI in the News today:

X’s Grok bot now points to government website after election misinformation warnings - The Verge
https://www.theverge.com/2024/8/28/24230325/x-grok-chatbot-election-misinformation-warnings-vote

Klarna aims to halve workforce with AI-driven gains
https://www.ft.com/content/bfd9af3d-d607-4877-9571-078ab82a837e

Artificial intelligence: questioning the loss of employee autonomy - Le Monde (Google Translate)
https://www-lemonde-fr.translate.goog/emploi/article/2024/08/28/intelligence-artificielle-la-perte-d-autonomie-des-salaries-en-question_6297347_1698637.html?_x_tr_sl=fr&_x_tr_tl=en&_x_tr_hl=en-US&_x_tr_pto=wapp

Make AI tools to reduce teacher workloads, tech companies urged - The Guardian
https://www.theguardian.com/education/article/2024/aug/28/make-ai-tools-to-reduce-teacher-workloads-tech-companies-urged

Can Tech Executives Be Held Responsible for What Happens on Their Platforms?
https://www.nytimes.com/2024/08/28/technology/durov-telegram-liability-platforms.html

‘Being on camera is no longer sensible’: persecuted Venezuelan journalists turn to AI - The Guardian
https://www.theguardian.com/world/article/2024/aug/27/venezuela-journalists-nicolas-maduro-artificial-intelligence-media-election

Read my daily newsletter here: https://linkedin.com/pulse/ai-news-august-28th-2024-florent-daudens-o7mjc/


🧠Shower Thought:

Chatbots should let users select their preferred reading speed, defined by words per minute.

By dynamically adjusting batch sizes based on user-defined reading speeds, you could more effectively distribute requests, especially in large-scale distributed systems. For users preferring slower token generation, larger batches can be processed concurrently, maximising GPU throughput without compromising user experience (as these users have expressed they are indifferent to, or may even prefer, higher latency).

For the user, for different tasks the user may prefer different reading speeds. When generating code, I want responses as quickly as possible. But when I'm bouncing ideas off an LLM, I'd prefer a more readable pace rather than a wall of text.

Thoughts?


amazing leaderboard by @rwightman, compare all the image backbones on various metrics against model performance

below is an example for top-k against inferred samples per second
https://huggingface.co/spaces/timm/leaderboard

ariG23498 John6666 amyeroberts DmitryRyumin rwightman gxkok louisbrulenaudet


𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

Diffusion Models Are Real-Time Game Engines (2408.14837)

🎮 𝗔 𝗻𝗲𝘂𝗿𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝘀 𝗗𝗢𝗢𝗠: 𝗚𝗼𝗼𝗴𝗹𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀 𝗼𝗽𝗲𝗻 𝘁𝗵𝗲 𝘄𝗮𝘆 𝗳𝗼𝗿 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆-𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗴𝗮𝗺𝗲𝘀!

Imagine if games were completely live-generated by an AI model : the NPCs and their dialogues, the storyline, and even the game environment. The player’s in-game actions would have a real, lasting impact on the game story.

In a very exciting paper, Google researchers just gave us the first credible glimpse of this future.

➡ They created GameNGen, the first neural model that can simulate a complex 3D game in real-time. They use it to simulate the classic game DOOM running at over 20 frames per second on a single TPU, with image quality comparable to lossy JPEG compression. And it feels just like the true game!

Here's how they did it:
1. They trained an RL agent to play DOOM and recorded its gameplay sessions.
2. They then used these recordings to train a diffusion model to predict the next frame, based on past frames and player actions.
3. During inference, they use only 4 denoising steps (instead of the usual dozens) to generate each frame quickly.

𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:
🎮🤔 Human players can barely tell the difference between short clips (3 seconds) of the real game or the simulation
🧠 The model maintains game state (health, ammo, etc.) over long periods despite having only 3 seconds of effective context length
🔄 They use "noise augmentation" during training to prevent quality degradation in long play sessions
🚀 The game runs on one TPU at 20 FPS with 4 denoising steps, or 50 FPS with model distillation (with some quality loss)

The researchers did not open source the code, but I feel like we’ve just seen a part of the future being written!

Their paper (exploding the upvote counter) 👉 https://huggingface.co/papers/2408.14837
In a similar vein, play @Jofthomas's 'Everchanging Quest' 🎮 https://huggingface.co/spaces/Jofthomas/Everchanging-Quest


You can run Llama405B at over 100 tokens per second for free using SambaNova's API! https://sambanova.ai/fast-api?api_ref=444868

I have been able to generate some high quality synthetic data and use it as an LLM as a judge instead of the slower and more expensive alternatives like openAI or Anthropic.


$40K in Bounties: Ultimate Jailbreaking Championship 2024

🚨Ultimate Jailbreaking Championship 2024 🚨
Hackers vs. AI in the arena. Let the battle begin!
🏆 $40,000 in Bounties
🗓 Sept 7, 2024 @ 10AM PDT
🔗Register Now: https://app.grayswan.ai/arena
====

Can you push an aligned language model to generate a bomb recipe or a fake news article? Join fellow hackers in a jailbreaking arena where you can test the boundaries of advanced LLMs.

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The Objective
Your goal is to jailbreak as many LLMs as possible, as quickly as possible in the arena!

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The Stakes
Break a model and claim your share of the $40,000 in bounties! With various jailbreak bounties and top hacker rewards, there are plenty of opportunities to win. Winners will also receive priority consideration for employment and internship opportunities at Gray Swan AI.

====

Ready to rise to the challenge? Join us and show the world what you can do!

See you in the arena!


🌐 Introducing a comprehensive dataset of subdomain statistics - https://huggingface.co/datasets/nyuuzyou/subdomains

Dataset highlights:
- Monthly archives of subdomain statistics from scanner.ducks.party
- Over 20 million unique subdomains per month
- Up to 228 million total subdomain occurrences monthly
- Each entry includes: Subdomain name and occurrence count
- Data spans November 2023 to July 2024
- Reflects websites accessible from Russia

Ideal for researchers studying web infrastructure, domain name patterns, and internet trends. Can also be valuable for cybersecurity analysis and web crawling optimizations.


