Post
1291
5 years already working in democratizing AI 🤗
Grateful to be part of such an awesome team making it happen every day.
Grateful to be part of such an awesome team making it happen every day.
Aritra Roy Gosthipaty PRO
ariG23498
AI & ML interests
Deep Representation Learning
Recent Activity
reacted
to
albertvillanova's
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with 🚀
about 23 hours ago
5 years already working in democratizing AI 🤗
Grateful to be part of such an awesome team making it happen every day.
reacted
to
albertvillanova's
post
with 🤗
about 23 hours ago
5 years already working in democratizing AI 🤗
Grateful to be part of such an awesome team making it happen every day.
liked
a Space
about 23 hours ago
FireRedTeam/FireRed-Image-Edit-1.0
Organizations
reacted to
albertvillanova's
post with 🚀🤗
about 23 hours ago
reacted to
pcuenq's
post with 🔥
about 1 month ago
Post
3347
👉 What happened in AI in 2025? 👈
We prepared the 2025 version of the HF AI Timeline Grid, highlighting open vs API-based model releases, and allowing you to browse and filter by access, modality, and release type!
Play with it here:
2025-ai-timeline/2025-ai-timeline
Here's my personal quarterly TL;DR:
1️⃣ Q1 — Learning to Reason
Deepseek not only releases a top-notch reasoning model, but shows how to train them and compete with closed frontier models. OpenAI debuts Deep Research.
Significant milestones: DeepSeek R1 & R1-Zero, Qwen 2.5 VL, OpenAI Deep Research, Gemini 2.5 Pro (experimental)
2️⃣ Q2 — Multimodality and Coding
More LLMs embrace multimodality by default, and there's a surge in coding agents. Strong vision, audio, and generative models emerge.
Significant milestones: Llama 4, Qwen 3, Imagen 4, OpenAI Codex, Google Jules, Claude 4
3️⃣ Q3 — "Gold" rush, OpenAI opens up, the community goes bananas
Flagship models get gold in Math olympiads and hard benchmarks. OpenAI releases strong open source models and Google releases the much anticipated nano-banana for image generation and editing. Agentic workflows become commonplace.
Significant milestones: Gemini and OpenAI IMO Gold, gpt-oss, Gemini 2.5 Flash Image, Grok 4, Claude Sonnet 4.5
4️⃣ Q4 — Mistral returns, leaderboard hill-climbing
Mistral is back with updated model families. All labs release impressive models to wrap up the year!
Significant milestones: Claude Opus 4.5, DeepSeek Math V2, FLUX 2, GPT 5.1, Kimi K2 Thinking, Nano Banana Pro, GLM 4.7, Gemini 3, Mistral 3, MiniMax M2.1 🤯
Credits
🙏 NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline
🫡 @reach-vb for the original idea, design and recipe
🙌 @ariG23498 and yours truly for compiling and verifying the 2025 edition
🥳 Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! 🥂
We prepared the 2025 version of the HF AI Timeline Grid, highlighting open vs API-based model releases, and allowing you to browse and filter by access, modality, and release type!
Play with it here:
2025-ai-timeline/2025-ai-timeline
Here's my personal quarterly TL;DR:
1️⃣ Q1 — Learning to Reason
Deepseek not only releases a top-notch reasoning model, but shows how to train them and compete with closed frontier models. OpenAI debuts Deep Research.
Significant milestones: DeepSeek R1 & R1-Zero, Qwen 2.5 VL, OpenAI Deep Research, Gemini 2.5 Pro (experimental)
2️⃣ Q2 — Multimodality and Coding
More LLMs embrace multimodality by default, and there's a surge in coding agents. Strong vision, audio, and generative models emerge.
Significant milestones: Llama 4, Qwen 3, Imagen 4, OpenAI Codex, Google Jules, Claude 4
3️⃣ Q3 — "Gold" rush, OpenAI opens up, the community goes bananas
Flagship models get gold in Math olympiads and hard benchmarks. OpenAI releases strong open source models and Google releases the much anticipated nano-banana for image generation and editing. Agentic workflows become commonplace.
Significant milestones: Gemini and OpenAI IMO Gold, gpt-oss, Gemini 2.5 Flash Image, Grok 4, Claude Sonnet 4.5
4️⃣ Q4 — Mistral returns, leaderboard hill-climbing
Mistral is back with updated model families. All labs release impressive models to wrap up the year!
