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RedRedRed

RedSparkie

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𝐃𝐈𝐒𝐀𝐏𝐏𝐄𝐀𝐑 ⛧ ᐅᓵᘜᓵᐪᗅᖶ 𝓐𝓡𝓣

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liked a model 2 days ago

burner22/numbat

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hugging-apps/bfs-best-face-swap

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posted an update 2 months ago

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New inpainting space using H3 from Minimax without any kind of censorship!

Use it with responsability:
RedSparkie/minimax-h3-inpainting-lenia

And give it some love 🫶

Leave your opinions and suggestiona on the discussion tab, please!

reacted to OppaAI's post with 🔥 2 months ago

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Congrat to HuggingFace Team

https://techcrunch.com/2026/08/26/nvidia-closes-in-on-hugging-face-acquisition/

3 replies

reacted to fffiloni's post with 🔥 2 months ago

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If an agent can build the obvious demo, the obvious demo probably isn’t worth building anymore.
For years, turning a research repo into something people could actually try was valuable by itself.
That part is becoming automated — and that’s a good thing.

Which means the interesting work moves elsewhere: finding the weird use case, the right interaction, the unexpected model combination — or simply knowing which paper is worth anyone’s attention.

The demo used to be the product. Now it needs a point of view.

1 reply

reacted to danielhanchen's post with 🔥 3 months ago

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Introducing Unsloth Desktop 🦥
The first desktop app to run and train models locally.

• Open-source. Runs on Mac, Windows and Linux
• Supports MLX, diffusion image/video, audio, GGUF
• Connect Claude Code and Codex to local LLMs
• 50% more accurate, self-healing tool calls + sandboxed code exec
• Works for CPU + multiGPU setups - NVIDIA, AMD, Intel, Mac
• Train models 2× faster with 70% less VRAM
• Private web search, deep research, RAG, MCP and exports (NVFP4, GGUF)
• Use Unsloth’s OpenAI-compatible API and cloud models
• Securely deploy LLMs remotely and access anywhere

Unsloth Desktop is now available on http://unsloth.ai
and GitHub.

GitHub: https://github.com/unslothai/unsloth
Blog and Guide: https://unsloth.ai/docs/desktop

6 replies

reacted to wop's post with 👀 4 months ago

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# PixelModel v1

Last month we released PixelModel — a neural network whose weights are literally the pixels of a PNG. It was a toy: 202,752 parameters, welded to 32×32 output, trained on six solid-color swatches. It scored FID 566.84 on the Tiny-T2I-Leaderboard, mostly by producing the same yellow noise for every prompt.

Today we're releasing PixelModel v1. It is 8.5× smaller — 23,747 parameters — and it beats v0 on both benchmark metrics while being trained on 20,000 real MS-COCO caption/image pairs instead of six color swatches. The entire model now fits in a 160×149 PNG.

That image is not a visualization of the model. It is the model. All 23,747 weights, one per pixel.

## links

bench-labs
Blog post [read it here ⇗]( bench-labs/blog)
See us on the [Leaderboard ⇗]( FlameF0X/Tiny-T2I-Leaderboard)
Model card [here]( bench-labs/pixelmodel-v1)

## The catch
A 23K-parameter model does not draw sandwiches. With ~1 parameter per training image, the loss-minimizing behavior is to output the average of all plausible images for a caption — caption-conditioned color, light, and layout statistics. Food prompts come out warm and brown; sky prompts come out cool and bright. That is the ceiling for this size class, and we'd rather show it than crop around it.

# cherry on top 🍒
The model generates 600 images (cpu) in 5 (five) seconds.
Thats 5000 images in 24 seconds on cpu.
The model trained on cpu for just 30 minutes.

reacted to cahlen's post with 🚀 8 months ago

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It’s wild to me how you can just make shit now.

You can take a weekend with a raspberry pi 5, a pi camera, a 3d printer, and a smidgen of custom fine tuning (wakeword, whisper, tinybert, and pipertts) and you have physical device as a talking personal assistant.

What a time to be alive.

Edge ai, physical ai, ai augmented animatronics… tiny models. Tiny agents.

Going to be a wild year.

6 replies

reacted to Reality123b's post with ❤️ 10 months ago

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Happy birthday to me!!!

2 replies

reacted to mlabonne's post with 🔥 over 1 year ago

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✂️ AutoAbliteration

I made a Colab notebook to automatically abliterate models.

It's quite general, so you can do interesting stuff like blocking a given language in the model outputs.

