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youssef boulaouane

byoussef

Boulaouaney

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liked a model 14 days ago

deepseek-ai/DeepSeek-OCR

reacted to merve's post with 🚀 14 days ago

https://huggingface.co/deepseek-ai/DeepSeek-OCR is out! 🔥 my take ⤵️ > pretty insane it can parse and re-render charts in HTML > it uses CLIP and SAM features concatenated, so better grounding > very efficient per vision tokens/performance ratio > covers 100 languages

upvoted a paper 21 days ago

Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation

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reacted to merve's post with 🚀 14 days ago

Post

4851

deepseek-ai/DeepSeek-OCR is out! 🔥 my take ⤵️
> pretty insane it can parse and re-render charts in HTML
> it uses CLIP and SAM features concatenated, so better grounding
> very efficient per vision tokens/performance ratio
> covers 100 languages

2 replies

reacted to vikhyatk's post with 🔥 about 1 month ago

Post

4213

Just released a preview of Moondream 3! moondream/moondream3-preview

This is a 9B parameter, 2B active MoE VLM with state of the art visual reasoning capabilities.

More details in the release blog post: https://moondream.ai/blog/moondream-3-preview

3 replies

reacted to jasoncorkill's post with 🔥 7 months ago

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2276

🔥 It's out! We published the dataset for our evaluation of @OpenAI 's new 4o image generation model.

Rapidata/OpenAI-4o_t2i_human_preference

Yesterday we published the first large evaluation of the new model, showing that it absolutely leaves the competition in the dust. We have now made the results and data available here! Please check it out and ❤️ !

reacted to tianchez's post with 🚀 9 months ago

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4604

Introducing VLM-R1!

GRPO has helped DeepSeek R1 to learn reasoning. Can it also help VLMs perform stronger for general computer vision tasks?

The answer is YES and it generalizes better than SFT. We trained Qwen 2.5 VL 3B on RefCOCO (a visual grounding task) and eval on RefCOCO Val and RefGTA (an OOD task).

https://github.com/om-ai-lab/VLM-R1

3 replies

reacted to andrewrreed's post with 🔥 10 months ago

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3125

🚀 Supercharge your LLM apps with Langfuse on Hugging Face Spaces!

Langfuse brings end-to-end observability and tooling to accelerate your dev workflow from experiments through production

Now available as a Docker Space directly on the HF Hub! 🤗

🔍 Trace everything: monitor LLM calls, retrieval, and agent actions with popular frameworks
1⃣ One-click deployment: on Spaces with persistent storage and integrated OAuth
🛠 Simple Prompt Management: Version, edit, and update without redeployment
✅ Intuitive Evals: Collect user feedback, run model/prompt evaluations, and improve quality
📊 Dataset Creation: Build datasets directly from production data to enhance future performance

Kudos to the Langfuse team for this collab and the awesome, open-first product they’re building! 👏 @marcklingen @Clemo @MJannik

🔗 Space: langfuse/langfuse-template-space
🔗 Docs: https://huggingface.co/docs/hub/spaces-sdks-docker-langfuse

1 reply

reacted to merve's post with 🚀 11 months ago

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2699

small but mighty 🔥
you can fine-tune SmolVLM on an L4 with batch size of 4 and it will only take 16.4 GB VRAM 🫰🏻 also with gradient accumulation simulated batch size is 16 ✨
I made a notebook that includes all the goodies: QLoRA, gradient accumulation, gradient checkpointing with explanations on how they work 💝 https://github.com/huggingface/smollm/blob/main/finetuning/Smol_VLM_FT.ipynb

reacted to rwightman's post with 🚀 12 months ago

Post

1089

Want to validate some hparams or figure out what 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.

reacted to averoo's post with 🔥 about 1 year ago

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3805

Hello, researchers! I've tried to made reading HF Daily Papers easier and made a tool that does reviews with LLMs like Claude 3.5, GPT-4o and sometimes FLUX.

📚 Classification by topics
📅 Sorting by publication date and HF addition date
🔄 Syncing every 2 hours
💻 Hosted on GitHub
🌏 English, Russian, and Chinese
📈 Top by week/month (in progress)

👉 https://hfday.ru

Let me know what do you think of it.

reacted to rwightman's post with ❤️ about 1 year ago

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2640

A 'small' MobileNet-V4 update, I just pushed weights for the smallest model I've trained in the series, a 0.5 width multiplier version of the MobileNet-V4 Conv Small.

Now you may look at this and say hey, why is this impressive? 64.8% top-1 and 2.2M params? MobileNetV3-Small 0.75, and MobileNet-V2 0.5 are both fewer params (at ~2M) and over 65% top-1, what gives? Well this is where MobileNet-V4 differs from the previous versions of the model family, it trades off (gives up) a little parameter efficiency for some computational efficiency.

So, let's look at the speed. On a 4090 w/ torchcompile
* 98K img/sec - timm/mobilenetv4_conv_small_050.e3000_r224_in1k
* 58K img/sec - timm/mobilenetv3_small_075.lamb_in1k
* 37K img/sec - timm/mobilenetv2_050.lamb_in1k

And there you go, if you have a need for speed, MNV4 is the better option.

posted an update over 1 year ago

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1065

On-Device deployment ready MobileNet-V4 models:
byoussef/mobilenetv4-conv-pretrained-tflite-models-66726c945040fc61f5a50b63

Models converted from timm's pretrained Pytorch weights to TFLite (fp32 & fp16).
All conv models fully compatible with TFLite Gpu Delegate on Android for fast on-device inference 🚀
Can also run super fast on Google-Edge-TPU on Pixel phones 🚀

reacted to merve's post with 🔥 over 1 year ago

Post

3191

releasing: smol vision 🌼

A repository with notebooks on shrinking, optimizing, speeding-up, customizing large vision models! https://github.com/merveenoyan/smol-vision

1 reply

reacted to beomi's post with 👍 over 1 year ago

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18027

#TPU #PyTorch #Jax

When You're trying to use PyTorch or Jax on TPU,

for v2/v3/v4:
use tpu-ubuntu2204-base

for v5p:
use v2-alpha-tpuv5

for v5e:
use v2-alpha-tpuv5-lite

You must use these base images for the system to 'boot'.

Previously used tpu-vm-v4-pt-1.13 images might seem to start the VM, but SSH connections do not work.

I thought it was a firewall issue and spent a lot of time on it before realizing it was a problem with the boot image 🥲

https://cloud.google.com/tpu/docs/runtimes#pytorch_and_jax

youssef boulaouane

AI & ML interests

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