Significant milestones: Claude Opus 4.5, DeepSeek Math V2, FLUX 2, GPT 5.1, Kimi K2 Thinking, Nano Banana Pro, GLM 4.7, Gemini 3, Mistral 3, MiniMax M2.1 🤯
Credits
🙏 NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline
🫡 @reach-vb for the original idea, design and recipe
🙌 @ariG23498 and yours truly for compiling and verifying the 2025 edition
🥳 Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! 🥂
- 2 replies
reacted to
sergiopaniego's
post with 🚀
about 2 months ago
Post
1912
Google DeepMind releases FunctionGemma, a 240M model specialized in 🔧 tool calling, built for fine-tuning
TRL has day-0 support. To celebrate, we’re sharing 2 new resources:
> Colab guide to fine-tune it for 🌐 browser control with BrowserGym OpenEnv
> Standalone training script
> Colab notebook: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/grpo_functiongemma_browsergym_openenv.ipynb
> Training script: https://github.com/huggingface/trl/blob/main/examples/scripts/openenv/browsergym_llm.py (command to run it inside the script)
> More notebooks in TRL: https://huggingface.co/docs/trl/example_overview#notebooks
TRL has day-0 support. To celebrate, we’re sharing 2 new resources:
> Colab guide to fine-tune it for 🌐 browser control with BrowserGym OpenEnv
> Standalone training script
> Colab notebook: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/grpo_functiongemma_browsergym_openenv.ipynb
> Training script: https://github.com/huggingface/trl/blob/main/examples/scripts/openenv/browsergym_llm.py (command to run it inside the script)
> More notebooks in TRL: https://huggingface.co/docs/trl/example_overview#notebooks
reacted to
Kseniase's
post with 👍
2 months ago
Post
4207
15 Outstanding Research Papers from NeurIPS 2025
NeurIPS 2025, as a premier annual event in machine learning and computational neuroscience, tackles major topics like the future of AI, current research, and the most difficult challenges. While we’re not attending this year, we’re closely following the updates and today we pull together a quick, easy-to-digest roundup of a few standout papers so you can jump in without getting overwhelmed.
Here is a list of 15 papers from NeurIPS 2025, including 8 top research papers that received awards, along with 7 others that caught our attention:
1. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks → https://neurips.cc/virtual/2025/loc/san-diego/test-of-time/128328
Test of Time Award winner. Introduces the RPN, a small convnet that predicts objectness and boxes on shared features, enabling Faster R-CNN to share computation and run around 5 fps on a GPU
2. Artificial Hivemind: The Open-Ended Homogeneity of LMs (and Beyond) → https://neurips.cc/virtual/2025/loc/san-diego/poster/121421
Releases a huge open-ended prompt dataset, showing that LLMs often fall into an “artificial hivemind” – generate surprisingly similar answers – and measuring diversity collapse
3. Optimal Mistake Bounds for Transductive Online Learning → https://neurips.cc/virtual/2025/loc/san-diego/poster/119098
Settles a 30-year-old question by showing how much unlabeled data helps in online learning – it gives a precise quadratic advantage with tight matching bounds
4. Gated Attention for LLMs: Non-linearity, Sparsity, and Attention-Sink-Free → https://neurips.cc/virtual/2025/loc/san-diego/poster/120216
Demonstrates how gating actually affects attention: a simple sigmoid gate after Scaled Dot-Product Attention (SDPA) boosts performance, stability, and long-context behavior by adding useful nonlinearity and sparse modulation
Read further below ⬇️
Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
NeurIPS 2025, as a premier annual event in machine learning and computational neuroscience, tackles major topics like the future of AI, current research, and the most difficult challenges. While we’re not attending this year, we’re closely following the updates and today we pull together a quick, easy-to-digest roundup of a few standout papers so you can jump in without getting overwhelmed.
