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Jordan Legg PRO
takarajordan
AI & ML interests
Chief AI Officer @takara.ai. Diffusion, Inference optimisation and all things MultiModal.
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#10 opened 3 days ago
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gimme an invite! :D
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s3nh's
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7 days ago
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1613
Welcome back,
Small Language Models Enthusiasts and GPU Poor oss enjoyers lets connect.
Just created an organization which main target is to have fun with smaller models tuneable on consumer range GPUs, feel free to join and lets have some fun, much love ;3
https://huggingface.co/SmolTuners
Small Language Models Enthusiasts and GPU Poor oss enjoyers lets connect.
Just created an organization which main target is to have fun with smaller models tuneable on consumer range GPUs, feel free to join and lets have some fun, much love ;3
https://huggingface.co/SmolTuners
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Amazing work
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7 days ago
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Aya by Cohere For AI can now see! 👀
C4AI community has built Maya 8B, a new open-source multilingual VLM built on SigLIP and Aya 8B 🌱 works on 8 languages! 🗣️
The authors extend Llava dataset using Aya's translation capabilities with 558k examples!
ry it here kkr5155/maya_demo
Dataset maya-multimodal/pretrain
Model maya-multimodal/maya 👏
kudos @nahidalam and team
C4AI community has built Maya 8B, a new open-source multilingual VLM built on SigLIP and Aya 8B 🌱 works on 8 languages! 🗣️
The authors extend Llava dataset using Aya's translation capabilities with 558k examples!
ry it here kkr5155/maya_demo
Dataset maya-multimodal/pretrain
Model maya-multimodal/maya 👏
kudos @nahidalam and team
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Apollo is a new family of open-source video language models by Meta, where 3B model outperforms most 7B models and 7B outperforms most 30B models 🧶
✨ the models come in 1.5B https://huggingface.co/Apollo-LMMs/Apollo-1_5B-t32, 3B https://huggingface.co/Apollo-LMMs/Apollo-3B-t32 and 7B https://huggingface.co/Apollo-LMMs/Apollo-7B-t32 with A2.0 license, based on Qwen1.5 & Qwen2
✨ the authors also release a benchmark dataset https://huggingface.co/spaces/Apollo-LMMs/ApolloBench
The paper has a lot of experiments (they trained 84 models!) about what makes the video LMs work ⏯️
Try the demo for best setup here https://huggingface.co/spaces/Apollo-LMMs/Apollo-3B
they evaluate sampling strategies, scaling laws for models and datasets, video representation and more!
> The authors find out that whatever design decision was applied to small models also scale properly when the model and dataset are scaled 📈 scaling dataset has diminishing returns for smaller models
> They evaluate frame sampling strategies, and find that FPS sampling is better than uniform sampling, and they find 8-32 tokens per frame optimal
> They also compare image encoders, they try a variation of models from shape optimized SigLIP to DINOv2
they find google/siglip-so400m-patch14-384 to be most powerful 🔥
> they also compare freezing different parts of models, training all stages with some frozen parts give the best yield
They eventually release three models, where Apollo-3B outperforms most 7B models and Apollo 7B outperforms 30B models 🔥
✨ the models come in 1.5B https://huggingface.co/Apollo-LMMs/Apollo-1_5B-t32, 3B https://huggingface.co/Apollo-LMMs/Apollo-3B-t32 and 7B https://huggingface.co/Apollo-LMMs/Apollo-7B-t32 with A2.0 license, based on Qwen1.5 & Qwen2
✨ the authors also release a benchmark dataset https://huggingface.co/spaces/Apollo-LMMs/ApolloBench
The paper has a lot of experiments (they trained 84 models!) about what makes the video LMs work ⏯️
Try the demo for best setup here https://huggingface.co/spaces/Apollo-LMMs/Apollo-3B
they evaluate sampling strategies, scaling laws for models and datasets, video representation and more!
> The authors find out that whatever design decision was applied to small models also scale properly when the model and dataset are scaled 📈 scaling dataset has diminishing returns for smaller models
> They evaluate frame sampling strategies, and find that FPS sampling is better than uniform sampling, and they find 8-32 tokens per frame optimal
> They also compare image encoders, they try a variation of models from shape optimized SigLIP to DINOv2
they find google/siglip-so400m-patch14-384 to be most powerful 🔥
> they also compare freezing different parts of models, training all stages with some frozen parts give the best yield
They eventually release three models, where Apollo-3B outperforms most 7B models and Apollo 7B outperforms 30B models 🔥
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7 days ago
you guys are amazing!
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In the past seven days, the Diffusers team has shipped:
1. Two new video models
2. One new image model
3. Two new quantization backends
4. Three new fine-tuning scripts
5. Multiple fixes and library QoL improvements
Coffee on me if someone can guess 1 - 4 correctly.
1. Two new video models
2. One new image model
3. Two new quantization backends
4. Three new fine-tuning scripts
5. Multiple fixes and library QoL improvements
Coffee on me if someone can guess 1 - 4 correctly.