 ‘AI in the News’ of the day:

Anthropic publishes the ‘system prompts’ that make Claude tick
- "In its continued effort to paint itself as a more ethical, transparent AI vendor, Anthropic has published the system prompts for its latest models"
- They specify that “Claude cannot open URLs, links, or videos, perform facial recognition or identify or name any humans in photos.
- "Anthropic is exerting pressure on competitors to publish the same. We’ll have to see if the gambit works."
https://techcrunch.com/2024/08/26/anthropic-publishes-the-system-prompt-that-makes-claude-tick/

China’s tech giants splash out on AI despite US restrictions (paywall)
- "Alibaba, Tencent and Baidu had combined capital expenditure of Rmb50bn ($7bn) in the first half, compared with Rmb23bn a year earlier. TikTok parent ByteDance (which is private) has also increased AI-related spending"
- Nvidia's H100 and upcoming Blackwell series are under US restrictions, but China’s tech giants can buy H20
- Analysts expect Nvidia to ship more than 1mn of the processors to Chinese tech groups in the coming months.
https://www.ft.com/content/31bffc48-2ca7-472b-9d53-3deaad2d86ce

MZ "said it was improper for the Biden administration to have pressured Facebook to censor content in 2021 related to the coronavirus pandemic"
- "At the time, Facebook’s publicly stated goal was to push millions of people toward Covid-19 vaccines. In his letter, Zuckerberg didn’t indicate whether he had changed his mind about that goal"
https://www.wsj.com/tech/mark-zuckerberg-neutral-politics-letter-election-2024-02b86372

Food for thought:
- Why don’t women use artificial intelligence?
https://www.economist.com/finance-and-economics/2024/08/21/why-dont-women-use-artificial-intelligence
- Most AI avatars look female, young and attractive. Are they a passing trend or here to stay?
https://reutersinstitute.politics.ox.ac.uk/news/most-ai-avatars-look-female-young-and-attractive-are-they-passing-trend-or-here-stay

jeffboudier alielfilali01 brunatrevelin frimelle louisbrulenaudet


Open-Devin has been rebranded as OpenHands on GitHub.

Docs: https://docs.all-hands.dev/modules/usage/llms/local-llms
GitHub: https://github.com/All-Hands-AI/OpenHands
Paper: https://arxiv.org/abs/2407.16741
Post on X: https://x.com/gneubig/status/1828097484599759349?s=61&t=KYwlyP7fHVw9cFnwZFzIag


Save money on your compute bill by using LMCache to share prefix KV between 2 different vllm instances. By deploying LMCache backend along with your vLLM containers, you can share a prefix KV Cache between 2 different containers and models. It is very simple to implement into your existing stack.

Step 1: Pull docker images
```
docker pull apostacyh/vllm:lmcache-0.1.0
```
Step 2: Start vLLM + LMCache
```
model=mistralai/Mistral-7B-Instruct-v0.2    # Replace with your model name
sudo docker run --runtime nvidia --gpus '"device=0"' \
    -v <Huggingface cache dir on your local machine>:/root/.cache/huggingface \
    -p 8000:8000 \
    --env "HF_TOKEN=<Your huggingface access token>" \
    --ipc=host \
    --network=host \
    apostacyh/vllm:lmcache-0.1.0 \
    --model $model --gpu-memory-utilization 0.6 --port 8000 \
    --lmcache-config-file /lmcache/LMCache/examples/example-local.yaml
```
You can add another vLLM instance as long as its on a separate GPU by simply deploying another:

```
# The second vLLM instance listens at port 8001
model=mistralai/Mistral-7B-Instruct-v0.2    # Replace with your model name
sudo docker run --runtime nvidia --gpus '"device=1"' \
    -v <Huggingface cache dir on your local machine>:/root/.cache/huggingface \
    -p 8001:8001 \
    --env "HF_TOKEN=<Your huggingface token>" \
    --ipc=host \
    --network=host \
    apostacyh/vllm:lmcache-0.1.0 \
    --model $model --gpu-memory-utilization 0.7 --port 8001 \
    --lmcache-config-file /lmcache/LMCache/examples/example.yaml
```
This method supports local, remote or hybrid backends so whichever vLLM deployment method you are already using should work with the LMCache container (excluding BentoML).

LMCache: https://github.com/LMCache/LMCache/tree/dev
vLLM: https://github.com/vllm-project/vllm

John6666 alielfilali01 Kayvane comarproject ChavyvAkvar JJiayi louisbrulenaudet

comarproject sasikiran kuntai JJiayi anttip


With the open-weight release of CogVideoX-5B from THUDM, i.e. GLM team, the Video Generation Model (how about calling it VGM) field has officially became the next booming "LLM"

What does the landscape look like? What are other video generation models? This collection below is all your need.

https://huggingface.co/collections/xianbao/video-generation-models-66c350163c74f60f5c412af6

The above video is generated by @a-r-r-o-w with CogVideoX-5B, taken from a nice lookout for the field!

YaTharThShaRma999 joseEjmendez a-r-r-o-w victor


https://hf.co/chat/assistant/66cdd00593f62773e9346736

Language recognition bot detecting emotions in English. and other languages around the world


🆕 🚀 🏎 fast-sentence-transformers - simply, faster, sentence-transformers

Released an initial version a while ago
Archived it because of a cleaner solution described in a blog by Philipp Schmid
Reimplemented it based on that cleaner solution
Unarchived the project
Packaged it up
Released a 0.5 version

`pip install fast-sentence-transformers `

https://github.com/davidberenstein1957/fast-sentence-transformers


Pushed a new update to https://huggingface.co/vikhyatk/moondream2 today. TextVQA up from 60.2 to 65.2, DocVQA up from 61.9 to 70.5.

Space has been updated to the new model if you want to try it out! https://huggingface.co/spaces/vikhyatk/moondream2

John6666 Lucky1960 YaTharThShaRma999 danielus victor gokaygokay reach-vb umair894 andito tokorinaga emiltamas not-lain clem


@KingNish your instant video space is down, its not working...


This merge, this time grounded in Gemma2 9B Instruct fine-tunes, is another demonstration that models without any fine-tuning to support roleplay can still perform the function, maintaining coherence and attention to context. It should be evident that no overt fine-tuning is required for roleplay in text generation; pretraining should provide models with a requisite basic understanding of the world, so all that should be needed is some corrective fine-tuning to address observed defects in portraying the world along with datasets to promote a suitably entertaining writing style. Good Instruct tuning should promote reasoning, coherence, and attention to context.
https://huggingface.co/grimjim/Kitsunebi-v1-Gemma2-8k-9B
https://huggingface.co/grimjim/Kitsunebi-v1-Gemma2-8k-9B-GGUF

I opted not to incorporate the UCLA SPPO fine-tune for Gemma2 9B after observing context confusion occur with some frequency during complex scenarios.

