nerdyface (Nerdy Face)

stefan-it

posted an update about 17 hours ago

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1012

After running some 3DMark and FurMark benchmarks on Windows to make sure that my new 5090 is not causing melting cables [1] and some nice shots with a thermal camera (I don't think that's too much), running some fine-tuning experiments with my favorite Flair & Transformers libraries are very easy to perform.

Important steps:

Good idea is to start with a fresh Ubuntu 24.04 installation with latest CUDA 12.8 and the open NVIDIA driver - follow more advices from [2]:

sudo apt -y install cuda-toolkit-12-8 nvidia-open

I tried update from an existing Ubuntu installation with an older CUDA and driver version and it resulted in a non-startable system.

If you are using PyTorch 2.6 with built CUDA 12.6 it will result in:

NVIDIA Graphics Device with CUDA capability sm_120 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_50 sm_60 sm_70 sm_75 sm_80 sm_86 sm_90.

But no worries! For PyTorch you need just to use a nightly 2.7 version that was built with CUDA 12.8. This can easily done via:

pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu128

After that the latest Flair version can be installed and fine-tuning will work!

References:

[1]: https://www.reddit.com/r/nvidia/comments/1inpox7/rtx_50_series_12vhpwr_megathread/
[2]: https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=24.04&target_type=deb_network

m-ric

posted an update 4 days ago

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4107

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/

stefan-it

posted an update 4 days ago

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5007

She arrived 😍

[Expect more models soon...]

2 replies

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JingzeShi

posted an update 7 days ago

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2876

🤗Welcome to the Doge Edge Device Small language Model.

SmallDoge/Doge-160M-Instruct

m-ric

posted an update 10 days ago

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2908

Less is More for Reasoning (LIMO): a 32B model fine-tuned with 817 examples can beat o1-preview on math reasoning! 🤯

Do we really need o1's huge RL procedure to see reasoning emerge? It seems not.
Researchers from Shanghai Jiaotong University just demonstrated that carefully selected examples can boost math performance in large language models using SFT —no huge datasets or RL procedures needed.

Their procedure allows Qwen2.5-32B-Instruct to jump from 6.5% to 57% on AIME and from 59% to 95% on MATH, while using only 1% of the data in previous approaches.

⚡ The Less-is-More Reasoning Hypothesis:
‣ Minimal but precise examples that showcase optimal reasoning patterns matter more than sheer quantity
‣ Pre-training knowledge plus sufficient computational resources at inference levels up math skills

➡️ Core techniques:
‣ High-quality reasoning chains with self-verification steps
‣ 817 handpicked problems that encourage deeper reasoning
‣ Enough inference-time computation to allow extended reasoning

💪 Efficiency gains:
‣ Only 817 examples instead of 100k+
‣ 40.5% absolute improvement across 10 diverse benchmarks, outperforming models trained on 100x more data

This really challenges the notion that SFT leads to memorization rather than generalization! And opens up reasoning to GPU-poor researchers 🚀

Read the full paper here 👉 LIMO: Less is More for Reasoning (2502.03387)

taesiri

authored a paper 10 days ago

ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models

Paper • 2502.09696 • Published 15 days ago • 38

fffiloni

posted an update 13 days ago

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4162

I was thinking i need to step up my game on training Flux LoRas models, time to have some fun ! ☀️

Expect a new drop per week on aesthetics that catched my attention, here are 3 of them that worked really well !

fffiloni/cute-comic-800
fffiloni/carbo-800
fffiloni/oniric-750

m-ric

posted an update 14 days ago

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2818

𝗚𝗿𝗲𝗮𝘁 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗮𝗹𝗲𝗿𝘁: you can now share agents to the Hub! 🥳🥳

And any agent pushed to Hub get a cool Space interface to directly chat with it.

This was a real technical challenge: for instance, serializing tools to export them meant that you needed to get all the source code for a tool, verify that it was standalone (not relying on external variables), and gathering all the packages required to make it run.

