si c'est un sujet qui t'interesse je suis absolutement disponible pour en parler 🤗
Joseph [open/acc] Pollack
Tonic
AI & ML interests
🤖Making robots to help people learn things quicker 👩🏻🚀🚀
Recent Activity
liked
a dataset
about 1 hour ago
miromind-ai/MiroRL-GenQA
published
a Space
about 1 hour ago
Tonic/on-device-operator
updated
a Space
about 2 hours ago
Tonic/operator-on-device
Organizations
replied to
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post
14 days ago
posted
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update
16 days ago
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3306
🫡 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
that's it , that's the post
find the dataset here : louisbrulenaudet/code-impots
follow : @louisbrulenaudet
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- 2 replies
reacted to
merve's
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16 days ago
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2183
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
> 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
it's really mindblowing that this week's updates are uniquely from china and completely opensource <3
reacted to
merve's
post with 🔥
18 days ago
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3571
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
💬 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
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reacted to
Wauplin's
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23 days ago
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2915
Say hello to
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
The full rationale, implementation details, and migration notes are in the blog post: https://huggingface.co/blog/hf-cli
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 🔥
27 days ago
Post
7967
🧬 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
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
- 5 replies
posted
an
update
29 days ago
Post
727
👋 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
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
Post
564
🙋🏻♂️ 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 !
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
Post
4083
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
The compact version is as follows — Poseidon-Reasoning-Mini-300K : prithivMLmods/Poseidon-Reasoning-Mini-300K
Collection : prithivMLmods/poseidon-reasoning-6879ca98e118b307c781a9ba
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
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about 1 month ago
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3313
🙋🏻♂️ Normalize adding compute & runtime traces to your model cards
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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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2892
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 🥰🥰
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 🥰🥰
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prithivMLmods's
post with 👍
about 1 month ago
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3860
Excited to bring the new models that are performing exceptionally well in document OCR, image captioning, and visual understanding tasks. Megalodon-OCR and Perseus-Doc-VL have both demonstrated significant improvements across key areas. You can explore live demos on Hugging Face Spaces to compare their performance with other top-tier models available on the hub. 🤗📄
Models & Spaces :
> Megalodon-OCR (3B) : prithivMLmods/Megalodon-OCR-Sync-0713
> Perseus-Doc-vl (7B): prithivMLmods/Perseus-Doc-vl-0712
> Doc-VLMs-OCR : https://huggingface.co/spaces/prithivMLmods/Multimodal-VLM-OCR
> core-OCR : prithivMLmods/core-OCR
Datasets Caption Mix :
> Corvus-OCR-Caption-Mix : prithivMLmods/Corvus-OCR-Caption-Mix
> Corvus-OCR-Caption-Mini-Mix : prithivMLmods/Corvus-OCR-Caption-Mini-Mix
Collections :
> Corvus OCR Caption Mix: prithivMLmods/corvus-ocr-caption-mix-687349bfaceffbd10976f0cc
> Captioning / OCR / DocTable : prithivMLmods/captioning-ocr-doctable-687382e1da822008bb5c06f2
GitHub :
> OCR-ReportLab : https://github.com/PRITHIVSAKTHIUR/OCR-ReportLab/blob/main/Megalodon-OCR-Sync-0713-ColabNotebook/Megalodon_OCR_Sync_0713_ReportLab.ipynb
Others Spaces :
> Multimodal-OCR : prithivMLmods/Multimodal-OCR
> Multimodal-VLMs : https://huggingface.co/spaces/prithivMLmods/Multimodal-OCR-Outpost
> Multimodal-OCR2 : prithivMLmods/Multimodal-OCR2
> Florence-2-Image-Caption : prithivMLmods/Florence-2-Image-Caption
> VisionScope-R2 : prithivMLmods/VisionScope-R2
> DocScope-R1 : prithivMLmods/DocScope-R1
.
.
.
To know more about it, visit the model card of the respective model. !!
Models & Spaces :
> Megalodon-OCR (3B) : prithivMLmods/Megalodon-OCR-Sync-0713
> Perseus-Doc-vl (7B): prithivMLmods/Perseus-Doc-vl-0712
> Doc-VLMs-OCR : https://huggingface.co/spaces/prithivMLmods/Multimodal-VLM-OCR
> core-OCR : prithivMLmods/core-OCR
Datasets Caption Mix :
> Corvus-OCR-Caption-Mix : prithivMLmods/Corvus-OCR-Caption-Mix
> Corvus-OCR-Caption-Mini-Mix : prithivMLmods/Corvus-OCR-Caption-Mini-Mix
Collections :
> Corvus OCR Caption Mix: prithivMLmods/corvus-ocr-caption-mix-687349bfaceffbd10976f0cc
> Captioning / OCR / DocTable : prithivMLmods/captioning-ocr-doctable-687382e1da822008bb5c06f2
GitHub :
> OCR-ReportLab : https://github.com/PRITHIVSAKTHIUR/OCR-ReportLab/blob/main/Megalodon-OCR-Sync-0713-ColabNotebook/Megalodon_OCR_Sync_0713_ReportLab.ipynb
Others Spaces :
> Multimodal-OCR : prithivMLmods/Multimodal-OCR
> Multimodal-VLMs : https://huggingface.co/spaces/prithivMLmods/Multimodal-OCR-Outpost
> Multimodal-OCR2 : prithivMLmods/Multimodal-OCR2
> Florence-2-Image-Caption : prithivMLmods/Florence-2-Image-Caption
> VisionScope-R2 : prithivMLmods/VisionScope-R2
> DocScope-R1 : prithivMLmods/DocScope-R1
.
.
.
To know more about it, visit the model card of the respective model. !!
posted
an
update
about 1 month ago
Post
3313
🙋🏻♂️ Normalize adding compute & runtime traces to your model cards
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- 2 replies
replied to
nroggendorff's
post
about 1 month ago
a couple months after you got banned for impersonating hf staff lmfao
posted
an
update
about 1 month ago
Post
502
Who's going to Raise Summit in Paris Tomorrow ?
If you're around , I would love to meet you :-)
If you're around , I would love to meet you :-)
reacted to
merve's
post with 🧠
2 months ago
Post
3596
IN: video fine-tuning support for
facebook
V-JEPA 2 in HF transformers 🔥
it comes with
> four models fine-tuned on Diving48 and SSv2 dataset facebook/v-jepa-2-6841bad8413014e185b497a6
> FastRTC demo on V-JEPA2 SSv2 qubvel-hf/vjepa2-streaming-video-classification
> fine-tuning script on UCF-101 https://gist.github.com/ariG23498/28bccc737c11d1692f6d0ad2a0d7cddb
> fine-tuning notebook on UCF-101 https://colab.research.google.com/drive/16NWUReXTJBRhsN3umqznX4yoZt2I7VGc?usp=sharing
we're looking forward to see what you will build! 🤗
facebook
V-JEPA 2 in HF transformers 🔥it comes with
> four models fine-tuned on Diving48 and SSv2 dataset facebook/v-jepa-2-6841bad8413014e185b497a6
> FastRTC demo on V-JEPA2 SSv2 qubvel-hf/vjepa2-streaming-video-classification
> fine-tuning script on UCF-101 https://gist.github.com/ariG23498/28bccc737c11d1692f6d0ad2a0d7cddb
> fine-tuning notebook on UCF-101 https://colab.research.google.com/drive/16NWUReXTJBRhsN3umqznX4yoZt2I7VGc?usp=sharing
we're looking forward to see what you will build! 🤗