💻 Colab: https://colab.research.google.com/drive/1RmLv-pCMBBsQGXQIM8yF-OdCNyoylUR1?usp=sharing

2 replies

reacted to samihalawa's post with 👀 over 1 year ago

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🥳🥳Just achieved 25m 59s of research with plain ChatGPT 🔥 Had it doing a complete internet search in just ONE call visiting 443 websites! Hard to beat huh!
PROMPT IN COMMENTS
Check out the Massive Article created by the prompt:
https://huggingface.co/blog/luigi12345/automating-lead-generation-with-ai

9 replies

reacted to m-ric's post with ❤️ over 1 year ago

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We now have a Deep Research for academia: SurveyX automatically writes academic surveys nearly indistinguishable from human-written ones 🔥

Researchers from Beijing and Shanghai just published the first application of a deep research system to academia: their algorithm, given a question, can give you a survey of all papers on the subject.

To make a research survey, you generally follow two steps, preparation (collect and organize papers) and writing (outline creation, writing, polishing). Researchers followed the same two steps and automated them.

🎯 For the preparation part, a key part is find all the important references on the given subject.
Researchers first cast a wide net of all relevant papers. But then finding the really important ones is like distilling knowledge from a haystack of information. To solve this challenge, they built an “AttributeTree” object that structures key information from citations. Ablating these AttributeTrees significantly decreased structure and synthesis scores, so they were really useful!

📝 For the writing part, key was to get a synthesis that's both short and true. This is not easy to get with LLMs! So they used methods like LLM-based deduplication to shorten the too verbose listings made by LLMs, and RAG to grab original quotes instead of made-up ones.

As a result, their system outperforms previous approaches by far!

As assessed by LLM-judges, the quality score os SurveyX even approaches this of human experts, with 4.59/5 vs 4.75/5 🏆

I advise you to read the paper, it's a great overview of the kind of assistants that we'll get in the short future! 👉 SurveyX: Academic Survey Automation via Large Language Models (2502.14776)
Their website shows examples of generated surveys 👉 http://www.surveyx.cn/

reacted to julien-c's post with 🔥 almost 2 years ago

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After some heated discussion 🔥, we clarify our intent re. storage limits on the Hub

TL;DR:
- public storage is free, and (unless blatant abuse) unlimited. We do ask that you consider upgrading to PRO and/or Enterprise Hub if possible
- private storage is paid above a significant free tier (1TB if you have a paid account, 100GB otherwise)

docs: https://huggingface.co/docs/hub/storage-limits

We optimize our infrastructure continuously to scale our storage for the coming years of growth in Machine learning, to the benefit of the community 🔥

cc: @reach-vb @pierric @victor and the HF team

29 replies

reacted to m-ric's post with 👀👀 almost 2 years ago

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𝗦𝗵𝗼𝘄𝗨𝗜: 𝗮 𝘀𝗺𝗮𝗹𝗹 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗮𝗴𝗲𝗻𝘁 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻 𝗻𝗮𝘃𝗶𝗴𝗮𝘁𝗲 𝗮𝗻𝘆 𝗨𝗜 𝗮𝗻𝗱 𝗼𝘂𝘁𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝘀 𝗺𝘂𝗰𝗵 𝗯𝗶𝗴𝗴𝗲𝗿 𝘀𝘆𝘀𝘁𝗲𝗺𝘀! 📲

A team from NUS and Microsoft just released an agent that can act on any UI (Desktop, Android, Web) without needing additional text information. It works extremely well : they applied their method on a tiny Qwen2-VL-2B, and they managed to beat methods that use either much more powerful vision models (like GPT-4V) without using any additional info (e.g. leveraging the DOM of a webpage) like previous methods did ! 👏👏

They started from the idea that most existing methods rely heavily on text, which makes them less generalizable, while letting aside rich UI structure that user actually rely on when navigating this interfaces.

⚙️ They put several good ideas to work:

💡 Simplify screenshots to the max:
They prune a lot the heavy visual content of UI screenshots, by removing cloned image patches (like any vast patch of the same color will be reduced to a small patch, while maintaining positional embeddings), then group patches from the same GUI elements together to simplify even further

💡 Build a truly generalist dataset:
To train a general UI agent, you need trajectories from each possible UI, and express them in a common language. Authors merge datasets like OmniAct for Desktop, Mind2Web for websites, AMEX for Android trajectories to create a high-quality and diverse dataset.

➡️ Nice results ensued:
They fine-tune a tiny Qwen-2-VL-2B on their method, and it reaches SOTA on several task (element identification, web navigation), even beating methods that either use additional info from the DOM or use much bigger VLMS like GPT-4v! 🏆

And performance could certainly jump with a slightly bigger vision model. Let's hope the community builds this soon! 🚀

Paper added to my "Agents" collection 👉 m-ric/agents-65ba776fbd9e29f771c07d4e

replied to singhsidhukuldeep's post almost 2 years ago

Is there any update on this field of research? Not very keen on this, but I know a friend who'd like to know 😅.

reacted to merve's post with 👀 almost 2 years ago

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Apple released AIMv2 🍏 a family of state-of-the-art open-set vision encoders
apple/aimv2-6720fe1558d94c7805f7688c
> like CLIP, but add a decoder and train on autoregression 🤯
> 19 open models come in 300M, 600M, 1.2B, 2.7B with resolutions of 224, 336, 448
> Load and use with 🤗 transformers

reacted to cfahlgren1's post with ❤️ almost 2 years ago

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You can clean and format datasets entirely in the browser with a few lines of SQL.