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7 days ago
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🦙New NVIDIA paper: LLaMA-Mesh 🦙
We enable large language models to generate and understand 3D meshes by representing them as text and fine-tuning. This unifies the 3D and text modalities in a single model and preserves language abilities, unlocking conversational 3D creation with mesh understanding.
🔎 Project Page: https://research.nvidia.com/labs/toronto-ai/LLaMA-Mesh/
🕹️ Interactive Demo: Zhengyi/LLaMA-Mesh (courtesy of HuggingFace and Gradio)
📖 Full Paper: https://arxiv.org/abs/2411.09595
👨💻Code: https://github.com/nv-tlabs/LLaMa-Mesh
💾 Model Checkpoint: Zhengyi/LLaMA-Mesh
🧩 Blender Addon: https://github.com/huggingface/meshgen (courtesy of Dylan Ebert)
🎥 5-min Overview Video: https://youtu.be/eZNazN-1lPo?si=-idQa5aaceVw0Bbj (courtesy of AI Papers Academy)
We enable large language models to generate and understand 3D meshes by representing them as text and fine-tuning. This unifies the 3D and text modalities in a single model and preserves language abilities, unlocking conversational 3D creation with mesh understanding.
🔎 Project Page: https://research.nvidia.com/labs/toronto-ai/LLaMA-Mesh/
🕹️ Interactive Demo: Zhengyi/LLaMA-Mesh (courtesy of HuggingFace and Gradio)
📖 Full Paper: https://arxiv.org/abs/2411.09595
👨💻Code: https://github.com/nv-tlabs/LLaMa-Mesh
💾 Model Checkpoint: Zhengyi/LLaMA-Mesh
🧩 Blender Addon: https://github.com/huggingface/meshgen (courtesy of Dylan Ebert)
🎥 5-min Overview Video: https://youtu.be/eZNazN-1lPo?si=-idQa5aaceVw0Bbj (courtesy of AI Papers Academy)
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Training YOLO with Synthetic Data from Duality AI's Falcon Simulation Software 🎮📊
Hello again! 👋 Duality.ai has released a second Google Colab and tutorial for training a YOLOv8 model using synthetic data from our Falcon simulation software!
https://falcon.duality.ai/secure/documentation/see-synth-work-no-specs?sidebarMode=learn#download-the-colab-notebook
Train using synthetic images of a soup can twin this time, and see it work on real-world images. 🥫🍜
The tutorial also walks you through how to add your own twin from our FalconCloud library, and our goal is to equip people like you to be able to create your own data for your own projects.
You'll have to create a free account to access the files, but once you do, you'll have access to not only this colab file, but also all of our lessons and our digital twin library. 🎓
Instructions for creating the synthetic data accessed by the colab notebook can be found here: https://falcon.duality.ai/secure/documentation/ex2-objdetection-newtwin?sidebarMode=learn
This method is a game-changer for cost-effective, scalable, and customizable datasets in computer vision.
Why Synthetic Data?🤔
- Precise Annotations: Get bounding boxes, segmentation masks, and more without manual effort.
- Customizable Scenarios: Get comprehensive data and cover all corner cases by simulating diverse conditions like lighting, weather, visual occlusions, and more.
What’s in the Notebook?📓
- Training & Evaluation: Train YOLOv8 with synthetic data and test its performance on real-world samples.
Let’s Discuss!💬
Check out our discord to see how people are using the Falcon simulation software to develop strong datasets and train robust models. https://discord.com/invite/dualityfalconcommunity
Hello again! 👋 Duality.ai has released a second Google Colab and tutorial for training a YOLOv8 model using synthetic data from our Falcon simulation software!
https://falcon.duality.ai/secure/documentation/see-synth-work-no-specs?sidebarMode=learn#download-the-colab-notebook
Train using synthetic images of a soup can twin this time, and see it work on real-world images. 🥫🍜
The tutorial also walks you through how to add your own twin from our FalconCloud library, and our goal is to equip people like you to be able to create your own data for your own projects.
You'll have to create a free account to access the files, but once you do, you'll have access to not only this colab file, but also all of our lessons and our digital twin library. 🎓
Instructions for creating the synthetic data accessed by the colab notebook can be found here: https://falcon.duality.ai/secure/documentation/ex2-objdetection-newtwin?sidebarMode=learn
This method is a game-changer for cost-effective, scalable, and customizable datasets in computer vision.
Why Synthetic Data?🤔
- Precise Annotations: Get bounding boxes, segmentation masks, and more without manual effort.
- Customizable Scenarios: Get comprehensive data and cover all corner cases by simulating diverse conditions like lighting, weather, visual occlusions, and more.
What’s in the Notebook?📓
- Training & Evaluation: Train YOLOv8 with synthetic data and test its performance on real-world samples.
Let’s Discuss!💬
Check out our discord to see how people are using the Falcon simulation software to develop strong datasets and train robust models. https://discord.com/invite/dualityfalconcommunity
- 2 replies