Here is a list of 15 papers from NeurIPS 2025, including 8 top research papers that received awards, along with 7 others that caught our attention:
1. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks → https://neurips.cc/virtual/2025/loc/san-diego/test-of-time/128328
Test of Time Award winner. Introduces the RPN, a small convnet that predicts objectness and boxes on shared features, enabling Faster R-CNN to share computation and run around 5 fps on a GPU
2. Artificial Hivemind: The Open-Ended Homogeneity of LMs (and Beyond) → https://neurips.cc/virtual/2025/loc/san-diego/poster/121421
Releases a huge open-ended prompt dataset, showing that LLMs often fall into an “artificial hivemind” – generate surprisingly similar answers – and measuring diversity collapse
3. Optimal Mistake Bounds for Transductive Online Learning → https://neurips.cc/virtual/2025/loc/san-diego/poster/119098
Settles a 30-year-old question by showing how much unlabeled data helps in online learning – it gives a precise quadratic advantage with tight matching bounds
4. Gated Attention for LLMs: Non-linearity, Sparsity, and Attention-Sink-Free → https://neurips.cc/virtual/2025/loc/san-diego/poster/120216
Demonstrates how gating actually affects attention: a simple sigmoid gate after Scaled Dot-Product Attention (SDPA) boosts performance, stability, and long-context behavior by adding useful nonlinearity and sparse modulation
Read further below ⬇️
Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
- 1 reply
reacted to
prithivMLmods's
post with 🤗
2 months ago
Post
3749
Hello everyone,
The
strangerzonehf
[HF] Community / Organization Page, which is maintained by me, has reached the Top 10 Developer Pages ranking at 6th place, contributing 3.4% in the calendar cycle from August 2024 to August 2025. It is also the only South Asia / Indian page in the list. I could not be more proud to be doing things for the community. ❤️🤗
Source: https://www.dataprovenance.org/economies-of-open-intelligence.pdf
It is a pleasure to be a part of it.
Thank you!
@prithivMLmods
The
Source: https://www.dataprovenance.org/economies-of-open-intelligence.pdf
It is a pleasure to be a part of it.
Thank you!
@prithivMLmods
reacted to
mrfakename's
post with 🔥
4 months ago
Post
6246
Trained a model for emotion-controllable TTS based on MiMo audio on LAION's dataset.
Still very early and does have an issue with hallucinating but results seem pretty good so far, given that it is very early into the training run.
Will probably kick off a new run later with some settings tweaked.
Put up a demo here: https://huggingface.co/spaces/mrfakename/EmoAct-MiMo
(Turn 🔊 on to hear audio samples)
Still very early and does have an issue with hallucinating but results seem pretty good so far, given that it is very early into the training run.
Will probably kick off a new run later with some settings tweaked.
Put up a demo here: https://huggingface.co/spaces/mrfakename/EmoAct-MiMo
(Turn 🔊 on to hear audio samples)
- 5 replies
reacted to
sergiopaniego's
post with 🔥
4 months ago
Post
1986
New drop! 💥 The VLM Object Understanding Comparison Space now runs with Qwen3-VL-4B and moondream3.
You can compare how models reason about images 🧠
Bonus: thanks to @ariG23498 , you now get auto-suggested prompts to explore faster.
Let’s gooo
sergiopaniego/vlm_object_understanding
You can compare how models reason about images 🧠
Bonus: thanks to @ariG23498 , you now get auto-suggested prompts to explore faster.
Let’s gooo
sergiopaniego/vlm_object_understanding
reacted to
tsungyi's
post with 🔥
5 months ago
Post
3731
We’re excited to share that Cosmos Reason has surpassed 1 million downloads on Hugging Face!
Cosmos Reason is an open, customizable, commercial-ready 7B-parameter reasoning vision language model (VLM) designed for physical AI. By combining physics understanding, prior knowledge, and common sense reasoning, Cosmos Reason empowers AI agents and robots to operate intelligently in real-world environments.
Key applications already unlocked include:
✅ Automating large-scale dataset curation and annotation
🤖 Powering robot planning and vision-language action (VLA) decision-making
📊 Driving advanced video analytics and actionable insight generation
We’re proud to see a global community of developers using Cosmos Reason to teach robots to think like humans—and we’re just getting started.
⚡ Get started with Cosmos Reason 1 NIM, an easy-to-use microservice for AI model deployment: https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/cosmos-reason1-7b?version=1
📈 See the leaderboard: facebook/physical_reasoning_leaderboard
Cosmos Reason is an open, customizable, commercial-ready 7B-parameter reasoning vision language model (VLM) designed for physical AI. By combining physics understanding, prior knowledge, and common sense reasoning, Cosmos Reason empowers AI agents and robots to operate intelligently in real-world environments.
