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Joseph [open/acc] Pollack

Tonic

https://discord.gg/qdfnvSPcqP

AI & ML interests

🤖Making robots to help people learn things quicker 👩🏻‍🚀🚀

Recent Activity

liked a model about 10 hours ago

LiquidAI/LFM2-350M

reacted to pagezyhf's post with 👍 about 17 hours ago

We've improved the Deploy button on Hugging Face model pages for Microsoft Azure 1/ no more long waits before seeing model support status 2/ ready-to-use CLI and Python snippets 3/ redirection to Azure AI Foundry rather than Azure ML ✋ if you see any bugs or have feedback, open an issue on our repo: https://github.com/huggingface/Microsoft-Azure

new activity 1 day ago

mradermacher/Deepseek-V2-13B-Math7K-Expert-Enhance-Subset-Expert-MoE-32-experts-i1-GGUF:the name just rolls off your tongue though doesnt it

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reacted to pagezyhf's post with 👍 about 17 hours ago

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186

We've improved the Deploy button on Hugging Face model pages for Microsoft Azure

1/ no more long waits before seeing model support status

2/ ready-to-use CLI and Python snippets

3/ redirection to Azure AI Foundry rather than Azure ML

✋ if you see any bugs or have feedback, open an issue on our repo:
https://github.com/huggingface/Microsoft-Azure

replied to their post 17 days ago

si c'est un sujet qui t'interesse je suis absolutement disponible pour en parler 🤗

posted an update 19 days ago

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🫡 I am the first and only one to like the French Tax Code Dataset

that's it , that's the post

find the dataset here : louisbrulenaudet/code-impots
follow : @louisbrulenaudet

2 replies

reacted to merve's post with 👍 19 days ago

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Cohere just dropped CohereLabs/command-a-vision-07-2025, a 112B (dense!) vision LM
> based on SigLIP2 & Command-A
> built for enterprise use cases 🔥
> use with Inference Providers or transformers 🤗
read their blog https://huggingface.co/blog/CohereLabs/introducing-command-a-vision-07-2025

2 replies

replied to merve's post 21 days ago

it's really mindblowing that this week's updates are uniquely from china and completely opensource <3

reacted to merve's post with 🔥 21 days ago

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past week in open AI was insane 🔥 here's some of picks, find more here merve/releases-july-25-688768ca47fe3693407e02d1

💬 LLMs & VLMs
> Qwen/Qwen3-235B-A22B-Thinking-2507 had a new update (OS)
> Qwen/Qwen3-Coder-480B-A35B-Instruct is out with 480B total 35B active params 🤯 (OS)
> AllenAI dropped an update to allenai/olmOCR-7B-0725 📝
> InternLM released internlm/Intern-S1 - 235B Qwen3 MoE + 6B InternViT encoder (OS)
> OmniSVG/OmniSVG is a new SVG generation VLM (OS)

🖼️ image/video/3D generation
> WanAI released Wan2.2 series - both T2V and I2V 14B models for high-quality video generation (OS) multimodalart/wan-22-688767e313337b434ed55112
> Tencent dropped tencent/HunyuanWorld-1 - image-to-3D scene generation model

1 reply

reacted to Wauplin's post with 🤝 26 days ago

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Say hello to hf: a faster, friendlier Hugging Face CLI ✨

We are glad to announce a long-awaited quality-of-life improvement: the Hugging Face CLI has been officially renamed from huggingface-cli to hf!

So... why this change?

Typing huggingface-cli constantly gets old fast. More importantly, the CLI’s command structure became messy as new features were added over time (upload, download, cache management, repo management, etc.). Renaming the CLI is a chance to reorganize commands into a clearer, more consistent format.

We decided not to reinvent the wheel and instead follow a well-known CLI pattern: hf <resource> <action>. Isn't hf auth login easier to type and remember?

The full rationale, implementation details, and migration notes are in the blog post: https://huggingface.co/blog/hf-cli

reacted to MaziyarPanahi's post with 🔥 30 days ago

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🧬 Breaking news in Clinical AI: Introducing the OpenMed NER Model Discovery App on Hugging Face 🔬

OpenMed is back! 🔥 Finding the right biomedical NER model just became as precise as a PCR assay!

