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liked
a Space
about 20 hours ago
Jimmyzheng-10/ScreenCoder
replied to
ZennyKenny's
post
about 20 hours ago
It's just a matter of time before all the data leakage and data scraping associated with building, training, and using AI results in some kind of major scandal.
That's why I think this paper by @spintronic is so important: https://huggingface.co/papers/2508.06647
Glad to know that there are already researchers looking to mitigate and address this risk before the s**t hits the fan.
liked
a Space
1 day ago
gradio/chat.gradio.app-HFIPs
Organizations
replied to
ZennyKenny's
post
about 20 hours ago
reacted to
fdaudens's
post with 👍
29 days ago
Post
2548
You might not have heard of Moonshot AI — but within 24 hours, their new model Kimi K2 shot to the top of Hugging Face’s trending leaderboard.
So… who are they, and why does it matter?
Had a lot of fun co-writing this blog post with @xianbao , with key insights translated from Chinese, to unpack how this startup built a model that outperforms GPT-4.1, Claude Opus, and DeepSeek V3 on several major benchmarks.
🧵 A few standout facts:
1. From zero to $3.3B in 18 months:
Founded in March 2023, Moonshot is now backed by Alibaba, Tencent, Meituan, and HongShan.
2. A CEO who thinks from the end:
Yang Zhilin (31) previously worked at Meta AI, Google Brain, and Carnegie Mellon. His vision? Nothing less than AGI — still a rare ambition among Chinese AI labs.
3. A trillion-parameter model that’s surprisingly efficient:
Kimi K2 uses a mixture-of-experts architecture (32B active params per inference) and dominates on coding/math benchmarks.
4. The secret weapon: Muon optimizer:
A new training method that doubles efficiency, cuts memory in half, and ran 15.5T tokens with zero failures. Big implications.
Most importantly, their move from closed to open source signals a broader shift in China’s AI scene — following Baidu’s pivot. But as Yang puts it: “Users are the only real leaderboard.”
👇 Check out the full post to explore what Kimi K2 can do, how to try it, and why it matters for the future of open-source LLMs:
https://huggingface.co/blog/fdaudens/moonshot-ai-kimi-k2-explained
So… who are they, and why does it matter?
Had a lot of fun co-writing this blog post with @xianbao , with key insights translated from Chinese, to unpack how this startup built a model that outperforms GPT-4.1, Claude Opus, and DeepSeek V3 on several major benchmarks.
🧵 A few standout facts:
1. From zero to $3.3B in 18 months:
Founded in March 2023, Moonshot is now backed by Alibaba, Tencent, Meituan, and HongShan.
2. A CEO who thinks from the end:
Yang Zhilin (31) previously worked at Meta AI, Google Brain, and Carnegie Mellon. His vision? Nothing less than AGI — still a rare ambition among Chinese AI labs.
3. A trillion-parameter model that’s surprisingly efficient:
Kimi K2 uses a mixture-of-experts architecture (32B active params per inference) and dominates on coding/math benchmarks.
4. The secret weapon: Muon optimizer:
A new training method that doubles efficiency, cuts memory in half, and ran 15.5T tokens with zero failures. Big implications.
Most importantly, their move from closed to open source signals a broader shift in China’s AI scene — following Baidu’s pivot. But as Yang puts it: “Users are the only real leaderboard.”
👇 Check out the full post to explore what Kimi K2 can do, how to try it, and why it matters for the future of open-source LLMs:
https://huggingface.co/blog/fdaudens/moonshot-ai-kimi-k2-explained
reacted to
asigalov61's
post with 🔥
about 1 month ago
Post
2358
Check out new symbolic music AI front end and CLI training app
https://webchatappai.github.io/midi-gen/
https://github.com/WebChatAppAi/Orpheus-Midi-Model-Maker
@Timzoid @Csplk @not-lain @victor @bartowski @John6666
https://webchatappai.github.io/midi-gen/
https://github.com/WebChatAppAi/Orpheus-Midi-Model-Maker
@Timzoid @Csplk @not-lain @victor @bartowski @John6666
- 2 replies
reacted to
sergiopaniego's
post with 🚀
about 1 month ago
Post
2003
Updated my HF Space for vibe testing smol VLMs on object detection, visual grounding, keypoint detection & counting! 👓
🆕 Compare Qwen2.5 VL 3B vs Moondream 2B side-by-side with annotated images & text outputs.
Try examples or test your own images! 🏃
📱Space: sergiopaniego/vlm_object_understanding
🆕 Compare Qwen2.5 VL 3B vs Moondream 2B side-by-side with annotated images & text outputs.
