Yes! As @victor mentioned https://huggingface.co/mcp?login should help you setup without the need of passing your token explicitly.
So as long as the client support OAuth, it should work auto-magically!
Let us know if you face any issues
Vaibhav Srivastav PRO
reach-vb
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2 days ago
Duplicate Datasets
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6 days ago
Transformers v5: Simple model definitions powering the AI ecosystem
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collection
8 days ago
Quantization Spaces on the Hub ⚡
Organizations
replied to
takarajordan's
post
3 months ago
posted
an
update
6 months ago
Post
5966
Excited to onboard FeatherlessAI on Hugging Face as an Inference Provider - they bring a fleet of 6,700+ LLMs on-demand on the Hugging Face Hub 🤯
Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! 💥
Go, play with it today: https://huggingface.co/blog/inference-providers-featherless
P.S. They're also bringing on more GPUs to support all your concurrent requests!
Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! 💥
Go, play with it today: https://huggingface.co/blog/inference-providers-featherless
P.S. They're also bringing on more GPUs to support all your concurrent requests!
- 1 reply
reacted to
jsulz's
post with 🔥
7 months ago
Post
2630
Heyo
@RichardErkhov
the
xet-team
at Hugging face was wondering if you wanted to join the fun and jump over to Xet storage. 🤗
We've been onboarding folks https://huggingface.co/blog/xet-on-the-hub know the backend can scale (Llama 4 and Qwen 3 are on Xet), is great for working with quants (see xet-team/quantization-dedup ), and we're pushing on inviting impactful orgs and users on the Hub. You fit the bill.
We'd love to onboard you, get some feedback, and create some excitement 🎉
The steps are pretty straightforward - join the waitlist at hf.co/join/xet and we'll take care of the rest.
The system is fully backward compatible, so you shouldn't notice a thing. BUT to get the best experience when uploading/downloading, make sure you have
What do you think?
xet-team
at Hugging face was wondering if you wanted to join the fun and jump over to Xet storage. 🤗We've been onboarding folks https://huggingface.co/blog/xet-on-the-hub know the backend can scale (Llama 4 and Qwen 3 are on Xet), is great for working with quants (see xet-team/quantization-dedup ), and we're pushing on inviting impactful orgs and users on the Hub. You fit the bill.
We'd love to onboard you, get some feedback, and create some excitement 🎉
The steps are pretty straightforward - join the waitlist at hf.co/join/xet and we'll take care of the rest.
The system is fully backward compatible, so you shouldn't notice a thing. BUT to get the best experience when uploading/downloading, make sure you have
hf_xet installed alongside the latest huggingface_hub What do you think?
- 4 replies
replied to
their
post
7 months ago
we're still optimising the > 50GB path, so at least right now, I'd recommend keeping <50 GB shards but this might change soon and then we can work out a plan
replied to
their
post
7 months ago
perfect! can you try and join the waitlist via hf.co/join/xet please!
posted
an
update
7 months ago
Post
4642
hey hey
@mradermacher
- VB from Hugging Face here, we'd love to onboard you over to our optimised xet backend! 💥
as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!
in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.
p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage
p.p.s. this is fully backwards compatible so everything will work as it should! 🤗
as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!
in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.
p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage
p.p.s. this is fully backwards compatible so everything will work as it should! 🤗
-
-
- 16 replies
reacted to
fdaudens's
post with 👍
7 months ago
Post
1269
The rapid progress in small audio models is mind-blowing! 🤯 Just tested OuteTTS v0.2 - cloned my voice from a 10s clip with impressive accuracy and natural prosody.
At 500M parameters, it's efficient enough to run on basic hardware but powerful enough for professional use.
This could transform how we produce audio content for new - think instant translated interviews keeping original voices, or scaled audio article production!
Demo and Model on the Hub: OuteAI/OuteTTS-0.2-500M h/t @reach-vb
At 500M parameters, it's efficient enough to run on basic hardware but powerful enough for professional use.
This could transform how we produce audio content for new - think instant translated interviews keeping original voices, or scaled audio article production!
Demo and Model on the Hub: OuteAI/OuteTTS-0.2-500M h/t @reach-vb
- 3 replies
reacted to
clem's
post with ❤️👀
8 months ago
Post
2309
Very interesting security section by
@yjernite
@lvwerra
@reach-vb
@dvilasuero
& the team replicating R1. Broadly applicable to most open-source models & some to APIs (but APIs have a lot more additional risks because you're not in control of the underlying system):
https://huggingface.co/blog/open-r1/update-4#is-it-safe
https://huggingface.co/blog/open-r1/update-4#is-it-safe
- 1 reply
reacted to
lbourdois's
post with 🔥❤️
9 months ago
Post
3532
We introduce FAT5 (Flash Attention T5) ⚡
An implementation of T5 in PyTorch with UL2 objective optimized for GPGPU for both training and inference thanks to 13 different optimizations.
The main one is that we have designed a CUDA kernel to expand the Flash Attention by @tridao with RPE biases and supports other PE such as RoPE, ALiBi or FIRE.
The result kernel is 2 times faster than a SPDA implementation.