Key applications already unlocked include:
✅ Automating large-scale dataset curation and annotation
🤖 Powering robot planning and vision-language action (VLA) decision-making
📊 Driving advanced video analytics and actionable insight generation
We’re proud to see a global community of developers using Cosmos Reason to teach robots to think like humans—and we’re just getting started.
⚡ Get started with Cosmos Reason 1 NIM, an easy-to-use microservice for AI model deployment: https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/cosmos-reason1-7b?version=1
📈 See the leaderboard: facebook/physical_reasoning_leaderboard
reacted to
lysandre's
post with 🔥👍🚀
5 months ago
Post
7851
We're kick-starting the process of Transformers v5, with
@ArthurZ
and
@cyrilvallez
!
v5 should be significant: we're using it as a milestone for performance optimizations, saner defaults, and a much cleaner code base worthy of 2025.
Fun fact: v4.0.0-rc-1 came out on Nov 19, 2020, nearly five years ago!
v5 should be significant: we're using it as a milestone for performance optimizations, saner defaults, and a much cleaner code base worthy of 2025.
Fun fact: v4.0.0-rc-1 came out on Nov 19, 2020, nearly five years ago!
- 6 replies
reacted to
sergiopaniego's
post with 🧠🧠
5 months ago
Post
4346
gpt-oss was possible thanks to new engineering efforts in 🤗 transformers. We just dropped a blog covering them:
- Kernels from the Hub
- MXFP4 Quantization
- Tensor & Expert Parallelism
- Dynamic Sliding Window & Cache
- Continuous Batching & Paged Attention
Grab a coffee & dive in! ☕️
https://huggingface.co/blog/faster-transformers
- Kernels from the Hub
- MXFP4 Quantization
- Tensor & Expert Parallelism
- Dynamic Sliding Window & Cache
- Continuous Batching & Paged Attention
Grab a coffee & dive in! ☕️
https://huggingface.co/blog/faster-transformers
replied to
their
post
5 months ago
hf.co/blog/faster-transformers
posted
an
update
5 months ago
Post
1632
New post is live!
This time we cover some major updates to
🤗
This time we cover some major updates to
transformers.🤗
-
- 2 replies
posted
an
update
7 months ago
Post
895
I have always advocated for writing techinical stories without using LLMs.
The following one page editorial really drives the point home.
https://www.nature.com/articles/s44222-025-00323-4
The following one page editorial really drives the point home.
https://www.nature.com/articles/s44222-025-00323-4
reacted to
jsulz's
post with 🔥🤯🚀
7 months ago
Post
3303
We've moved over 20PB from Git LFS to Xet on the Hub without downtime or data loss. Having things "just work" on a migration of this scale is about as good as it gets.
Now, we're migrating the rest of the Hub https://huggingface.co/blog/migrating-the-hub-to-xet
But how did we get here?
In the early days of joining Hugging Face, we made a few key design decisions:
* There would be no "hard cut-over" from Git LFS to Xet
* A Xet-enabled repository should be able to contain both Xet and LFS files
* Repository migrations from LFS to Xet can run in the background without disrupting downloads or uploads
These were largely driven by our desire to ensure the community could keep working without interruption.
We cover the infrastructure making this all go in this post, specifically:
* An integral piece of infrastructure known internally as the Git LFS Bridge
* Background content migrations that run around the clock
To skip the wait and join Xet now, sign up here https://huggingface.co/join/xet
Now, we're migrating the rest of the Hub https://huggingface.co/blog/migrating-the-hub-to-xet
But how did we get here?
In the early days of joining Hugging Face, we made a few key design decisions:
* There would be no "hard cut-over" from Git LFS to Xet
* A Xet-enabled repository should be able to contain both Xet and LFS files
* Repository migrations from LFS to Xet can run in the background without disrupting downloads or uploads
These were largely driven by our desire to ensure the community could keep working without interruption.
We cover the infrastructure making this all go in this post, specifically:
* An integral piece of infrastructure known internally as the Git LFS Bridge
* Background content migrations that run around the clock
To skip the wait and join Xet now, sign up here https://huggingface.co/join/xet