Hugging Face Changelog
Faster Tensor Viewer
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Maani
AI & ML interests
Novel Architectures | System Design
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upvoted a changelog 6 days ago
Faster Tensor Viewer liked a model 8 days ago
unsloth/embeddinggemma-2-GGUF liked a Space 9 days ago
Intel/low_bit_open_llm_leaderboardOrganizations
upvoted a changelog 6 days ago
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model 8 days ago
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Space 9 days ago
reacted to tardellirs's post with 🔥 12 days ago
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3,927
Robotics is now the second most downloaded dataset category on the Hub, after text generation.
Robotics datasets got 13.7M downloads in September, ahead of text classification and question answering. Two years ago the category ranked 23rd. One in 7 new datasets is now robotics, mostly LeRobot recordings: typically a few dozen demos, about half of them on low-cost SO-100/SO-101 arms.
I found this after adding datasets and Spaces to Model Pulse, which rebuilds daily history from @cfahlgren1 's hub-stats snapshots. Two more findings:
- In 2022, 36% of authors who list training data cited classic NLP sets like IMDb, SQuAD and GLUE. In 2026 it's 2.4%. Reasoning traces distilled from models like DeepSeek-R1 and Claude are now the most cited kind.
- In October 2025, 122K Spaces were created, 71K of them websites built with DeepSite. That's about 6x the monthly pace of late 2024, while likes given per month fell from about 35K to about 20K.
New in the app: a page for every dataset, with daily downloads and the models trained on it (636 list FineWeb), a page for every Space, and rankings for both.
Thank you to everyone who liked Model Pulse this week: it made Spaces of the Week and is #7 on trending. Thanks also to @dipankarsarkar , whose comments on the last post fixed three data issues. If a number looks wrong, tell me.
Spaces: tardellirs/model-pulse
Robotics datasets got 13.7M downloads in September, ahead of text classification and question answering. Two years ago the category ranked 23rd. One in 7 new datasets is now robotics, mostly LeRobot recordings: typically a few dozen demos, about half of them on low-cost SO-100/SO-101 arms.
I found this after adding datasets and Spaces to Model Pulse, which rebuilds daily history from @cfahlgren1 's hub-stats snapshots. Two more findings:
- In 2022, 36% of authors who list training data cited classic NLP sets like IMDb, SQuAD and GLUE. In 2026 it's 2.4%. Reasoning traces distilled from models like DeepSeek-R1 and Claude are now the most cited kind.
- In October 2025, 122K Spaces were created, 71K of them websites built with DeepSite. That's about 6x the monthly pace of late 2024, while likes given per month fell from about 35K to about 20K.
New in the app: a page for every dataset, with daily downloads and the models trained on it (636 list FineWeb), a page for every Space, and rankings for both.
Thank you to everyone who liked Model Pulse this week: it made Spaces of the Week and is #7 on trending. Thanks also to @dipankarsarkar , whose comments on the last post fixed three data issues. If a number looks wrong, tell me.
Spaces: tardellirs/model-pulse
- 40 replies
upvoted a changelog 14 days ago
Hugging Face Changelog
P(doom) on user profiles
• 360
bf16/safe tensor
❤️👍 1
2
#4 opened 24 days ago
by Maani
liked a
model 19 days ago
liked a
model 20 days ago
Sample generations and test outputs
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4
#14 opened 28 days ago
by 
abenzerps
liked 2
models 24 days ago
reacted to Banaxi-Tech's post with 🚀 24 days ago
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4,877
We're excited to release BananaAll, our SLM Super App.
It allows you to do EVERYTHING you need to do to trains SLMs in a single app, no terminal, no 30 chrome tabs.
The train tab allows you to train models, select datasets from presets, and use other ones with auto mapping, model size slider, it automatically generates a training script for you.
Then after you've trained the model or want to compare it to competitors, the evaluation tab, run ARC EASY, ARC Challenge, Hellaswag, PIQA, Arithmark 3, BananaMind Base Bench and more! Simple Results screen.
And lastly the inference tab, run your trained models or others.
Normally you would need seperate apps or scripts for that, but the BananaAll Super App lets you do all of that in a single app.
We also trained a small 2.5M parameter model on 200M tokens of Fineweb edu, The results: BananaMind Base Bench 854 and 53% on PIQA. On only 200M tokens.
Check it out at https://github.com/BananaMind/BananaAll.
It allows you to do EVERYTHING you need to do to trains SLMs in a single app, no terminal, no 30 chrome tabs.
The train tab allows you to train models, select datasets from presets, and use other ones with auto mapping, model size slider, it automatically generates a training script for you.
Then after you've trained the model or want to compare it to competitors, the evaluation tab, run ARC EASY, ARC Challenge, Hellaswag, PIQA, Arithmark 3, BananaMind Base Bench and more! Simple Results screen.
And lastly the inference tab, run your trained models or others.
Normally you would need seperate apps or scripts for that, but the BananaAll Super App lets you do all of that in a single app.
We also trained a small 2.5M parameter model on 200M tokens of Fineweb edu, The results: BananaMind Base Bench 854 and 53% on PIQA. On only 200M tokens.
Check it out at https://github.com/BananaMind/BananaAll.