We also use Triton kernels to optimize certain parts of the architecture, such as the cross-entropy and RMSNorm layer.
The various kernels have been carefully built to be compatible with BF16 and torch.compile to go even faster and achieve efficient pretraining.
All other optimizations are described in a 📝 subsequent blog post available on @huggingface 🤗: CATIE-AQ/FAT5-report.
This methodology enabled us to efficiently pretrain as a proof of concept a FAT5 with 147M parameters in French in a reasonable time (1,461H for 419B tokens), with limited resources (1 A100 i.e. a computational budget of ~ €1,900) and a low carbon footprint (13.5kg eq CO2).
The model's weights are also available on Hugging Face: CATIE-AQ/FAT5-small.
Not very useful in practice, it's a PoC and not an instructed model (it's planned for later).
All the code is available on GitHub if you want to pretrain your own model in your own language or for a specific domain: https://github.com/catie-aq/flashT5 ⭐
Ending by indicating that was a joint project with @BorisAlbar at hf.co/CATIE-AQ.
An implementation of T5 in PyTorch with UL2 objective optimized for GPGPU for both training and inference thanks to 13 different optimizations.
The main one is that we have designed a CUDA kernel to expand the Flash Attention by @tridao with RPE biases and supports other PE such as RoPE, ALiBi or FIRE.
The result kernel is 2 times faster than a SPDA implementation.
We also use Triton kernels to optimize certain parts of the architecture, such as the cross-entropy and RMSNorm layer.
The various kernels have been carefully built to be compatible with BF16 and torch.compile to go even faster and achieve efficient pretraining.
All other optimizations are described in a 📝 subsequent blog post available on @huggingface 🤗: CATIE-AQ/FAT5-report.
This methodology enabled us to efficiently pretrain as a proof of concept a FAT5 with 147M parameters in French in a reasonable time (1,461H for 419B tokens), with limited resources (1 A100 i.e. a computational budget of ~ €1,900) and a low carbon footprint (13.5kg eq CO2).
The model's weights are also available on Hugging Face: CATIE-AQ/FAT5-small.
Not very useful in practice, it's a PoC and not an instructed model (it's planned for later).
All the code is available on GitHub if you want to pretrain your own model in your own language or for a specific domain: https://github.com/catie-aq/flashT5 ⭐
Ending by indicating that was a joint project with @BorisAlbar at hf.co/CATIE-AQ.
reacted to
julien-c's
post with 🚀🔥
9 months ago
Post
4289
Important notice 🚨
For Inference Providers who have built support for our Billing API (currently: Fal, Novita, HF-Inference – with more coming soon), we've started enabling Pay as you go (=PAYG)
What this means is that you can use those Inference Providers beyond the free included credits, and they're charged to your HF account.
You can see it on this view: any provider that does not have a "Billing disabled" badge, is PAYG-compatible.
For Inference Providers who have built support for our Billing API (currently: Fal, Novita, HF-Inference – with more coming soon), we've started enabling Pay as you go (=PAYG)
What this means is that you can use those Inference Providers beyond the free included credits, and they're charged to your HF account.
You can see it on this view: any provider that does not have a "Billing disabled" badge, is PAYG-compatible.
- 9 replies
reacted to
PranjaliJoshi's
post with ❤️👀
9 months ago
Post
734
🌍 Have you tried Cosmos world foundation models on Hugging Face? Because more updates are coming! 🚀
Cosmos world foundation models (WFMs) are generative pretrained models for synthetic data generation for training AI models for robot or autonomous vehicle development.
🛠️ If you are building generative VLMs or foundation models for physical AI like policy models- there are new updates coming at NVIDIA GTC.
GTC is NVIDIA’s biggest annual event (March 17-21) - it will have deep dives, training labs, and researcher-led sessions on Cosmos.
Plus, Jensen Huang’s keynote! 🎤
🎟️ 20% off GTC registration → Use code HUGGINGFACE20
🔗 https://www.nvidia.com/gtc/
📍 Happening in person at the San Jose Convention Center and online.
Explore all Cosmos sessions at GTC: https://nvda.ws/41yBkmY
Try the existing Cosmos WFMs:
🔗 Hugging Face models: nvidia/cosmos-6751e884dc10e013a0a0d8e6
🛠️ Post-training scripts: https://github.com/NVIDIA/Cosmos/blob/main/cosmos1/models/POST_TRAINING.md
Cosmos world foundation models (WFMs) are generative pretrained models for synthetic data generation for training AI models for robot or autonomous vehicle development.
🛠️ If you are building generative VLMs or foundation models for physical AI like policy models- there are new updates coming at NVIDIA GTC.
GTC is NVIDIA’s biggest annual event (March 17-21) - it will have deep dives, training labs, and researcher-led sessions on Cosmos.
Plus, Jensen Huang’s keynote! 🎤
🎟️ 20% off GTC registration → Use code HUGGINGFACE20
🔗 https://www.nvidia.com/gtc/
📍 Happening in person at the San Jose Convention Center and online.