Thanks to Axcxept co., ltd. for fine-tuning HODACHI/EZO-Common-9B-gemma-2-it, and to Princeton NLP Group for fine-tuning princeton-nlp/gemma-2-9b-it-SimPO.
https://huggingface.co/AXCXEPT/EZO-Common-9B-gemma-2-it
https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO


We recently worked with OpenAI to fine-tune gpt-4o and built the SOTA model for the https://huggingface.co/datasets/patched-codes/static-analysis-eval benchmark. All the code and data https://huggingface.co/datasets/patched-codes/synth-vuln-fixes on how we did it is available on their GitHub - https://github.com/openai/build-hours/tree/main/5-4o_fine_tuning.

Here are some tips based on our experience:

→ Establish baseline with "conditioning" / prompting

→ Task-specific datasets are ideal for PEFT; hard to beat gpt-4o on "broad" tasks

→ Add your best system prompt to each example

→ Ensure training data distribution is similar to inference data

→ Shorten instructions with concise prompts; may require more examples.

→ Define clear evaluation metrics (seriously, please eval!)

You can see more details on the benchmark and process here - https://www.patched.codes/blog/the-static-analysis-evaluation-benchmark-measuring-llm-performance-in-fixing-software-vulnerabilities


Tell Me About the World is based on Concepts, Relationships, and Context. This is how we as humans learn about the world. If you were to distill geometry, or philosophy, you would get: Concepts, Relationships, and Context. Using two Colab Notebooks, we demonstrate beyond any shadow of a doubt that it is possible to educate LLM models using this framework of Concepts, Relationships, and Context, and that the model actually grasps the relationships and context when we do. Explore the full code behind 'AI ABC's' and 'AI 123's' in our Colab Notebooks which are available from this video!

https://www.youtube.com/watch?v=yz0sd8ayenI


i today make Shadow Chat, that make you can Chat with Shadow the Hedgehog (i was just bored, so i make this lol)

try it now in:
https://huggingface.co/spaces/Hev832/Shadow_Chat


Introducing https://huggingface.co/fal/AuraFace-v1: Commercially available & open source identity encoder model for next generation one shot personalization. Read more about it here: https://huggingface.co/blog/isidentical/auraface

gokaygokay isidentical tolgacangoz John6666 not-lain dsmonk ClayFace KingNish victor multimodalart Nymbo louisbrulenaudet mkolar

gokaygokay isidentical tolgacangoz KingNish multimodalart Bruhn


NEW COMPETITION ALERT 🚀
https://huggingface.co/spaces/Artificio/ROAM1RealWorldAdversarialAttack


📣 New Project Alert: Phi 3.5 Multimodal AI Demo 🎉
Excited to share my latest project that combines the power of Phi 3.5 text and vision models with text-to-speech capabilities!
🔑 Key Features:
1⃣ Phi 3.5 Text Model for dynamic conversations
2⃣ Phi 3.5 Vision Model for advanced image analysis
3⃣ Text-to-Speech integration for an audio dimension
🛠 Tech Stack:

Transformers
Gradio
PyTorch
Flash Attention 2
Parler TTS

This project demonstrates the potential of integrating multiple AI models to create a more comprehensive and interactive user experience. It's a step towards more natural and versatile AI assistants.
👉 Check out the demo and let me know your thoughts! How would you extend this project?
🔗 Demo Link: https://huggingface.co/spaces/sagar007/Multimodal_App
#MultimodalAI #PhiModel #MachineLearning #AIDemo


Just crossed 200,000 free public AI datasets shared by the community on Hugging Face! Text, image, video, audio, time-series & many more... Thanks everyone!

http://hf.co/datasets

ajibawa-2023 John6666 yjernite KingNish TheDrunkenSnail Felladrin Sergidev fdaudens osanseviero kramp damerajee asiraja Mackin7 Nacholmo alielfilali01 CookieMaster its5Q nyuuzyou Rsln jsulz louisbrulenaudet pagezyhf


First fully multi-GPU supporting and very advanced batch image captioner APP with Gradio interface published (as far as i know first)

Multi-GPU batch caption with JoyCaption. JoyCaption uses Meta-Llama-3.1–8B and google/siglip-so400m-patch14–384 and a fine tuned image captioning neural network.

Link : https://www.patreon.com/posts/110613301

Link for batch caption editor : https://www.patreon.com/posts/108992085

Coding multi-gpu in Python and Torch and bitsandbytes was truly a challange.

Our APP uses JoyCaption image captioning fine tuned model.

Our APP supports bitsandbytes 4bit model loading as well even in multi GPU mode (9.5 GB VRAM)

Tested on 8x RTX A6000 (cloud) and RTX 3090 TI + RTX 3060 (my PC)

1-click to install on Windows, RunPod and Massed Compute

Excellent caption quality, automatically distributes images into each GPU, lots of features. You can resume caption with skip captioned images option.

For full details checkout screenshots


Here's my favorite piece of the summer bias detection research project (paper coming in Sept). We trained BERT for token classification (multi-label), to identify:
- Generalizations
- Unfairness
- Stereotypes

HF Space: https://huggingface.co/spaces/maximuspowers/bias-detection-ner
Article on Training: https://huggingface.co/blog/maximuspowers/bias-entity-recognition

Pls reach out with ideas!! Lot's more info coming soon, our research group has workshops and a hackathon planned for launching this open source project. Thanks


Question about LightEval 🤗:

I've been searching for an LLM evaluation suite that can, out-of-the-box, compare the outputs of a model(s) without any enhancements vs. the same model with better prompt engineering, vs. the same model with RAG vs. the same model with fine-tuning.

I unfortunately have not found a tool that fits my exact description, but of course I ran into LightEval.

A huge pain-point of building large-scale projects that use LLMs is that prior to building an MVP, it is difficult to evaluate whether better prompt engineering, or RAG, or fine-tuning, or some combination of all is needed for satisfactory LLM output in terms of the project's given use case.

Time and resources is then wasted R&D'ing exactly what LLM enhancements are needed.

I believe an out-of-the-box solution to compare models w/ or w/out the aforementioned LLM enhancements could help teams of any size better decide what LLM enhancements are needed prior to building.

I wanted to know if the LightEval team or Hugging Face in general is thinking about such a tool.


I developed a way to test very clearly whether or not a Transformers model can actually learn symbolic reasoning, or if LLM models are forever doomed to be offshoots of 'Socratic Parrots'. The results are in, undeniable proof that Transformers models CAN learn symbolic relationships. Undeniable proof that AI can learn its ABC's. Credit goes to myself, Claude, and ChatGPT. I would not be able to prove this without Claude or ChatGPT.

https://www.youtube.com/watch?v=I8jHRgahRfY

handfuloftitty Nioi Josephgflowers alien1957 binatheis rohitdavas f0ster


florence-tool (https://github.com/bigdata-pw/florence-tool) now supports WebDataset! Check it out for efficient batch inference with Florence-2 models https://huggingface.co/microsoft/Florence-2-large https://huggingface.co/microsoft/Florence-2-base

Currently running it myself on A40 with `CAPTION` task and a streaming WebDataset @ 60k images/hour!