In this post, I replicate the process @mlabonne used to clean the new microsoft/orca-agentinstruct-1M-v1 dataset.

The cleaning process consists of:
- Joining the separate splits together / add split column
- Converting string messages into list of structs
- Removing empty system prompts

https://huggingface.co/blog/cfahlgren1/the-beginners-guide-to-cleaning-a-dataset

Here's his new cleaned dataset: mlabonne/orca-agentinstruct-1M-v1-cleaned

1 reply

reacted to TuringsSolutions's post with 🧠 about 2 years ago

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

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

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

reacted to DmitryRyumin's post with 🔥 over 2 years ago

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🚀🎭🌟 New Research Alert - CVPR 2024 (Avatars Collection)! 🌟🎭🚀
📄 Title: EMOPortraits: Emotion-enhanced Multimodal One-shot Head Avatars 🔝

📝 Description: EMOPortraits is an enhanced multimodal one-shot head avatar model that achieves SOTA performance in emotion transfer and audio-driven facial animation tasks by improving the training pipeline and architecture to better handle intense and asymmetric facial expressions, while also proposing a novel multiview video dataset containing a wide range of such expressions.

👥 Authors: Nikita Drobyshev, Antoni Bigata Casademunt, Konstantinos Vougioukas, Zoe Landgraf, Stavros Petridis, and Maja Pantic

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

📄 Paper: EMOPortraits: Emotion-enhanced Multimodal One-shot Head Avatars (2404.19110)

🌐 GitHub Page: https://neeek2303.github.io/EMOPortraits

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

🚀 Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36

🔍 Keywords: #EMOPortraits #EmotionalTransfer #FacialAnimation #HeadAvatar #MultimodalLearning #OneShotLearning #AsymmetricFacialExpressions #IntenseFacialExpressions #NovelDataset #CVPR2024 #DeepLearning #ComputerVision #Innovation

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reacted to smangrul's post with 🤯 over 2 years ago

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Unlocking the Power of locally running Llama-3 8B Model Agents with Chat-UI! 🔥🚀✨

I'm thrilled to share my hackathon-style side project:
1. Finetuning Llama-8B for function calling using PEFT QLoRA as the instruct Llama-3 model doesn't support this. The colab notebook for it is here: https://lnkd.in/ggJMzqh2. 🛠️
2. Finetuned model along with the 4-bit quants here: https://lnkd.in/gNpFKY6V ✨
3. Clone Hugging Face https://lnkd.in/gKBKuUBQ and make it compatible for function calling by building upon the PR https://lnkd.in/gnqFuAd4 for my model and local inferencing usecase using Ollama. This was a steep learning curve wherein I stayed awake the whole night to get it working. 💪🏽
4. Above, I used SerpAPI for web browsing and Mongo DB Atlas free tier for persistence of conversations and assistant configs. 🔎
5. More work is required to switch between using tools and responding directly wherein I see the model breaks. 🧐

How cool is this wherein we are approaching experience akin to ChatGPT while using local hosted agent model running on your laptop! 💻

1 reply

reacted to macadeliccc's post with ❤️ over 2 years ago

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Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

UCLA-AGI has proven that large language models, even weaker large language models, can improve themselves with data only produced by original model. The question they answer in their paper is:

"Can we empower a weak LLM to improve itself without acquiring additional human annotated data?"

They answer this question by the proposal and testing of a novel fine-tuning method they call Self-Play fIne-tuNing (SPIN). The process starts by applying a supervised fine-tune (SFT) to zephyr-7b using all 200k samples of HuggingfaceH4/ultrachat_200k to eliminate the need for a human annotator.

Once the model has completed SFT, the SPIN method suggests generating 50k samples of synthetic data pairs of 'chosen' and 'rejected' samples. The model will be fine tuned on those generations, and this process will repeat for another 3 iterations for a total 200k samples.

This experiment is unique because they propose that their method can yield upwards of 10% performance gains without using any additional human annotated data. The strategy was designed to improve the less strong language models, but with further experimentation could be a formidable strategy for improving language models.

If you would like to explore this strategy for yourself, here are some resources:
Colab: https://colab.research.google.com/drive/1IjDeNVBsRru2-hM_9aauD6gVI-VvJnpk?usp=sharing
Github: https://github.com/uclaml/SPIN
The product of the experiment: UCLA-AGI/zephyr-7b-sft-full-SPIN-iter3
Paper: Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models (2401.01335)

3 replies

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