Explore all Cosmos sessions at GTC: https://nvda.ws/41yBkmY
Try the existing Cosmos WFMs:
🔗 Hugging Face models: nvidia/cosmos-6751e884dc10e013a0a0d8e6
🛠️ Post-training scripts: https://github.com/NVIDIA/Cosmos/blob/main/cosmos1/models/POST_TRAINING.md
- 1 reply
reacted to
AdinaY's
post with 🚀🔥😎
9 months ago
Post
4097
Exciting releases from the Chinese community this February🔥
👉 https://huggingface.co/collections/zh-ai-community/2025-february-67a35aaa68e97812def5b6ef
MLLM:
✨ Ovis2 by Alibaba
AIDC-AI/ovis2-67ab36c7e497429034874464
✨ Step Audio Chat by StepFun AI
stepfun-ai/step-audio-67b33accf45735bb21131b0b
Audio:
✨ Step Audio TTS by StepFunAI
stepfun-ai/Step-Audio-TTS-3B
✨ InspireMusic by Alibaba
FunAudioLLM
✨ Baichuan Audio by BaichuanAI
baichuan-inc/Baichuan-Audio-Instruct
Video:
✨ Wan2.1 by Alibaba_Wan
Wan-AI/Wan2.1-T2V-14B
✨ Stepvideo-T2V by StepFun AI
stepfun-ai/stepvideo-t2v
✨ SkyReels-V1 by Skywork
Skywork/skyreels-v1-67b34676ff65b4ec02d16307
✨ LLaDA-8B by RenminUniversity
GSAI-ML/LLaDA-8B-Instruct
MoE:
✨ Moonlight-16B by MoonshotAI (Kimi)
moonshotai/Moonlight-16B-A3B-Instruct
Reasoning:
✨ TinyR1-32B by Qihoo360
qihoo360/TinyR1-32B-Preview
Dataset:
✨ Chinese DeepSeek R1-Distill data -110k
Congliu/Chinese-DeepSeek-R1-Distill-data-110k
👉 https://huggingface.co/collections/zh-ai-community/2025-february-67a35aaa68e97812def5b6ef
MLLM:
✨ Ovis2 by Alibaba
AIDC-AI/ovis2-67ab36c7e497429034874464
✨ Step Audio Chat by StepFun AI
stepfun-ai/step-audio-67b33accf45735bb21131b0b
Audio:
✨ Step Audio TTS by StepFunAI
stepfun-ai/Step-Audio-TTS-3B
✨ InspireMusic by Alibaba
FunAudioLLM
✨ Baichuan Audio by BaichuanAI
baichuan-inc/Baichuan-Audio-Instruct
Video:
✨ Wan2.1 by Alibaba_Wan
Wan-AI/Wan2.1-T2V-14B
✨ Stepvideo-T2V by StepFun AI
stepfun-ai/stepvideo-t2v
✨ SkyReels-V1 by Skywork
Skywork/skyreels-v1-67b34676ff65b4ec02d16307
✨ LLaDA-8B by RenminUniversity
GSAI-ML/LLaDA-8B-Instruct
MoE:
✨ Moonlight-16B by MoonshotAI (Kimi)
moonshotai/Moonlight-16B-A3B-Instruct
Reasoning:
✨ TinyR1-32B by Qihoo360
qihoo360/TinyR1-32B-Preview
Dataset:
✨ Chinese DeepSeek R1-Distill data -110k
Congliu/Chinese-DeepSeek-R1-Distill-data-110k
let's gooo!
reacted to
lysandre's
post with 🚀
10 months ago
Post
8204
SmolVLM-2 and SigLIP-2 are now part of
They're added on top of the v4.49.0 release, and can be installed from the following tags:
This marks a new beginning for the release process of transformers. For the past five years, we've been doing monthly releases featuring many models (v4.49.0, the latest release, features 9 new architectures).
Starting with SmolVLM-2 & SigLIP2, we'll now additionally release tags supporting new models on a stable branch. These models are therefore directly available for use by installing from the tag itself. These tags will continue to be updated with fixes applied to these models.
Going forward, continue expecting software releases following semantic versioning: v4.50.0 will have ~10 new architectures compared to v4.49.0, as well as a myriad of new features, improvements and bug fixes. Accompanying these software releases, we'll release tags offering brand new models as fast as possible, to make them accessible to all immediately.
transformers in dedicated releases!They're added on top of the v4.49.0 release, and can be installed from the following tags:
v4.49.0-SmolVLM-2 and v4.49.0-SigLIP-2.This marks a new beginning for the release process of transformers. For the past five years, we've been doing monthly releases featuring many models (v4.49.0, the latest release, features 9 new architectures).
Starting with SmolVLM-2 & SigLIP2, we'll now additionally release tags supporting new models on a stable branch. These models are therefore directly available for use by installing from the tag itself. These tags will continue to be updated with fixes applied to these models.
Going forward, continue expecting software releases following semantic versioning: v4.50.0 will have ~10 new architectures compared to v4.49.0, as well as a myriad of new features, improvements and bug fixes. Accompanying these software releases, we'll release tags offering brand new models as fast as possible, to make them accessible to all immediately.
-
- 1 reply