- 25 replies
replied to Banaxi-Tech's post 24 days ago
++ on it being dark mode by design!
reacted to danielhanchen's post with 🚀🔥 24 days ago
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model 25 days ago
reacted to SeaWolf-AI's post with 👀 25 days ago
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3,811
🖼️ NO GPU, Only CPU : Z-Image model
Zero graphics cards. 46 seconds. Photoreal.
That laptop you're reading this on. No graphics card, right? It generates images.
No CUDA install. No Python environment. No driver changes. One binary, three model files. Done.
📊 Measured — GPU count used: zero
512×512 : 46.4 s
Korean prompt : 45.3 s (faster than English)
1024×1024 : 192.7 s
Peak RAM : 6.42 GB
GPUs used : 0
(Intel Xeon Gold 6526Y ×2, 48 threads, Q4_0, 3 steps)
⚡ From 244 seconds to 46 — 5.3×
Run it on defaults and it takes 244 s. Switch to 3 steps and it's 48.6 s. Add VAE tiling and it's 46.4 s.
The biggest culprit was the default. Z-Image Turbo is distilled to paint in few strokes, but the tool's default is 20. We were throwing away 5× for no reason. So were we, at first.
3 is the floor. Put 4 and 3 side by side and you cannot tell them apart. At 2 it collapses — water droplets and wood grain vanish, and the surface turns cloth-like.
🔗 Links
Model
FINAL-Bench/POCKET-Zimage-CPU
Live demo Space (runs on CPU)
FINAL-Bench/POCKET-Zimage-CPU
POCKET collection
FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6
Zero graphics cards. 46 seconds. Photoreal.
That laptop you're reading this on. No graphics card, right? It generates images.
No CUDA install. No Python environment. No driver changes. One binary, three model files. Done.
📊 Measured — GPU count used: zero
512×512 : 46.4 s
Korean prompt : 45.3 s (faster than English)
1024×1024 : 192.7 s
Peak RAM : 6.42 GB
GPUs used : 0
(Intel Xeon Gold 6526Y ×2, 48 threads, Q4_0, 3 steps)
⚡ From 244 seconds to 46 — 5.3×
Run it on defaults and it takes 244 s. Switch to 3 steps and it's 48.6 s. Add VAE tiling and it's 46.4 s.
The biggest culprit was the default. Z-Image Turbo is distilled to paint in few strokes, but the tool's default is 20. We were throwing away 5× for no reason. So were we, at first.
3 is the floor. Put 4 and 3 side by side and you cannot tell them apart. At 2 it collapses — water droplets and wood grain vanish, and the surface turns cloth-like.
🔗 Links
Model
FINAL-Bench/POCKET-Zimage-CPU
Live demo Space (runs on CPU)
FINAL-Bench/POCKET-Zimage-CPU
POCKET collection
FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6
- 2 replies
upvoted an article 25 days ago
Article
AI Agents List [2026] | Frameworks, Agentic Harnesses & Useful Repos
tegridydev
• • 6 reacted to tegridydev's post with 🔥 25 days ago
Post
1,660
Apparently quite a few of you found my 2025 agents list useful, so here we go again :D
AI Agents List [2026] | Frameworks, Agentic Harnesses & Useful Repos
https://huggingface.co/blog/tegridydev/ai-agents-list-2026-frameworks-agentic-harnesses
The original was basically my own notepad that I decided to post before losing another version somewhere on my desktop lol. This one keeps that idea, with a much bigger list and a bit more organisation.
There are frameworks for building your own agents, coding tools you can actually open and use, research agents, browser automation, voice agents, and a dedicated section for agentic harnesses. Also included the useful boring stuff: testing, tracing, permissions and checking whether a project is still maintained.
This is a research directory, not a benchmark or a claim that every project has been personally tested. The main entries have public documentation and recent repository or release activity. Smaller projects and slower moving tools are labelled separately. I do my best to actually read through community feedback and real world user write ups when I research and create these lists / knowledge bases etc but as always, do your own research and go in with an open mind when you test out stuff :D
....that's what keeps it fun (for me anyway haha!)
Love always, it's a crazy world atm and stuff is moving so quickly, be kind to eachother and share knowledge and we must just get through this all good <3
~tegridydev
AI Agents List [2026] | Frameworks, Agentic Harnesses & Useful Repos
https://huggingface.co/blog/tegridydev/ai-agents-list-2026-frameworks-agentic-harnesses
The original was basically my own notepad that I decided to post before losing another version somewhere on my desktop lol. This one keeps that idea, with a much bigger list and a bit more organisation.
There are frameworks for building your own agents, coding tools you can actually open and use, research agents, browser automation, voice agents, and a dedicated section for agentic harnesses. Also included the useful boring stuff: testing, tracing, permissions and checking whether a project is still maintained.
This is a research directory, not a benchmark or a claim that every project has been personally tested. The main entries have public documentation and recent repository or release activity. Smaller projects and slower moving tools are labelled separately. I do my best to actually read through community feedback and real world user write ups when I research and create these lists / knowledge bases etc but as always, do your own research and go in with an open mind when you test out stuff :D
....that's what keeps it fun (for me anyway haha!)
Love always, it's a crazy world atm and stuff is moving so quickly, be kind to eachother and share knowledge and we must just get through this all good <3
~tegridydev
liked a
model 28 days ago
