MM-Conv: A Multi-modal Conversational Dataset for Virtual Humans
Paper
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2410.00253
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Published
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jimregan
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Post
3375
🚀 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
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Open Source Avengers, Assemble! Ask an expert AI agent team to solve complex problems together 🔥
Consilium brings together multiple agents that debate and use live research (web, arXiv, SEC) to reach a consensus. You set the strategy, they find the answer.
Credit to @azettl for this awesome demo: Agents-MCP-Hackathon/consilium_mcp
Consilium brings together multiple agents that debate and use live research (web, arXiv, SEC) to reach a consensus. You set the strategy, they find the answer.
Credit to @azettl for this awesome demo: Agents-MCP-Hackathon/consilium_mcp
- 2 replies
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🚀 ZeroGPU
Nothing too fancy for now—ZeroGPU Spaces still default to
- 💰 size-based quotas / pricing (
- 🦣 the upcoming
You can as of now control GPU size via a Space variable. Accepted values:
-
-
-
The auto mode checks total CUDA tensor size during startup:
- More than 30GB →
- Otherwise →
medium size is now available as a power-user featureNothing too fancy for now—ZeroGPU Spaces still default to
large (70GB VRAM)—but this paves the way for:- 💰 size-based quotas / pricing (
medium will offer significantly more usage than large)- 🦣 the upcoming
xlarge size (141GB VRAM)You can as of now control GPU size via a Space variable. Accepted values:
-
auto (future default)-
medium-
large (current default)The auto mode checks total CUDA tensor size during startup:
- More than 30GB →
large- Otherwise →
medium
-
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Hey everyone, we've given https://hf.co/spaces page a fresh update!
Smart Search: Now just type what you want to do—like "make a viral meme" or "generate music"—and our search gets it.
New Categories: Check out the cool new filter bar with icons to help you pick a category fast.
Redesigned Space Cards: Reworked a bit to really show off the app descriptions, so you know what each Space does at a glance.
Random Prompt: Need ideas? Hit the dice button for a burst of inspiration.
We’d love to hear what you think—drop us some feedback plz!
Smart Search: Now just type what you want to do—like "make a viral meme" or "generate music"—and our search gets it.
New Categories: Check out the cool new filter bar with icons to help you pick a category fast.
Redesigned Space Cards: Reworked a bit to really show off the app descriptions, so you know what each Space does at a glance.
Random Prompt: Need ideas? Hit the dice button for a burst of inspiration.
We’d love to hear what you think—drop us some feedback plz!
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- 6 replies
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3375
Finally, an open-source AI that turns your lyrics into full songs is here—meet YuE! Unlike other tools that only create short clips, YuE can make entire songs (up to 5 minutes) with vocals, melody, and instruments all working together. Letsss go!
m-a-p/YuE-s1-7B-anneal-en-cot
m-a-p/YuE-s1-7B-anneal-en-cot
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I re-worked the JuptyerLab Space template recently. It's optimized for
timm use, but will work great with transformers and other libs. Updated the base image, Python 3.12, Pillow-SIMD before better CPU use with image preprocessing, and made a number of other tweaks. From the Jupyter launcher you can run the terminal and setup a timm environment in moments with setup_timm_dev or setup_timm_scripts helpers. Give it a try,
timm/jupyterlab-timm
Post
1209
New
* PaliGemma2 encoders (ported from google/paligemma-2-release-67500e1e1dbfdd4dee27ba48)
* AIMv-2 encoders (ported from apple/aimv2-6720fe1558d94c7805f7688c)
A few higher resolution 384x384 ConvNeXt-Nano ImageNet-12k pretrain & finetunes. See other changes here: https://github.com/huggingface/pytorch-image-models/releases/tag/v1.0.13
And support added in both
* DFN CLIP L/14 w/ 39B samples seen - apple/DFN2B-CLIP-ViT-L-14-39B, timm/vit_large_patch14_clip_224.dfn2b_s39b
* MetaCLIP H/14 (altogether) - timm/vit_huge_patch14_clip_224.metaclip_altogether
And last, ~70-80 models that were relying on
timm 1.0.13 and OpenCLIP 2.30.0 releases to start the year. Both modest but worthwhile updates.timm added a number of new model weights, supporting loading of: * PaliGemma2 encoders (ported from google/paligemma-2-release-67500e1e1dbfdd4dee27ba48)
* AIMv-2 encoders (ported from apple/aimv2-6720fe1558d94c7805f7688c)
A few higher resolution 384x384 ConvNeXt-Nano ImageNet-12k pretrain & finetunes. See other changes here: https://github.com/huggingface/pytorch-image-models/releases/tag/v1.0.13
And support added in both
OpenCLIP and timm for two CLIP models that were missed. The DFN L/14 is 🔥* DFN CLIP L/14 w/ 39B samples seen - apple/DFN2B-CLIP-ViT-L-14-39B, timm/vit_large_patch14_clip_224.dfn2b_s39b
* MetaCLIP H/14 (altogether) - timm/vit_huge_patch14_clip_224.metaclip_altogether
And last, ~70-80 models that were relying on
timm remapping from OpenCLIP got their own timm hub instances to allow use with the upcoming Transformers TimmWrapperModel
Post
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There's a new
New optimizers include:
* AdafactorBigVision -
* ADOPT -
* MARS -
* LaProp -
* Cautious Optimizers - a modification to all of the above, prefix with
I shared some caution comparisons in this model repo: rwightman/timm-optim-caution
For details, references, see the code: https://github.com/huggingface/pytorch-image-models/tree/main/timm/optim
timm release, v 1.0.12, with a focus on optimizers. The optimizer factory has been refactored, there's now a timm.optim.list_optimizers() and new way to register optimizers and their attributes. As always you can use an timm optimizer like a torch one, just replace torch.optim with timm.optimNew optimizers include:
* AdafactorBigVision -
adfactorbv* ADOPT -
adopt / adoptw (decoupled decay)* MARS -
mars* LaProp -
laprop* Cautious Optimizers - a modification to all of the above, prefix with
c as well as cadamw, cnadamw, csgdw, clamb, crmsproptf I shared some caution comparisons in this model repo: rwightman/timm-optim-caution
For details, references, see the code: https://github.com/huggingface/pytorch-image-models/tree/main/timm/optim
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Qwen/QwQ-32B-Preview shows us the future (and it's going to be exciting)...
I tested it against some really challenging reasoning prompts and the results are amazing 🤯.
Check this dataset for the results: https://huggingface.co/datasets/victor/qwq-misguided-attention
I tested it against some really challenging reasoning prompts and the results are amazing 🤯.
Check this dataset for the results: https://huggingface.co/datasets/victor/qwq-misguided-attention
- 2 replies
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Perfect example of why
Qwen/Qwen2.5-Coder-32B-Instruct is insane?
Introducing: AI Video Composer 🔥
huggingface-projects/ai-video-composer
Drag and drop your assets (images/videos/audios) to create any video you want using natural language!
It works by asking the model to output a valid FFMPEG and this can be quite complex but most of the time Qwen2.5-Coder-32B gets it right (that thing is a beast). It's an update of an old project made with GPT4 and it was almost impossible to make it work with open models back then (~1.5 years ago), but not anymore, let's go open weights 🚀.
Introducing: AI Video Composer 🔥
huggingface-projects/ai-video-composer
Drag and drop your assets (images/videos/audios) to create any video you want using natural language!
It works by asking the model to output a valid FFMPEG and this can be quite complex but most of the time Qwen2.5-Coder-32B gets it right (that thing is a beast). It's an update of an old project made with GPT4 and it was almost impossible to make it work with open models back then (~1.5 years ago), but not anymore, let's go open weights 🚀.
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I'm currently on a push to expand the scope of image based datasets on the Hub. There's certainly a lot already, but for anyone who's looked closely, there's not a whole lot of standardization. I am to fix that, datasets under the
timm
and
pixparse
orgs will serve as canonical examples for various task / modality combinations and be useable without fuss in libraries like
I just uploaded the first multi-label dataset that I'll support with
Next up object detection & segmentation! I've got an annotation spec sorted out, a lot of datasets ready to rip, and yeah that means
timm
and
pixparse
orgs will serve as canonical examples for various task / modality combinations and be useable without fuss in libraries like timm, OpenCLIP, and hopefully more.I just uploaded the first multi-label dataset that I'll support with
timm scripts soon:
timm/plant-pathology-2021 Next up object detection & segmentation! I've got an annotation spec sorted out, a lot of datasets ready to rip, and yeah that means
timm support for object detection, eventually segmentation, is finally under development :OPost
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Qwen2.5-72B is now the default HuggingChat model.
This model is so good that you must try it! I often get better results on rephrasing with it than Sonnet or GPT-4!!
This model is so good that you must try it! I often get better results on rephrasing with it than Sonnet or GPT-4!!
Post
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Want to validate some hparams or figure out what
I had this sitting on my drive and forgot where I pulled it together from. It's 100 classes of imagenet, 50k train and 10k val images (from ImageNet-1k train set), and 5k test images (from ImageNet-1k val set). 7.4GB instead of > 100GB for the full ImageNet-1k. This ver is not reduced resolution like some other 'mini' versions. Super easy to use with timm train/val scripts, checkout the dataset card.
I often check fine-tuning with even smaller datasets like:
* timm/resisc45
* timm/oxford-iiit-pet
But those are a bit small to train any modest size model w/o starting from pretrained weights.
timm model to use before commiting to download or training with a large dataset? Try mini-imagenet:
timm/mini-imagenet I had this sitting on my drive and forgot where I pulled it together from. It's 100 classes of imagenet, 50k train and 10k val images (from ImageNet-1k train set), and 5k test images (from ImageNet-1k val set). 7.4GB instead of > 100GB for the full ImageNet-1k. This ver is not reduced resolution like some other 'mini' versions. Super easy to use with timm train/val scripts, checkout the dataset card.
I often check fine-tuning with even smaller datasets like:
* timm/resisc45
* timm/oxford-iiit-pet
But those are a bit small to train any modest size model w/o starting from pretrained weights.
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New MobileNetV4 weights were uploaded a few days ago -- more ImageNet-12k training at 384x384 for the speedy 'Conv Medium' models.
There are 3 weight variants here for those who like to tinker. On my hold-out eval they are ordered as below, not that different, but the Adopt 180 epochs closer to AdamW 250 than to AdamW 180.
* AdamW for 250 epochs - timm/mobilenetv4_conv_medium.e250_r384_in12k
* Adopt for 180 epochs - timm/mobilenetv4_conv_medium.e180_ad_r384_in12k
* AdamW for 180 epochs - timm/mobilenetv4_conv_medium.e180_r384_in12k
This was by request as a user reported impressive results using the 'Conv Large' ImagNet-12k pretrains as object detection backbones. ImageNet-1k fine-tunes are pending, the weights do behave differently with the 180 vs 250 epochs and the Adopt vs AdamW optimizer.
There are 3 weight variants here for those who like to tinker. On my hold-out eval they are ordered as below, not that different, but the Adopt 180 epochs closer to AdamW 250 than to AdamW 180.
* AdamW for 250 epochs - timm/mobilenetv4_conv_medium.e250_r384_in12k
* Adopt for 180 epochs - timm/mobilenetv4_conv_medium.e180_ad_r384_in12k
* AdamW for 180 epochs - timm/mobilenetv4_conv_medium.e180_r384_in12k
This was by request as a user reported impressive results using the 'Conv Large' ImagNet-12k pretrains as object detection backbones. ImageNet-1k fine-tunes are pending, the weights do behave differently with the 180 vs 250 epochs and the Adopt vs AdamW optimizer.
Post
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A new
Featured in the release are:
* The MambaOut model, a cheeky arch inspired by SSM but without the SSM part, a ConvNeXt with gating.
* Several timm trained MambaOut variations with arch tweaks and ImageNet-12k pretrain to verify scaling, supplement ported weights.
* The smallest MobileNetV4, a 0.5x width scaled Conv-Small.
* Two impressive MobileNetV3 Large models outperforming all previous, using MNV4 Small recipe.
* 'Zepto,' a new compact ConvNeXt variant even smaller than the previous Atto, 2.2M params, RMSNorm, and solid results for its size.
* Newly ported SigLIP SO400M/16 ViT multi-lingual weights, the largest i18n weights, prevous was B/16.
* Two ImageNet-1k fine-tuned SigLIP SO400M models at 378x378
* InternViT 300M weight port. A really solid ViT encoder distilled from OpenGVLab 6B VL model encoder.
* An assortment of very small, sub 1M param pretrained test models to improve library unit tests and serve low-resource applications.
timm release (1.0.11) is out now. A also wrote an article on one of the included models: https://huggingface.co/blog/rwightman/mambaoutFeatured in the release are:
* The MambaOut model, a cheeky arch inspired by SSM but without the SSM part, a ConvNeXt with gating.
* Several timm trained MambaOut variations with arch tweaks and ImageNet-12k pretrain to verify scaling, supplement ported weights.
* The smallest MobileNetV4, a 0.5x width scaled Conv-Small.
* Two impressive MobileNetV3 Large models outperforming all previous, using MNV4 Small recipe.
* 'Zepto,' a new compact ConvNeXt variant even smaller than the previous Atto, 2.2M params, RMSNorm, and solid results for its size.
* Newly ported SigLIP SO400M/16 ViT multi-lingual weights, the largest i18n weights, prevous was B/16.
* Two ImageNet-1k fine-tuned SigLIP SO400M models at 378x378
* InternViT 300M weight port. A really solid ViT encoder distilled from OpenGVLab 6B VL model encoder.
* An assortment of very small, sub 1M param pretrained test models to improve library unit tests and serve low-resource applications.
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nvidia/Llama-3.1-Nemotron-70B-Instruct-HF is now available in HuggingChat if you want to try it!
https://huggingface.co/chat/models/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
https://huggingface.co/chat/models/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
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NEW - Inference Playground
Maybe like me you have always wanted a super easy way to compare llama3.2-1B vs. llama3.2-3B? or the same model with different temperatures?
Trying and comparing warm Inference API models has never been easier!
Just go to https://hf.co/playground, set your token and you're ready to go.
We'll keep improving, feedback welcome 😊
Maybe like me you have always wanted a super easy way to compare llama3.2-1B vs. llama3.2-3B? or the same model with different temperatures?
Trying and comparing warm Inference API models has never been easier!
Just go to https://hf.co/playground, set your token and you're ready to go.
We'll keep improving, feedback welcome 😊
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- 2 replies