Try examples or test your own images! 🏃
📱Space: sergiopaniego/vlm_object_understanding
reacted to
AdinaY's
post with 🚀
about 1 month ago
Post
1985
The Chinese Open Source Heatmap is live 🔥
You can now track the companies/ research labs/ communities powering China’s open source AI movement.
zh-ai-community/model-release-heatmap-zh
Some highlights:
✨Giant Tech are investing more in open source.
-Alibaba: Full stack open ecosystem
-Tecent: Hunyuan image/video/3D
-Bytedance: Catching up fast in 2025
-Baidu: New player in open LLM
✨New players emerging post–DeepSeek moment.
-Xiaomi
-Red Note
-Bilibili
-MiniMax
-Moonshot AI
✨Startup list is shifting fast! Those who find a direction aligned with their strengths are the ones who endure.
-DeepSeek
-MiniMax
-StepFun
-Moonshot AI
-Zhipu AI
-OpenBMB
✨Research Lab & Community are making key contributions.
-BAAI
-Shanghai AI Lab
-OpenMOSS
-MAP
You can now track the companies/ research labs/ communities powering China’s open source AI movement.
zh-ai-community/model-release-heatmap-zh
Some highlights:
✨Giant Tech are investing more in open source.
-Alibaba: Full stack open ecosystem
-Tecent: Hunyuan image/video/3D
-Bytedance: Catching up fast in 2025
-Baidu: New player in open LLM
✨New players emerging post–DeepSeek moment.
-Xiaomi
-Red Note
-Bilibili
-MiniMax
-Moonshot AI
✨Startup list is shifting fast! Those who find a direction aligned with their strengths are the ones who endure.
-DeepSeek
-MiniMax
-StepFun
-Moonshot AI
-Zhipu AI
-OpenBMB
✨Research Lab & Community are making key contributions.
-BAAI
-Shanghai AI Lab
-OpenMOSS
-MAP
I think it's nice that Zero GPU is something that provides so much to so many people across the hugging face community to build and use things that would otherwise be unavailable / unfair barriers of entry to do so at no cost to the average user.
zeroGPU has max execution time parameters with sensible defaults and ranges so it should not take to much quota even if left unchecked. Don't forget you will get more quota in no time at all.
reacted to
merve's
post with 🚀
about 2 months ago
Post
2332
y'all have been asking my opinion on how OCR models compare to each other 👀
I will leave three apps to compare newest models by @prithivMLmods instead ⤵️
> compare Nanonets-OCR-s, Qwen2-VL-OCR-2B-Instruct, RolmOCR, Aya-Vision prithivMLmods/Multimodal-OCR
> SmolDocling, Nanonets-OCR-s, MonkeyOCR, Typhoon-OCR-7B prithivMLmods/Multimodal-OCR2
> docscopeOCR, MonkeyOCR, coreOCR prithivMLmods/core-OCR
I will leave three apps to compare newest models by @prithivMLmods instead ⤵️
> compare Nanonets-OCR-s, Qwen2-VL-OCR-2B-Instruct, RolmOCR, Aya-Vision prithivMLmods/Multimodal-OCR
> SmolDocling, Nanonets-OCR-s, MonkeyOCR, Typhoon-OCR-7B prithivMLmods/Multimodal-OCR2
> docscopeOCR, MonkeyOCR, coreOCR prithivMLmods/core-OCR
- 1 reply
reacted to
cbensimon's
post with 🔥
2 months ago
Post
3412
🚀 ZeroGPU now supports PyTorch native quantization via
While it hasn’t been battle-tested yet,
Let us know if you run into any issues — and we’re excited to see what the community will build!
torchaoWhile it hasn’t been battle-tested yet,
Int8WeightOnlyConfig is already working flawlessly in our tests.Let us know if you run into any issues — and we’re excited to see what the community will build!
import spaces
from diffusers import FluxPipeline
from torchao.quantization.quant_api import Int8WeightOnlyConfig, quantize_
pipeline = FluxPipeline.from_pretrained(...).to('cuda')
quantize_(pipeline.transformer, Int8WeightOnlyConfig()) # Or any other component(s)
@spaces.GPU
def generate(prompt: str):
return pipeline(prompt).images[0]
- 5 replies
reacted to
clem's
post with 🤗
3 months ago
Post
3573
It's just become easier to share your apps on the biggest AI app store (aka HF spaces) for unlimited storage, more visibility and community interactions.
Just pick a React, Svelte, or Vue template when you create your space or add
Or follow this link: https://huggingface.co/new-space?sdk=static
Let's build!
Just pick a React, Svelte, or Vue template when you create your space or add
app_build_command: npm run build in your README's YAML and app_file: build/index.html in your README's YAML block.Or follow this link: https://huggingface.co/new-space?sdk=static
Let's build!