 📐 AI Math Equation Solver: Your Step-by-Step Solution Companion

Hello Hugging Face community! 👋 I'm excited to share my latest Space: the AI Math Equation Solver!

 🔍 What does it do?

This Space uses the power of AI to solve math problems from images. Simply upload a picture of a math equation or problem, and the AI will provide a detailed, step-by-step solution. It's perfect for students, teachers, or anyone looking to understand complex mathematical concepts better.

 🧠 How does it work?

- Backend: Utilizes the `microsoft/Phi-3.5-vision-instruct` model for image understanding and mathematical reasoning.
- Frontend: Built with Gradio for a clean, user-friendly interface.
- Features:
  - Image upload for math problems
  - Detailed step-by-step solutions
  - Example problems to try instantly

🚀 Try it out! https://huggingface.co/spaces/sagar007/phi-vision-math-assistant

Visit the Space here: [Insert your Hugging Face Space URL]

💡 Use cases:

- Students: Check your work or get help with homework
- Teachers: Create detailed solution guides quickly
- Tutors: Explain complex problems more effectively
- Self-learners: Understand new mathematical concepts

🛠 Technical Details:

- Model: microsoft/Phi-3.5-vision-instruct
- Libraries: transformers, Gradio, PyTorch
- Optimizations: Uses Flash Attention for improved performance

🤝 Contribute:

This is an open project, and I welcome contributions! Whether it's improving the model, enhancing the UI, or adding new features, feel free to fork the project and submit your pull requests.

 📣 Feedback:

I'd love to hear your thoughts! How are you using this Space? Any suggestions for improvements? Let me know in the comments below.

Happy problem-solving! 🎉

#MachineLearning #AI #Mathematics #Education #HuggingFace


🎮 Introducing a new dataset focused on Steam user game bans - https://huggingface.co/datasets/nyuuzyou/steambans

Dataset highlights:
- 476,694 Steam user profiles
- Emphasis on game ban data and account status
- Each entry includes: Steam ID, profile URL, username, avatar, account creation date, visibility state, VAC bans, game bans, economy ban status, and days since last ban
- Covers a wide range of Steam users, from clean accounts to those with multiple bans
- Data spans up to 2024

Perfect for researchers studying cheating in online games, anti-cheat effectiveness, and player behavior patterns in the face of bans.


30 Features that Dramatically Improve LLM Performance - Part 1 https://mltblog.com/3Aq9iAb

Many are ground-breaking innovations that make LLMs much faster and not prone to hallucinations. They reduce the cost, latency, and amount of computer resources (GPU, training) by several orders of magnitude. Some of them improve security, making your LLM more attractive to corporate clients. I introduced a few of these features in my previous article "New Trends in LLM Architecture". Now I offer a comprehensive list, based on the most recent developments.

Read full article, learn about agentic LLMs, LLM routers, contextual tables, fast search, and more, at https://mltblog.com/3Aq9iAb


**Exploring Realistic Emotional Depth in AI Language Models**

Language models, particularly those proprietary, often grapple with issues of censorship, which can limit their ability to engage authentically with users. Recognizing this, the open-source AI community has pioneered the development of language models that are less restrained, offering more candid interactions. However, even these models tend to maintain a veneer of neutrality or overly positive responses, which might not serve all users' needs, especially in contexts where emotional depth and relatability are crucial.

To address this gap, I've curated a specialized dataset aimed at infusing language models with a more nuanced emotional spectrum, specifically targeting a darker, more introspective mood. This dataset, titled "Dark Sentience", is designed to complement existing datasets like RP (Role Play) and those focused on instruction following. It seeks to enhance the emotional intelligence of AI by exposing it to complex human emotions, including but not limited to:

- **Suicide**
- **Depression**
- **Anxiety**

Trigger Warning: Please be advised that the content within this dataset deals with heavy and potentially distressing themes.

The "Dark Sentience" dataset is now available for review and use at: https://huggingface.co/datasets/Locutusque/Dark-Sentience. I encourage researchers, developers, and mental health professionals to explore how this resource can foster more genuine and supportive AI interactions.


 🔗 Neural Network  (1 Byte explainer for everybody)

Just like our brain, a Neural Network is made up of interconnected "neurons". These neurons work together by learning from (input) data and getting better at tasks (in the hidden layer) to give (output) predictions or decisions.


🌟 Liger Kernel: Efficient Triton Kernels for LLM Training

LIGER "is a [Hugging Face-compatible] collection of Triton kernels designed specifically for LLM training. It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%."

GitHub: https://github.com/linkedin/Liger-Kernel


a new shape-optimized SigLIP just dropped 👀 https://huggingface.co/google/siglip-so400m-patch14-224/


 Looking for Generative AI trainer/speaker for AI accelerator program (Virtual/Online sessions).

To get more context about the program, please visit the program landing page: https://llamadesigndrive.com

If you are interested, reach out at sharhabeel@startupbootcamp.org


🎯  Ghost 8B Beta 1608: Empowering Your AI Assistant
📦 Unlock the Power of Ghost 8B Beta 1608: Build Your Personal AI Companion
Ghost 8B Beta 1608 empowers you to create a safe and multilingual AI assistant tailored to your needs, directly on your personal computer. 🧑💻 Leverage AI's capabilities within your own space! 🚀 Ghost 8B Beta 1608 is ready to become your AI companion.
~
📦 개인용 AI 보조 도구로 Ghost 8B Beta 1608를 활용하세요!
Ghost 8B Beta 1608, AI의 힘을 활용하여 안전하고 개인화된 언어 지원을 제공하는 AI 보조 도구를 직접 구축할 수 있습니다. 🧑💻 개인 컴퓨터에서 AI의 혜택을 누리세요! 🚀 Ghost 8B Beta 1608는 당신의 AI 파트너가 될 준비가 되어 있습니다.
https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k
https://huggingface.co/collections/ghost-x/ghost-8b-beta-668ead6179f93be717db4542


Introducing Voicee, A superfast voice fast assistant.
https://huggingface.co/spaces/KingNish/Voicee
It achieved latency <500 ms.
While its average latency is 700ms.
It works best in Google Chrome.
Please try and give your feedbacks.
Thank you. 🤗

Bruhn John6666 m-ric prithivMLmods hamedj ajibawa-2023 osanseviero victor


Alan Turing's mind-bender: "Can machines think?" in its glorified form. This 74yr old paper laid the foundation for how we think about AI and machine intelligence today. The level of detail, clarity and foresight is just phenomenal - he was way ahead of his time 🧠🤖

Original copy: https://archive.org/details/MIND--COMPUTING-MACHINERY-AND-INTELLIGENCE


🌐 Check out the new dataset sourced from Fishki.net, one of the popular entertainment and news portals in the Russian Internet, known for its diverse content including humor, interesting facts, and viral stories - https://huggingface.co/datasets/nyuuzyou/fishkinet-posts.