Go try it out! 👉 https://github.com/huggingface/smolagents

2 replies

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m-ric

posted an update 14 days ago

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2452

For those who haven't come across it yet, here's a handy trick to discuss an entire GitHub repo with an LLM:

=> Just replace "github" with "gitingest" in the url, and you get the whole repo as a single string that you can then paste in your LLMs

fuzzy-mittenz

posted an update 15 days ago

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642

So frustrated with "Reasoning" Models.
Sure, introducing RAG into the mix, or giving it an interpreter to math with helps, but never as much as a model that has good instructions.

Even if it's just to repeat the information before answering, a normal model will usually out "Think" it's reasoning counterpart.

Not sure if it's my frustrations but the best answers I've received (from a reasoner), so far, are from the simple instructions to, "Do better!"

Figured I would share the special sauce.

Using 10-100x Compute just to heat the office can't be environmentally friendly, and It still has no Idea where my keys are.

m-ric

posted an update 16 days ago

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4771

"𝟮𝟬𝟮𝟱 𝘄𝗶𝗹𝗹 𝗯𝗲 𝘁𝗵𝗲 𝘆𝗲𝗮𝗿 𝗼𝗳 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀": this statement has often been made, here are numbers to support it.

I've plotted the progress of AI agents on GAIA test set, and it seems they're headed to catch up with the human baseline in early 2026.

And that progress is still driven mostly by the improvement of base LLMs: progress would be even faster with fine-tuned agentic models.

m-ric

posted an update 21 days ago

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3697

𝗔𝗱𝘆𝗲𝗻'𝘀 𝗻𝗲𝘄 𝗗𝗮𝘁𝗮 𝗔𝗴𝗲𝗻𝘁𝘀 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝘀𝗵𝗼𝘄𝘀 𝘁𝗵𝗮𝘁 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸-𝗥𝟭 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗲𝘀 𝗼𝗻 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝘁𝗮𝘀𝗸𝘀! ❌

➡️ How well do reasoning models perform on agentic tasks? Until now, all indicators seemed to show that they worked really well. On our recent reproduction of Deep Search, OpenAI's o1 was by far the best model to power an agentic system.

So when our partner Adyen built a huge benchmark of 450 data science tasks, and built data agents with smolagents to test different models, I expected reasoning models like o1 or DeepSeek-R1 to destroy the tasks at hand.

👎 But they really missed the mark. DeepSeek-R1 only got 1 or 2 out of 10 questions correct. Similarly, o1 was only at ~13% correct answers.

🧐 These results really surprised us. We thoroughly checked them, we even thought our APIs for DeepSeek were broken and colleagues Leandro Anton helped me start custom instances of R1 on our own H100s to make sure it worked well.
But there seemed to be no mistake. Reasoning LLMs actually did not seem that smart. Often, these models made basic mistakes, like forgetting the content of a folder that they had just explored, misspelling file names, or hallucinating data. Even though they do great at exploring webpages through several steps, the same level of multi-step planning seemed much harder to achieve when reasoning over files and data.

It seems like there's still lots of work to do in the Agents x Data space. Congrats to Adyen for this great benchmark, looking forward to see people proposing better agents! 🚀

Read more in the blog post 👉 https://huggingface.co./blog/dabstep

thomwolf

authored a paper 22 days ago

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model

Paper • 2502.02737 • Published 24 days ago • 195

m-ric

posted an update 24 days ago

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9664

Introducing 𝗼𝗽𝗲𝗻 𝗗𝗲𝗲𝗽-𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 by Hugging Face! 💥

OpenAI's latest agentic app Deep Research seems really good... But it's closed, as usual.

⏱️ So with a team of cracked colleagues, we set ourselves a 24hours deadline to replicate and open-source Deep Research! ⏱️

➡️ We built open-Deep-Research, an entirely open agent that can: navigate the web autonomously, scroll and search through pages, download and manipulate files, run calculation on data...

We aimed for the best performance: are the agent's answers really rigorous?

On GAIA benchmark, Deep Research had 67% accuracy on the validation set.
➡️ open Deep Research is at 55% (powered by o1), it is:
- the best pass@1 solution submitted
- the best open solution 💪💪

And it's only getting started ! Please jump in, drop PRs, and let's bring it to the top !