- 1 reply
Time to podman my songs
reacted to
Yehor's
post with 🔥🔥
4 months ago
Post
2115
Convert your audio data to Parquet/DuckDB files with blazingly fast speeds!
Repository with pre-built binaries: https://github.com/crs-org/audios-to-dataset
Repository with pre-built binaries: https://github.com/crs-org/audios-to-dataset
-
- 2 replies
reacted to
danielhanchen's
post with ❤️
4 months ago
Post
5119
You can now run Llama 4 on your own local device! 🦙
Run our Dynamic 1.78-bit and 2.71-bit Llama 4 GGUFs:
unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
You can run them on llama.cpp and other inference engines. See our guide here: https://docs.unsloth.ai/basics/tutorial-how-to-run-and-fine-tune-llama-4
Run our Dynamic 1.78-bit and 2.71-bit Llama 4 GGUFs:
unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
You can run them on llama.cpp and other inference engines. See our guide here: https://docs.unsloth.ai/basics/tutorial-how-to-run-and-fine-tune-llama-4
- 1 reply
reacted to
MikeDoes's
post with 😎
5 months ago
Post
2722
🚀 Ai4Privacy Team is excited to unveil PII-Masking-1M, our most significant release yet! 🎉
This publication series 📦 includes datasets 📊, models 🤖, and applications ⚙️ to advance PII masking with AI systems 🛡️
Starting on Monday with daily posts at 7 PM CET ⏰
This publication series 📦 includes datasets 📊, models 🤖, and applications ⚙️ to advance PII masking with AI systems 🛡️
Starting on Monday with daily posts at 7 PM CET ⏰
reacted to
thomwolf's
post with ❤️
5 months ago
Post
3064
We've kept pushing our Open-R1 project, an open initiative to replicate and extend the techniques behind DeepSeek-R1.
And even we were mind-blown by the results we got with this latest model we're releasing: ⚡️OlympicCoder ( open-r1/OlympicCoder-7B and open-r1/OlympicCoder-32B)
It's beating Claude 3.7 on (competitive) programming –a domain Anthropic has been historically really strong at– and it's getting close to o1-mini/R1 on olympiad level coding with just 7B parameters!
And the best part is that we're open-sourcing all about its training dataset, the new IOI benchmark, and more in our Open-R1 progress report #3: https://huggingface.co/blog/open-r1/update-3
Datasets are are releasing:
- open-r1/codeforces
- open-r1/codeforces-cots
- open-r1/ioi
- open-r1/ioi-test-cases
- open-r1/ioi-sample-solutions
- open-r1/ioi-cots
- open-r1/ioi-2024-model-solutions
And even we were mind-blown by the results we got with this latest model we're releasing: ⚡️OlympicCoder ( open-r1/OlympicCoder-7B and open-r1/OlympicCoder-32B)
It's beating Claude 3.7 on (competitive) programming –a domain Anthropic has been historically really strong at– and it's getting close to o1-mini/R1 on olympiad level coding with just 7B parameters!
And the best part is that we're open-sourcing all about its training dataset, the new IOI benchmark, and more in our Open-R1 progress report #3: https://huggingface.co/blog/open-r1/update-3
Datasets are are releasing:
- open-r1/codeforces
- open-r1/codeforces-cots
- open-r1/ioi
- open-r1/ioi-test-cases
- open-r1/ioi-sample-solutions
- open-r1/ioi-cots
- open-r1/ioi-2024-model-solutions
reacted to
prithivMLmods's
post with 🔥
6 months ago
Post
5911
Dropping some of the custom fine-tunes based on SigLIP2,
with a single/multi label classification problem type! 🌀🧤
- AI vs Deepfake vs Real : prithivMLmods/AI-vs-Deepfake-vs-Real-Siglip2
- Deepfake Detect : prithivMLmods/Deepfake-Detect-Siglip2
- Fire Detection : prithivMLmods/Fire-Detection-Siglip2
- Deepfake Quality Assess : prithivMLmods/Deepfake-Quality-Assess-Siglip2
- Guard Against Unsafe Content : prithivMLmods/Guard-Against-Unsafe-Content-Siglip2
🌠Collection : prithivMLmods/siglip2-custom-67bcdb2de8fe96b99fb4e19e
with a single/multi label classification problem type! 🌀🧤
- AI vs Deepfake vs Real : prithivMLmods/AI-vs-Deepfake-vs-Real-Siglip2
- Deepfake Detect : prithivMLmods/Deepfake-Detect-Siglip2
- Fire Detection : prithivMLmods/Fire-Detection-Siglip2
- Deepfake Quality Assess : prithivMLmods/Deepfake-Quality-Assess-Siglip2
- Guard Against Unsafe Content : prithivMLmods/Guard-Against-Unsafe-Content-Siglip2
🌠Collection : prithivMLmods/siglip2-custom-67bcdb2de8fe96b99fb4e19e
reacted to
davidberenstein1957's
post with ❤️
6 months ago
Post
3321
🚀 Find banger tools for your smolagents!