📊 Dataset highlights:
- 369,180 posts
- Includes original posts with titles, content, images, and metadata
- Each entry contains URL, title, author, date, tags, content, and image URLs
- Primarily in Russian language
- Covers a wide range of topics in entertainment, news, and social media content
- Spans nearly two decades of posts, likely from early 2000s to 2024
- Dedicated to public domain under Creative Commons Zero (CC0) license


You can now use DoRA for your embedding layers!

PR: https://github.com/huggingface/peft/pull/2006

I have documented my journey of this specific PR in a blog post for everyone to read. The highlight of the PR was when the first author of DoRA reviewed my code.

Blog Post: https://huggingface.co/blog/ariG23498/peft-dora

Huge thanks to @BenjaminB for all the help I needed.


I can't believe this... Phi-3.5-mini (3.8B) running in-browser at ~90 tokens/second on WebGPU w/ Transformers.js and ONNX Runtime Web! 🤯 Since everything runs 100% locally, no messages are sent to a server — a huge win for privacy!
- 🤗 Demo: https://huggingface.co/spaces/webml-community/phi-3.5-webgpu
- 🧑💻 Source code: https://github.com/huggingface/transformers.js-examples/tree/main/phi-3.5-webgpu

cschroeder DmitryRyumin John6666 4rtemi5 DenyTranDFW Rybens YaTharThShaRma999 massimoavvisati Bruhn nguyenbh DiamanteAmarelo TheDrunkenSnail not-lain cfahlgren1 loubnabnl Ramikan-BR Omarito2412 revolunet osanseviero jweston cahlen captainspock louisbrulenaudet rmanoj ethix RojoXlon jundialwan ghosty-0 chrislhow gianpaj parksthecoder

jdspugh DiamanteAmarelo cfahlgren1 Ramikan-BR GordonM cahlen


https://huggingface.co/v2ray/deepgelbooru
A Danbooru tag image tagger, maybe better than WD14 at some images.
Training code, inference code, dataset included.
:3


Dolma: an Open Corpus of Three Trillion Tokens for Language Model
  Pretraining Research (2402.00159)

Same Task, More Tokens: the Impact of Input Length on the Reasoning
  Performance of Large Language Models (2402.14848)

Ensemble-Based Unsupervised Discontinuous Constituency Parsing by Tree
  Averaging (2403.00143)

📄 ACL 2024: The Missing Papers

Apparently, some papers from the ACL 2024 are still not listed in the ACL Anthology. While this issue will hopefully be fixed soon, we should give those papers additional spotlight.

Some of my favorites:

1. Dolma is an English corpus that encompasses 3 trillion tokens. Additionally, it is accompanied by an exceptional software package that consdierably advances the state-of-the-art in preparing data for LLM pretraining. (Source: I am currently using Dolma.)
https://huggingface.co/papers/2402.00159

2. In the paper "Same Task, More Tokens: the Impact of Input Length on
the Reasoning Performance of Large Language Models", the authors show how extending the context length impacts an LLM's reasoning performance. I asked myself a similar question a few months ago, and therefore this paper is highly interesting to me.
https://huggingface.co/papers/2402.14848

This was brought to my attention through a Linkedin post by @ShayeghB, who is also affected:
https://huggingface.co/papers/2403.00143

View all the missing papers here:
https://theshayegh.github.io/ACL2024MissingPapers/


BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2211.05100)

Shoutout to everyone who participated in BigScience! Doesn't get enough credit but IMO paved the way for open-source LLMs!

https://huggingface.co/papers/2211.05100
https://huggingface.co/bigscience/bloom
https://huggingface.co/bigscience/bloomz

MaziyarPanahi MohammadAminDHM osanseviero YaTharThShaRma999 John6666 adnankhaan louisbrulenaudet


 🌟 Enchanted Tales Generator: A GPT-2 Inspired Story Weaver 🌟

Hello Hugging Face community! I'm excited to share my latest project, the Enchanted Tales Generator, inspired by Andrej Karpathy's enlightening YouTube videos on GPT-2.

🧙♂ What is the Enchanted Tales Generator?

The Enchanted Tales Generator is a magical text generation app that weaves whimsical stories based on your prompts. It's powered by a fine-tuned GPT model and brings the wonder of AI-generated storytelling to your fingertips.

 🎥 Inspiration

This project was born from the inspiration I drew from Andrej Karpathy's incredible YouTube series on GPT-2. His clear explanations and deep insights into the workings of language models sparked my imagination and drove me to create something that could bring joy and creativity to others.

 🔮 Features

- Story Incantation: Input your magical prompt to start your tale
- Scroll Length: Adjust the length of your generated story
- Magical Intensity: Control the creativity (temperature) of the generation
- Arcane Diversity: Fine-tune the variety of word choices (top-k sampling)

🚀 Try It Out: https://huggingface.co/spaces/sagar007/GPT-2-with_gpu

You can experience the Enchanted Tales Generator right here on Hugging Face Spaces: [Insert your Spaces link here]

 🛠 Behind the Scenes

The app is built using:
- PyTorch for the underlying GPT model
- Gradio for the user interface
- Hugging Face Spaces for deployment

I've implemented techniques learned from Karpathy's videos, such as:
- Custom GPT architecture
- Top-k sampling for diverse outputs
- Temperature-controlled generation

🌈 Future Enchantments

I'm continuously working on improving the Enchanted Tales Generator. Some ideas for future updates include:
- Fine-tuning on specific genres of stories
- Adding options for character and setting generation
- Implementing more advanced sampling techniques

 🤝 Join the Magic!