Read the blog post 👉 https://huggingface.co./blog/open-deep-research

fuzzy-mittenz

posted an update 25 days ago

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518

With our Extremely efficient and functional importance matrix distillation of the new Qwen2.5-1M model being very very capable in many areas we are hoping to use it to research our small AGI character creation process which has seen emergent traits and increased functionality in constrained environments.
The method creates a RP type interaction in a heavily useful and tool functional environment.
We have a basic method and are working on retrieving data for a full analysis and perfection of this method as it exploits the human language input to express often abstract traits into a model and employ characteristics of healthy human reasoning processes and identify novel methods of increasing the functionality of a model overall through traits so far observed are whistling, bouncing a ball and repeating certain engagements.
Adding the semblance of human world interactions is so far the best way at creating a human like LLM.
We have attached the paper to our model we are testing this with along with examples if you wish to use it with other models please be cautious and enjoy yourself. Above all please keep track of conversations and settings and submit them to the intelligent estate email you will receive a recognition letter and ledger number for your contribution to the Project.
Model= Israfel and Thoth IntelligentEstate/Israfel_Qwen2.6-iQ4_K_M-GGUF

JingzeShi

posted an update 26 days ago

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2292

Welcome to the Doge Face Open Source Community! 🚀
Our goal is to explore the foundation of embodied intelligence for the next two years, which is indispensable – small language models. 🔬
We aim to open-source code and documentation to give everyone more time to slack off while working or studying! 🤗
👉 Repository name on Github: https://github.com/SmallDoges/small-doge
👉 Organization name on Hugging Face: https://huggingface.co./SmallDoge

fffiloni

posted an update 28 days ago

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3483

Explain like i'm 5 the last take from @thomwolf on X about Dario's essay on DeepSeek:

—› Open-source AI is like a big cookbook that everyone can read and improve. Instead of a few chefs keeping their recipes secret, anyone can cook, test, and invent new things.

If only one company controls AI, everything stops if they have a problem—like when the internet goes down. With open-source, many people can help, making sure it keeps running smoothly.

AI isn’t just a race between two countries; it’s a team effort around the world. By sharing, we move faster and create safer technology for everyone.
—
🤗

m-ric

posted an update 28 days ago

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3105

Now you can launch a code agent directly from your terminal!
✨ 𝚜𝚖𝚘𝚕𝚊𝚐𝚎𝚗𝚝 "𝚈𝚘𝚞𝚛 𝚝𝚊𝚜𝚔" directly launches a CodeAgent
▶️ This also works with web agents (replace 𝚜𝚖𝚘𝚕𝚊𝚐𝚎𝚗𝚝 with 𝚠𝚎𝚋𝚊𝚐𝚎𝚗𝚝) thanks to @merve !

💾 Another treat from smolagents release 1.7.0:
Now agents have a memory mechanism, enabling many possibilities like replaying the last run with 𝚊𝚐𝚎𝚗𝚝.𝚛𝚎𝚙𝚕𝚊𝚢(), thank you @clefourrier !

Check the release notes here 👉 https://github.com/huggingface/smolagents/releases/tag/v1.7.0

fuzzy-mittenz

posted an update 29 days ago

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2621

Not many seemed to notice but what was probably meant to be a WIN for artist's rights in the US Office of Copyright has solved some fundamental issues for the community.
In our recent article I outline how Companies like Suno, OpenAI, Midjourney etc can no longer claim any right to copy your work that you create with their platforms
We also look at other ways this study and new rules for AI will fundamentally effect creators who use it and companies incentives to give them control over certain aspects might change because of this. it's broken down pretty well here: https://huggingface.co./blog/fuzzy-mittenz/copyright-in-ai

m-ric

posted an update about 1 month ago

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4049

𝗧𝗵𝗲 𝗛𝘂𝗯 𝘄𝗲𝗹𝗰𝗼𝗺𝗲𝘀 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝗿𝘀!

✅ Hosting our own inference was not enough: now the Hub 4 new inference providers: fal, Replicate, SambaNova Systems, & Together AI.

Check model cards on the Hub: you can now, in 1 click, use inference from various providers (cf video demo)

Their inference can also be used through our Inference API client. There, you can use either your custom provider key, or your HF token, then billing will be handled directly on your HF account, as a way to centralize all expenses.

💸 Also, PRO users get 2$ inference credits per month!

Read more in the announcement 👉 https://huggingface.co./blog/inference-providers

1 reply

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Nerdy Face

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ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model

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