I created the Tools gallery, which makes tools specifically developed by/for smolagents searchable and visible. This will help with:
- inspiration
- best practices
- finding cool tools
Space: davidberenstein1957/smolagents-and-tools
I created the Tools gallery, which makes tools specifically developed by/for smolagents searchable and visible. This will help with:
- inspiration
- best practices
- finding cool tools
Space: davidberenstein1957/smolagents-and-tools
- 1 reply
reacted to
hlarcher's
post with 🔥
7 months ago
Post
1165
We are introducing multi-backend support in Hugging Face Text Generation Inference!
With new TGI architecture we are now able to plug new modeling backends to get best performances according to selected model and available hardware. This first step will very soon be followed by the integration of new backends (TRT-LLM, llama.cpp, vLLM, Neuron and TPU).
We are polishing the TensorRT-LLM backend which achieves impressive performances on NVIDIA GPUs, stay tuned 🤗 !
Check out the details: https://huggingface.co/blog/tgi-multi-backend
With new TGI architecture we are now able to plug new modeling backends to get best performances according to selected model and available hardware. This first step will very soon be followed by the integration of new backends (TRT-LLM, llama.cpp, vLLM, Neuron and TPU).
We are polishing the TensorRT-LLM backend which achieves impressive performances on NVIDIA GPUs, stay tuned 🤗 !
Check out the details: https://huggingface.co/blog/tgi-multi-backend
reacted to
CultriX's
post with ❤️
7 months ago
Post
2152
# Space for Multi-Agent Workflows using AutoGen
Hi all, I created this "AutoGen Multi-Agent Workflow" space that allows you to experiment with multi-agent workflows.
By default, it allows code generation with built-in quality control and automatic documentation generation. It achieves this by leveraging multiple AI agents working together to produce high-quality code snippets, ensuring they meet the specified requirements.
In addition to the default, the space allows users to set custom system messages for each assistant, potentially completely changing the workflow.
# Workflow Steps
1. User Input:
- The user defines a prompt, such as "Write a random password generator using python."
- Outcome: A clear task for the primary assistant to accomplish.
2. Primary Assistant Work:
- The primary assistant begins working on the provided prompt.
It generates an initial code snippet based on the user's request.
- Outcome: An initial proposal for the requested code.
3. Critic Feedback:
- The critic reviews the generated code provides feedback or (if the output meets the criteria), broadcasts the APPROVED message.
(This process repeats until the output is APPROVED or 10 messages have been exchanged).
- Outcome: A revised Python function that incorporates the critic's feedback.
4. Documentation Generation:
- Once the code is approved, it is passed to a documentation assistant.
The documentation assistant generates a concise documentation for the final code.
- Outcome: A short documentation including function description, parameters, and return values.
Enjoy!
CultriX/AutoGen-MultiAgent-Example
Hi all, I created this "AutoGen Multi-Agent Workflow" space that allows you to experiment with multi-agent workflows.
By default, it allows code generation with built-in quality control and automatic documentation generation. It achieves this by leveraging multiple AI agents working together to produce high-quality code snippets, ensuring they meet the specified requirements.
In addition to the default, the space allows users to set custom system messages for each assistant, potentially completely changing the workflow.
# Workflow Steps
1. User Input:
- The user defines a prompt, such as "Write a random password generator using python."
- Outcome: A clear task for the primary assistant to accomplish.
2. Primary Assistant Work:
- The primary assistant begins working on the provided prompt.
It generates an initial code snippet based on the user's request.
- Outcome: An initial proposal for the requested code.
3. Critic Feedback:
- The critic reviews the generated code provides feedback or (if the output meets the criteria), broadcasts the APPROVED message.
(This process repeats until the output is APPROVED or 10 messages have been exchanged).
- Outcome: A revised Python function that incorporates the critic's feedback.
4. Documentation Generation:
- Once the code is approved, it is passed to a documentation assistant.
The documentation assistant generates a concise documentation for the final code.
- Outcome: A short documentation including function description, parameters, and return values.
Enjoy!
CultriX/AutoGen-MultiAgent-Example
- 4 replies
replied to
singhsidhukuldeep's
post
7 months ago
Paper for those who wish to read it themselves https://arxiv.org/abs/2305.19860
Github from authors https://github.com/WLiK/LLM4Rec-Awesome-Papers