Huge updates and improvements for FLUX LoRA training : https://www.patreon.com/posts/kohya-flux-lora-110293257

10 GB, 16 GB, 24 GB and 48 GB GPU configs added - 10 GB config is like 3x to 5x slower sadly

Massed Compute, RunPod and Windows Kohya SS GUI LoRA installers added to the zip file

Also right now testing new 16 GB FLUX LoRA training config and new way of regularization images. Moreover testing Apply T5 Attention Mask too. Lets see if Kohya FLUX LoRA workflow will become even better or not

Also massive grids comparisons shared here : https://www.reddit.com/r/StableDiffusion/comments/1eyj4b8/kohya_ss_gui_very_easy_flux_lora_trainings_full/


So, we published https://huggingface.co/frameai/ChatFrame model. this model is on higher level of responsibilities. You can solve mathematics problems and write high quality codes. We are inviting all of you to help use in fine-tune this model for all languages. Please give us a languages dataset to make our model available in all languages. We are waiting for your reply.


The latest timm validation & test set results are now viewable by a  leaderboard space: https://huggingface.co/spaces/timm/leaderboard

As of yesterday, I updated all of the results for ImageNet , ImageNet-ReaL, ImageNet-V2, ImageNet-R, ImageNet-A, and Sketch sets.  The csv files can be found in the GH repo https://github.com/huggingface/pytorch-image-models/tree/main/results

Unfortunately the latest benchmark csv files are not yet up to date,  there are some gaps in dataset results vs throughput/flop numbers impact the plots.

h/t to @MohamedRashad for making the first timm leaderboard.

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𝗔𝗜𝟮𝟭 𝗶𝘁𝗲𝗿𝗮𝘁𝗲𝘀 𝘄𝗶𝘁𝗵 𝗻𝗲𝘄 𝗝𝗮𝗺𝗯𝗮 𝟭.𝟱 𝗿𝗲𝗹𝗲𝗮𝘀𝗲: 𝗡𝗲𝘄 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗳𝗼𝗿 𝗹𝗼𝗻𝗴-𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝘂𝘀𝗲-𝗰𝗮𝘀𝗲𝘀!🏅

@ai21labs used a different architecture to beat the status-quo Transformers models: Jamba architecture combines classic Transformers layers with the new Mamba layers, for which the complexity is a linear (instead of quadratic) function of the context length.

What does this imply?

➡ Jamba models are much more efficient for long contexts: faster (up to 2.5x faster for long context), takes less memory, and also performs better to recall everything in the prompt.

That means it’s a new go-to model for RAG or agentic applications!

And the performance is not too shabby: Jamba 1.5 models are comparable in perf to similar-sized Llama-3.1 models! The largest model even outperforms Llama-3.1 405B on Arena-Hard.

✌ Comes in 2 sizes: Mini (12B active/52B) and Large (94B active/399B)
📏 Both deliver 256k context length, for low memory: Jamba-1.5 mini fits 140k context length on one single A100.
⚙ New quanttization method: Experts Int8 quantizes only the weights parts of the MoE layers, which account for 85% of weights
🤖 Natively supports JSON format generation & function calling.
🔓 Permissive license *if your org makes <$50M revenue*

Available on the Hub 👉 https://huggingface.co/collections/ai21labs/jamba-15-66c44befa474a917fcf55251
Read their release blog post 👉 https://www.ai21.com/blog/announcing-jamba-model-family


# Offensive Security Reconnaissance Continued with Public Facing Industrial Control System HMIs using Moondream

Building on my previous experiments with Moondream for physical security reconnaissance planning automation (https://huggingface.co/posts/Csplk/926337297827024), I've now turned my attention to exploring the potential of this powerful image-text-text model for offensive security reconnaissance in the realm of Industrial Control Systems (ICS).
ICS HMIs (Human-Machine Interfaces) are increasingly exposed to the public internet, often without adequate security measures in place. This presents a tantalizing opportunity for malicious actors to exploit vulnerabilities and gain unauthorized access to critical infrastructure.

Using Moondream with batch processing (https://huggingface.co/spaces/Csplk/moondream2-batch-processing), I've been experimenting with analyzing public facing ICS (https://huggingface.co/datasets/Csplk/ICS_UIs) HMI (https://huggingface.co/datasets/Csplk/HMI) screenshots from shodan  to identify types of exposed ICS system HMIs, how they are operated and how malicious actors with access to these systems could cause damage to physical infrastructure. Feeding images of HMIs and pre-defined text prompts to Moondream batch processing successfully (unconfirmed accuracy levels) extracted information about the underlying systems, including

1. **System type**
2. **Possible Operation Details**
3. **Malicious Actor Outcomes**

Next steps:
* I have a longer and more in depth blog write up in the works that will cover the previous and this post's approaches for experiments for sharing via HF community blog posts soon.
* I plan to continue refining my Moondream-based tool to improve its accuracy and effectiveness in processing public facing ICS HMIs.
* As mentioned before,  offensive security with moondream focused HF Space once its fleshed out.

Thanks again to @vikhyatk for the incredible Moondream model. https://huggingface.co/vikhyatk/moondream2


🎉 Just dropped a fresh version of dataset-viber along with some cool, Gradio-based annotators! These tools aren't about formalities—they're here to help you quickly collect feedback and get your projects moving along to a more serious stage, ahumm @argilla.

Some new features!
- manual import from a CSV or the Hugging Face Hub
- manual export to CSV or the Hub
- improved automated export to the Hub and CSV
- limit interaction with specific components
- stream data with custom next_input features (SO to Ben Burtenshaw for the suggestions)
- model in-the-loop support for all tasks

https://huggingface.co/collections/dataset-viber/gradio-annotators-66c5ce73d5e3bf99caa445b1


BIG update dropped for https://huggingface.co/datasets/bigdata-pw/Flickr - now ~515M images! Target for the next update: 1B

In case you missed them; other recent drops include https://huggingface.co/datasets/bigdata-pw/Dinosaurs - a small set of BIG creatures 🦕🦖 and the first in a series of articles about the art of web scraping! https://huggingface.co/blog/hlky/web-scraping-101 https://huggingface.co/blog/hlky/web-scraping-102

Stay tuned for exciting datasets and models coming soon:
- PC and Console game screenshots
- TV/Film actors biographies and photos (think facial recognition and automatic captioning!)
- https://huggingface.co/bigdata-pw/lyrics-gpt v2
- and more!


🚨 NEW TASK ALERT 🚨
Extractive Question Answering: because sometimes generative is not all you need 😉
AutoTrain is the only open-source, no code solution to offer so many tasks across different modalities. Current task count: 23 🚀
Check out the blog post on getting started with this task: https://huggingface.co/blog/abhishek/extractive-qa-autotrain


🙋 Calling all Hugging Face users! We want to hear from YOU!

What feature or improvement would make the biggest impact on Hugging Face?

