Gradio-Blocks (Gradio-Blocks-Party)

AtAndDev

posted an update 3 days ago

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172

Qwen 3 Coder is a personal attack to k2, and I love it.
It achieves near SOTA on LCB while not having reasoning.
Finally people are understanding that reasoning isnt necessary for high benches...

Qwen ftw!

DECENTRALIZE DECENTRALIZE DECENTRALIZE

Tonic

posted an update 5 days ago

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559

👋 Hey there folks,

just submitted my plugin idea to the G-Assist Plugin Hackathon by @nvidia . Check it out, it's a great way to use a local SLA model on a windows machine to easily and locally get things done ! https://github.com/NVIDIA/G-Assist

Tonic

posted an update 7 days ago

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466

🙋🏻‍♂️ Hey there folks ,

Yesterday , Nvidia released a reasoning model that beats o3 on science, math and coding !

Today you can try it out here : Tonic/Nvidia-OpenReasoning

hope you like it !

Abhaykoul

posted an update 10 days ago

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2914

🎉 Dhanishtha-2.0-preview-0725 is Now Live

The Intermediate Thinking Model just got even better.
With the new update, Dhanishtha is now sharper, smarter, and trained further on tool use

🧠 What Makes Dhanishtha Different?
Unlike standard COT models that give one-shot responses, Dhanishtha thinks in layers:

> Think → Answer → Rethink → Improve → Rethink again if needed.

HelpingAI/Dhanishtha-2.0-preview-0725

Tonic

posted an update 13 days ago

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3236

🙋🏻‍♂️ Normalize adding compute & runtime traces to your model cards

2 replies

·

taesiri

authored a paper 15 days ago

A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality

Paper • 2507.07202 • Published 16 days ago • 22

Tonic

posted an update 19 days ago

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473

Who's going to Raise Summit in Paris Tomorrow ?

If you're around , I would love to meet you :-)

Abhaykoul

posted an update 24 days ago

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2955

🎉 Dhanishtha 2.0 Preview is Now Open Source!

The world's first Intermediate Thinking Model is now available to everyone!

Dhanishtha 2.0 Preview brings revolutionary intermediate thinking capabilities to the open-source community. Unlike traditional reasoning models that think once, Dhanishtha can think, answer, rethink, answer again, and continue rethinking as needed using multiple blocks between responses.

🚀 Key Features
- Intermediate thinking: Think → Answer → Rethink → Answer → Rethink if needed...
- Token efficient: Uses up to 79% fewer tokens than DeepSeek R1 on similar queries
- Transparent thinking: See the model's reasoning process in real-time
- Open source: Freely available for research and development

HelpingAI/Dhanishtha-2.0-preview
https://helpingai.co/chat

1 reply

·

Abhaykoul

posted an update about 1 month ago

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4330

Introducing Dhanishtha 2.0: World's first Intermediate Thinking Model

Dhanishtha 2.0 is the world's first LLM designed to think between the responses. Unlike other Reasoning LLMs, which think just once.

Dhanishtha can think, rethink, self-evaluate, and refine in between responses using multiple <think> blocks.
This technique makes it Hinghlt Token efficient it Uses up to 79% fewer tokens than DeepSeek R1
---

You can try our model from: https://helpingai.co/chat
Also, we're gonna Open-Source Dhanistha on July 1st.

---
For Devs:
🔑 Get your API key at https://helpingai.co/dashboard

from HelpingAI import HAI  # pip install HelpingAI==1.1.1
from rich import print

hai = HAI(api_key="hl-***********************")

response = hai.chat.completions.create(
    model="Dhanishtha-2.0-preview",
    messages=[{"role": "user", "content": "What is the value of ∫0∞𝑥3/𝑥−1𝑑𝑥 ?"}],
    stream=True,
    hide_think=False # Hide or show models thinking
)

for chunk in response:
    print(chunk.choices[0].delta.content, end="", flush=True)

2 replies

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Tonic

posted an update about 2 months ago

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680

🙋🏻‍♂️ hey there folks ,

So every bio/med/chem meeting i go to i always the same questions "why are you sharing a gdrive link with me for this?" and "Do you have any plans to publish your model weights and datasets on huggingface?" and finally i got a good answer today which explains everything :

basically there is some kind of government censorship on this (usa, but i'm sure others too) and they are told they are not allowed as it is considered a "dataleak" which is illegal !!!!

this is terrible ! but the good news is that we can do something about it !

so there is this "call for opinions and comments" here from the NIH (usa) , and here we can make our opinion on this topic known : https://osp.od.nih.gov/comment-form-responsibly-developing-and-sharing-generative-artificial-intelligence-tools-using-nih-controlled-access-data/

kindly consider dropping your opinion and thoughts about this censorship of science , and share this post , link or thoughts widely .

Together maybe we can start to share data and model weights appropriately and openly in a good way 🙏🏻🚀

cc. @cyrilzakka

AtAndDev

posted an update about 2 months ago

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2883

deepseek-ai/DeepSeek-R1-0528

This is the end

1 reply

·

mariagrandury

authored 2 papers 2 months ago

Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation

Paper • 2504.07072 • Published Apr 9 • 9

It's the same but not the same: Do LLMs distinguish Spanish varieties?

Paper • 2504.20049 • Published Apr 8

Tonic

posted an update 2 months ago

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2533

🙋🏻‍♂️ Hey there folks ,

Yesterday the world's first "Learn to Vibe Code" application was released .

As vibe coding is the mainstream paradigm , so now the first educational app is there to support it .

You can try it out already :

https://vibe.takara.ai

and of course it's entirely open source, so i already made my issue and feature branch :-) 🚀

hannayukhymenko

posted an update 3 months ago

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3582

🚀 We are delighted to announce MamayLM, a new state-of-the-art efficient Ukrainian LLM!

📈 MamayLM surpasses similar-sized models in both English and Ukrainian, while matching or overtaking up to 10x larger models.

📊 MamayLM is a 9B model that can run on a single GPU, enabling cost-efficient AI autonomy and adoption across sectors in Ukraine such as education, legal, healthcare, public services and others (e.g., by specializing it to particular use cases). MalayLM is also attractive for organizations wishing to preserve data privacy as it s efficiency allows it to run on a local machine.

🧠 MamayLM is trained on high-quality Ukrainian data and understands Ukrainian language, culture, and history. It is built on top of Google’s Gemma 2 9B model, but uses a number of new advances stemming from INSAIT’s experience in creating BgGPT, a Bulgarian LLM we released last year, now adopted nationwide and profiled several times by Google as a worldwide success case.

🤝 MamayLM is developed in a collaboration between researchers at INSAIT and ETH Zürich and is trained entirely via donations to INSAIT for AI compute resources.

📥 MamayLM is now freely available to download on INSAIT’s HuggingFace in both full and quantized versions. We also publicly release all Ukrainian benchmarks we evaluated on.

📝 Further, we release blog posts in both English and Ukrainian, sharing our approach to creating MamayLM, hoping to drive further improvements by the community.

🌎 The release of LLMs for various languages is part of INSAIT’s mission in ensuring countries can achieve AI autonomy in a cost-efficient, controlled, safe and predictable manner.

MamayLM model and benchmarks: