SmolVLM: Redefining small and efficient multimodal models
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2504.05299
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pcuenq
authored
a
paper
19 days ago
Post
2277
Wohoo 🥳 I have finished my 2025 GPU workstation build and I am very excited to train new awesome open source models on it.
I built my last GPU workstation 5 years ago featuring an AMD Ryzen 5900X, 64GB of G.SKILL Trident Z RGB on an ASRock X570 Taichi cooled by an Alphacool Eisbär 420. GPU was a Zotac RTX 3090 AMP Extreme. Unfortunately, I was never satisfied with the case - some Fractal Define 7, as it is definitely too small, airflow is not optimal as I had to open the front door all the time and it also arrived with a partly damaged side panel.
For my new build, I've used the following components: an outstanding new AMD Ryzen 9950X3D with 64GB of Corsair Dominator Titanium (what a name). As a huge Noctua fan - warm greetings to my Austrian neighbors - I am using the brand new Noctua NH-D15 G2 on an ASRock X870E Taichi in an amazing Lian Li LANCOOL III chassis. One joke that only NVIDIA Blackwell users will understand: you definitely need a tempered glass panel to check if your GPU cables/connectors start melting 😂 And the best is yet to come: I returned my previously bought Zotac RTX 5090 Solid to the eBay seller (because of... missing ROPs, only NVIDIA Blackwell users will again understand) and bought a Zotac 5090 AMP Extreme INFINITY (yes, the long name indicates that this is the flagship model from Zotac) from a more trustworthy source (NBB in Germany).
I am so happy to start training and fine-tuning new open source models - stay tuned!!!
I built my last GPU workstation 5 years ago featuring an AMD Ryzen 5900X, 64GB of G.SKILL Trident Z RGB on an ASRock X570 Taichi cooled by an Alphacool Eisbär 420. GPU was a Zotac RTX 3090 AMP Extreme. Unfortunately, I was never satisfied with the case - some Fractal Define 7, as it is definitely too small, airflow is not optimal as I had to open the front door all the time and it also arrived with a partly damaged side panel.
For my new build, I've used the following components: an outstanding new AMD Ryzen 9950X3D with 64GB of Corsair Dominator Titanium (what a name). As a huge Noctua fan - warm greetings to my Austrian neighbors - I am using the brand new Noctua NH-D15 G2 on an ASRock X870E Taichi in an amazing Lian Li LANCOOL III chassis. One joke that only NVIDIA Blackwell users will understand: you definitely need a tempered glass panel to check if your GPU cables/connectors start melting 😂 And the best is yet to come: I returned my previously bought Zotac RTX 5090 Solid to the eBay seller (because of... missing ROPs, only NVIDIA Blackwell users will again understand) and bought a Zotac 5090 AMP Extreme INFINITY (yes, the long name indicates that this is the flagship model from Zotac) from a more trustworthy source (NBB in Germany).
I am so happy to start training and fine-tuning new open source models - stay tuned!!!
- 2 replies
Post
966
🇹🇷 😍 I'm very happy to finally announce my new Turkish LM called "BERT5urk":
stefan-it/bert5urk
It is a 1.42B T5-based model, trained with UL2 pretraining objective on the Turkish part of the awesome HuggingFaceFW/fineweb-2 dataset.
Feel free to check it out!
stefan-it/bert5urk
It is a 1.42B T5-based model, trained with UL2 pretraining objective on the Turkish part of the awesome HuggingFaceFW/fineweb-2 dataset.
Feel free to check it out!
- 1 reply
Post
3173
After running some 3DMark and FurMark benchmarks on Windows to make sure that my new 5090 is not causing melting cables [1] and some nice shots with a thermal camera (I don't think that's too much), running some fine-tuning experiments with my favorite Flair & Transformers libraries are very easy to perform.
Important steps:
Good idea is to start with a fresh Ubuntu 24.04 installation with latest CUDA 12.8 and the open NVIDIA driver - follow more advices from [2]:
I tried update from an existing Ubuntu installation with an older CUDA and driver version and it resulted in a non-startable system.
If you are using PyTorch 2.6 with built CUDA 12.6 it will result in:
But no worries! For PyTorch you need just to use a nightly 2.7 version that was built with CUDA 12.8. This can easily done via:
After that the latest Flair version can be installed and fine-tuning will work!
References:
[1]: https://www.reddit.com/r/nvidia/comments/1inpox7/rtx_50_series_12vhpwr_megathread/
[2]: https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=24.04&target_type=deb_network
Important steps:
Good idea is to start with a fresh Ubuntu 24.04 installation with latest CUDA 12.8 and the open NVIDIA driver - follow more advices from [2]:
sudo apt -y install cuda-toolkit-12-8 nvidia-openI tried update from an existing Ubuntu installation with an older CUDA and driver version and it resulted in a non-startable system.
If you are using PyTorch 2.6 with built CUDA 12.6 it will result in:
NVIDIA Graphics Device with CUDA capability sm_120 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_50 sm_60 sm_70 sm_75 sm_80 sm_86 sm_90.But no worries! For PyTorch you need just to use a nightly 2.7 version that was built with CUDA 12.8. This can easily done via:
pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu128After that the latest Flair version can be installed and fine-tuning will work!
References:
[1]: https://www.reddit.com/r/nvidia/comments/1inpox7/rtx_50_series_12vhpwr_megathread/
[2]: https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=24.04&target_type=deb_network
- 1 reply
Post
5116
She arrived 😍
[Expect more models soon...]
[Expect more models soon...]
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- 2 replies
Post
6425
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
Adding `safetensors` variant of this model
#1 opened 3 months ago
by
SFconvertbot
g8a9
authored
a
paper
3 months ago
Post
1584
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
LouisCastricato
authored
a
paper
3 months ago
avramandrei
authored
a
paper
3 months ago
Post
1184
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
versae
authored
a
paper
4 months ago
Post
1555
My latest project is the outcome of the last 2+ years working with TPUs from the amazing TPU Research Cloud (TRC) program and training Encoder-only LMs with the TensorFlow Model Garden library.
👉 Link: https://github.com/stefan-it/model-garden-lms
An overview of some features:
- Cheatsheet for setting-up a TPU VM Pod (with all necessary dependencies) to pretrain LMs with TF Model Garden
- Conversion scripts that convert TF Model Garden weights to Hugging Face Transformers-compatible models
- Supported architectures include BERT, BERT with Token Dropping and TEAMS
I also released BERT-based models pretrained on the great Hugging Face FineWeb and FineWeb-Edu datasets (10BT subset). With more to come!
👉 Model Hub Link:
model-garden-lms
If you find these resources useful, please give them a like!
Made from Bavarian Oberland with ❤️ and 🥨.
👉 Link: https://github.com/stefan-it/model-garden-lms
An overview of some features:
- Cheatsheet for setting-up a TPU VM Pod (with all necessary dependencies) to pretrain LMs with TF Model Garden
- Conversion scripts that convert TF Model Garden weights to Hugging Face Transformers-compatible models
- Supported architectures include BERT, BERT with Token Dropping and TEAMS
I also released BERT-based models pretrained on the great Hugging Face FineWeb and FineWeb-Edu datasets (10BT subset). With more to come!
👉 Model Hub Link:
model-garden-lms
If you find these resources useful, please give them a like!
Made from Bavarian Oberland with ❤️ and 🥨.
christopher
posted
an
update
5 months ago
Post
1811
The folks at Foursquare released a dataset of 104.5 million places of interest (
foursquare/fsq-os-places) and here's all of them on a plot
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- 4 replies
christopher
posted
an
update
5 months ago
Post
2413
The Lichess database of games, puzzles, and engine evaluations is now on the Hub:
Lichess
Billions of chess data points to download, query, and stream and we're excited to see what you'll build with it! ♟️ 🤗
- Lichess/positions-datasets-66f50837db5cd3287d60d489
- Lichess/games-datasets-66f508df78f4b43e1bb2d353
Lichess
Billions of chess data points to download, query, and stream and we're excited to see what you'll build with it! ♟️ 🤗
- Lichess/positions-datasets-66f50837db5cd3287d60d489
- Lichess/games-datasets-66f508df78f4b43e1bb2d353
paws
authored
a
paper
5 months ago
Post
1454
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
- 3 replies
Post
1377
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
1081
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.