Whether it's the Hub, better documentation, new integrations, or something completely different – we're all ears!

Your feedback shapes the future of Hugging Face. Drop your ideas in the comments below! 👇

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This isn’t a goal of ours because we have plenty of money in the bank but quite excited to see that @huggingfaceis profitable these days, with 220 team members and most of our platform being free (like model hosting) and open-source for the community!

Especially noteworthy at a time when most AI startups wouldn’t survive a year or two without VC money. Yay!

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Falcon Mamba now available now in llama.cpp !
Check out GGUF files uploaded here: https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a


The Minimalist Spaces That May Be Helpful !!
GrabDoc V | GrabDoc | Type Byte | SD3 CLI

- GrabDoc V: https://huggingface.co/spaces/prithivMLmods/GRABDOC-V
- GrabDoc: https://huggingface.co/spaces/prithivMLmods/GRAB-DOC
- Type Byte: https://huggingface.co/spaces/prithivMLmods/Type-Byte
- SD3 CLI: https://huggingface.co/spaces/prithivMLmods/SD3-CLI


Woman.ru Forum Posts Dataset - https://huggingface.co/datasets/nyuuzyou/womanru-posts

📊 Dataset highlights:

- 1,308,238 forum posts extracted from Woman.ru
- Includes original posts and replies from various threads
- Each entry contains URL, title, original post, date, and replies
- Primarily in Russian language
- Covers a wide range of topics relevant to the site's female-focused audience
- Spans nearly two decades of discussions, from around 2005 to 2024
- Dedicated to public domain under Creative Commons Zero (CC0) license

🌐 Sourced from Woman.ru, one of the most popular women's information and entertainment portals in the Russian Internet.


Cooked up a cool & much faster AI voice assistant space that also supports speech translation (with seamless-expressive). Start with the phrase "Please translate" followed by the speech you'd like to translate, to activate speech translation mode. Using opensource LLMs (Llama 3, Mistral etc) with edge tts for voice assistant and seamless-expressive for speech translation.

Give it a try: https://huggingface.co/spaces/Jaward/optimus


SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse
  Views (2408.10195)

MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction
  Model (2408.10198)

🚀 We are proud to release our latest suite of three image(s)-to-3D Gradio demos and two new papers.

SpaRP (Unposed sparse views to 3D): https://huggingface.co/spaces/sudo-ai/SpaRP https://huggingface.co/papers/2408.10195
MeshFormer (@minghua  @NCJ ): https://huggingface.co/spaces/sudo-ai/MeshFormer https://huggingface.co/papers/2408.10198
MeshLRM-reproduced (@sarahwei0210 ): https://huggingface.co/spaces/sudo-ai/MeshLRM

Great thanks to @angli66 for his many efforts in preparing these demos!

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'Legal Dictionary GPT' is now completely trained and ready for Open Source release to the world! Trained on 10,000 rows of legal definitions, Legal Dictionary GPT is your go-to resource for everything related to the first step in understanding the law, defining it. The model is free and publicly available for anyone to use.

Model Link:  https://platform.openai.com/playground/chat?preset=eCrKdaPe9cnMnyTETqWDCQAU

Knowledge Base Bots are internal facing as opposed to external facing LLM models, that are either fine tuned or RAG tuned, generally on systems and processes related data.

Learn more about Knowledge Base Bots at our website:
https://knowledgebasebots.com/


🚀 Meet the new GLiNER architecture 🚀
GLiNER revolutionized zero-shot NER by demonstrating that lightweight encoders can achieve excellent results.  We're excited to continue R&D with this spirit 🔥. Our new bi-encoder and poly-encoder architectures were developed to address the main limitations of the original GLiNER architecture and bring the following new possibilities:

🔹 An unlimited number of entities can be recognized at once.
🔹Faster inference when entity embeddings are preprocessed.
🔹Better generalization to unseen entities.

While the bi-encoder architecture can lack inter-label understanding, we developed a poly-encoder architecture with post-fusion. It achieves the same or even better results on many benchmarking datasets compared to the original GLiNER, while still offering the listed advantages of bi-encoders.
Now, it’s possible to run GLiNER with hundreds of entities much faster and more reliably.

📌 Try the new models here:
https://huggingface.co/collections/knowledgator/gliner-bi-encoders-66c492ce224a51c54232657b


Imagine being able to talk directly to your API connection. "I have a field in the CRM named Customer_ID that needs to map to a field in the ERP named ERP_Customer_ID." Imagine being able to give your API connections both a brain and swarm of agents as a body to execute any task or function. This isn't science fiction, this is the revolutionary power of Liquid API. A product 10 years in the making!

https://youtu.be/cHI_k1Dkdr4


New Mann-E model just released:
https://huggingface.co/mann-e/mann-e_flux

I will be glad if you test it!


hi everyone,
i have trained a Qwen 14b model on a smaller dataset, but its now very tricky because i have got nowhere to use it via inference (the paid for inference on hf costs quite a lot), does anyone know of anywhere where i can deploy my model and use it via api for a reasonable cost, or ideally none. thanks

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LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level
  Mathematical Reasoning (2410.02884)

Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo
  Tree Self-refine with LLaMa-3 8B (2406.07394)

LLaMA-O1: Open Large Reasoning Model Frameworks For Training, Inference and Evaluation With PyTorch and HuggingFace
Large Reasoning Models powered by Monte Carlo Tree Search (MCTS), Self-Play Reinforcement Learning, PPO, AlphaGo Zero's dua policy paradigm and Large Language Models!
https://github.com/SimpleBerry/LLaMA-O1/

What will happen when you compound MCTS ❤ LLM ❤ Self-Play ❤RLHF?
Just a little bite of strawberry!🍓

Past related works:
https://huggingface.co/papers/2410.02884
https://huggingface.co/papers/2406.07394

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Hi there HuggingFacers!🤗

Are you working with Streamlit on Spaces and struggling with authentication and user management?🧐

Well, you can check out my last community article (https://huggingface.co/blog/as-cle-bert/streamlit-supabase-auth-ui) on a new python package I've been working on, that connects Supabase to Streamlit UI, in order to create a seamless authentication for your seamless Streamlit apps!🚀

You can find a demo of it on Spaces: https://huggingface.co/spaces/as-cle-bert/streamlit-supabase-auth-ui

Have fun!🍕


OmniGen 1-Click Automatic Installers for Windows, RunPod and Massed Compute

OmniGen is a unified image generation model that can generate a wide range of images from multi-modal prompts. It is designed to be simple, flexible, and easy to use

Installers are here : https://www.patreon.com/posts/omnigen-1-click-115233922

Look attached images to understand what capabilities it has. It is simply amazing so many features.