I'm thrilled to unveil my comprehensive OpenMed Named Entity Recognition Model Discovery App that puts 384 specialized biomedical AI models at your fingertips.

🎯 Why This Matters in Healthcare AI:
Traditional clinical text mining required hours of manual model evaluation. My Discovery App instantly connects researchers, clinicians, and data scientists with the exact NER models they need for their biomedical entity extraction tasks.

🔬 What You Can Discover:
✅ Pharmacological Models - Extract "chemical compounds", "drug interactions", and "pharmaceutical" entities from clinical notes
✅ Genomics & Proteomics - Identify "DNA sequences", "RNA transcripts", "gene variants", "protein complexes", and "cell lines"
✅ Pathology & Disease Detection - Recognize "pathological formations", "cancer types", and "disease entities" in medical literature
✅ Anatomical Recognition - Map "anatomical systems", "tissue types", "organ structures", and "cellular components"
✅ Clinical Entity Extraction - Detect "organism species", "amino acids", 'protein families", and "multi-tissue structures"

💡 Advanced Features:
🔍 Intelligent Entity Search - Find models by specific biomedical entities (e.g., "Show me models detecting CHEM + DNA + Protein")
🏥 Domain-Specific Filtering - Browse by Oncology, Pharmacology, Genomics, Pathology, Hematology, and more
📊 Model Architecture Insights - Compare BERT, RoBERTa, and DeBERTa implementations
⚡ Real-Time Search - Auto-filtering as you type, no search buttons needed
🎨 Clinical-Grade UI - Beautiful, intuitive interface designed for medical professionals

Ready to revolutionize your biomedical NLP pipeline?

🔗 Try it now: OpenMed/openmed-ner-models
🧬 Built with: Gradio, Transformers, Advanced Entity Mapping

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posted an update about 1 month ago

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👋 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

posted an update about 1 month ago

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🙋🏻‍♂️ 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 !

reacted to prithivMLmods's post with ❤️ about 1 month ago

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Dropping the general-purpose reasoning dataset Poseidon-Reasoning-5M, which supports general thought processes, math, and science — featuring a diverse mixture of domains 🌊 : prithivMLmods/Poseidon-Reasoning-5M

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Poseidon-Reasoning-5M", split="data")

The compact version is as follows — Poseidon-Reasoning-Mini-300K : prithivMLmods/Poseidon-Reasoning-Mini-300K

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Poseidon-Reasoning-Mini-300K", split="train")

Collection : prithivMLmods/poseidon-reasoning-6879ca98e118b307c781a9ba

reacted to their post with 👍 about 1 month ago

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

2 replies

replied to hba123's post about 1 month ago

thanks for the authentic and artisanal walkthrough & write-up, i'll definitely be following along , and btw i'm hosting the latest prover from kimi moonshot team on my profile page in case you want to add it to your leaderboard , it's quite different than other provers , but i also think it's quite good for entry-level people like me (because it combines theorem proofs with natural language explanations, so it really becomes a great learning tool)

reacted to hba123's post with 👍❤️🧠 about 1 month ago

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In our latest paper, Bourbaki (7b), we show how one can achieve state-of-the-art 7B theorem provers on PutnamBench by applying MCTS to what we call self-generated and goal-conditioned MDPs. I started a series of Blogs on this!

Why a series of Blogs 😝? I want to try to make everyone understand what Bourbaki (7b) is and what it does. I don't want to just give you a ChatGPT summary with some result hype. I think there are many things to improve, and I am hoping with more exposure to this, beyond experiments and codes, some people would be interested and help us improve it!

In this first blog, we will be talking basics: 1) MCTS and why it should be applied to LLMs so that the whole world is not just fine-tuning a 100000000000000000000000 b model on 10 data points (not that i have not done it before 🤪🤪), 2) the basics of MDPs, and 3) the Vanilla MCTS algorithm.

Check it out: https://huggingface.co/blog/hba123/bourbaki7b

If you find it useful, consider upvoting and sharing this post and the Hugging Face blog! Thank you 🥰🥰