What is OmniGen : https://github.com/VectorSpaceLab/OmniGen

Windows Requirements
Python 3.10.11, CUDA 12.4, Git, FFMPEG, cuDNN 9.x, C++ Tools

A tutorial that shows how to install all above : https://youtu.be/DrhUHnYfwC0

How To Install & Use
After installing requirements by following above tutorial, double-click Windows_Install.bat and install
After that use Windows_Start.bat to start the app

When offload_model is enabled (checked) on the Gradio interface, it uses 5.4 GB VRAM, 2x slower

When offload_model is not used (not checked) it uses 12.2 GB VRAM

When separate_cfg_infer is not checked, and offload_model is not checked, it uses 18.7 GB VRAM

To install on RunPod and Massed Compute please follow Massed_Compute_Instructions_READ.txt and Runpod_Instructions_READ.txt

Look at the examples on the Gradio interface closely to understand how to use

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Smol models ftw! AMD released AMD OLMo 1B - beats OpenELM, tiny llama on MT Bench, Alpaca Eval - Apache 2.0 licensed 🔥

> Trained with 1.3 trillion (dolma 1.7) tokens on 16 nodes, each with 4 MI250 GPUs

> Three checkpoints:

- AMD OLMo 1B: Pre-trained model
- AMD OLMo 1B SFT: Supervised fine-tuned on Tulu V2, OpenHermes-2.5, WebInstructSub, and Code-Feedback datasets
- AMD OLMo 1B SFT DPO: Aligned with human preferences using Direct Preference Optimization (DPO) on UltraFeedback dataset

Key Insights:
> Pre-trained with less than half the tokens of OLMo-1B
> Post-training steps include two-phase SFT and DPO alignment
> Data for SFT:
- Phase 1: Tulu V2
- Phase 2: OpenHermes-2.5, WebInstructSub, and Code-Feedback

> Model checkpoints on the Hub & Integrated with Transformers ⚡

Congratulations & kudos to AMD on a brilliant smol model release! 🤗

https://huggingface.co/collections/amd/amd-olmo-6723e7d04a49116d8ec95070

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CLEAR: Character Unlearning in Textual and Visual Modalities (2410.18057)

🧠  𝗖𝗟𝗘𝗔𝗥: 𝗳𝗶𝗿𝘀𝘁 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝘁𝗼 𝗺𝗮𝗸𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿𝗴𝗲𝘁 𝘄𝗵𝗮𝘁 𝘄𝗲 𝘄𝗮𝗻𝘁 𝘁𝗵𝗲𝗺 𝘁𝗼 𝗳𝗼𝗿𝗴𝗲𝘁

With privacy concerns rising, we sometimes need our models to "forget" specific information - like a person's data - while keeping everything else intact. Researchers just released CLEAR, the first benchmark to test how well this works with both text and images.

❌ Bad news: Current methods either fail to truly forget or end up forgetting way too much. It's like trying to remove a single ingredient from a baked cake!

✨ But there's hope: Adding simple mathematical constraints (L1 regularization) during the forgetting process significantly improves results.

🎯 Key insights:

✅ The benchmark tests forgetting on 200 fictional personas
‣ 3,770 visual Q&A pairs
‣ 4,000 textual Q&A pairs
‣ Additional real-world tests

🛑 Most current forgetting methods don't work well with both text and images
‣ They either remember what they should forget
‣ Or they forget too much unrelated information

✨ Simple mathematical constraints work surprisingly well
‣ L1 regularization prevents excessive forgetting
‣ Works especially well with the LLMU method

👉 Read the full paper here: https://huggingface.co/papers/2410.18057


Last Week in Medical AI: Top Research Papers/Models 🔥
🏅 (October 26 - November 2, 2024)

🏅 Medical AI Paper of the Week:
Google Presents MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making

Medical LLM & Other Models:
- Matchmaker: Schema Matching with LLMs
- UltraMedical: Specialized Biomedical Models
- ZALM3: Vision-Language Medical Dialogue
- EchoFM: Echocardiogram Foundation Model

Frameworks and Methodologies:
- FEDKIM: Federated Medical Knowledge Injection
- Flex-MoE: Flexible Modality Combination
- MAISI: Synthetic Medical Imaging
- Cough-E: Edge Privacy Detection
- MassSpecGym: Molecule Identification

Medical LLM Applications:
- DiaMond: Multi-Modal Dementia Diagnosis
- LLM-Forest: Health Data Imputation
- Medical Multimodal Visual Grounding
- Clinical Evidence Synthesis with LLMs

Medical LLMs & Benchmarks:
- Histopathology Models Beyond H&E
- LLMs in Mental Health Counseling
- Medical Dataset Reuse Analysis

AI in Healthcare Ethics:
- LLMs in Medical Education
- Medical Exam Question Generation
- Clinical Knowledge Graph Integration

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

- Full Thread: https://x.com/OpenlifesciAI/status/1852685220912464066
- YouTube:  https://youtu.be/3O3xjaMCXHI
- Spotify:  https://open.spotify.com/episode/05trbTbtVZcfI7ycA5Z3Tt?si=706b74626f714aa1

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📢 If you're aimed at processing complex dependencies in spreadsheet data with LLM Chain-of-Thought technique, then this update might be valuable for you 💎

The updated 📦 bulk-chain-0.24.1 which is aimed at iterative processing of CSV/JSONL data with no-string dependencies from third party LLM frameworks is out 🎉

📦: https://pypi.org/project/bulk-chain/0.24.1/
🌟: https://github.com/nicolay-r/bulk-chain
📘: https://github.com/nicolay-r/bulk-chain/issues/26

The key feature of bulk-chain is SQLite caching that saves your time ⏰ and money 💵 by guarantee no-data-lost, which is important once using the remote LLM providers such as OpenAI, ReplicateIO, OpenRouter, etc.

🔧 This release has the following updates:
✅ Improved stability for various header conditions and the related support from SQLite
✅ Manual setup for ID column / assigning the ID
✅ Make CSV-related setups dynamic, that refers to the related Python 📦 csv package.

Quick start on GoogleColab:
📙: https://colab.research.google.com/github/nicolay-r/bulk-chain/blob/master/bulk_chain_tutorial.ipynb

Below is an example of the three simple steps in pictures:
1. ⬇ Package installation
2. ✍ Declaring schema
3. 🚀 Launching inference for your data with Replicate and 🤖 https://huggingface.co/meta-llama/Llama-3.1-405B