Akhil-Theerthala/Raw-Reddit-Feb-2023
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Lacking vllm support for Transformers v5, frustrating only me?
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Diversity Vs Density: A data strategy comparison for fine-tuning VLMs
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🎉 Exciting News — NVIDIA Cosmos is celebrating its 1st birthday and has hit 5 MILLION downloads! 🎉
In just one year, the Cosmos ecosystem has grown rapidly:
🧠 Cosmos Reason and Cosmos Predict have surpassed 2 MILLION downloads each on @HuggingFace , topping physical AI leaderboards
🔄 Cosmos Transfer is enabling adaptation across domains and tasks
🔮 Cosmos Cookbook is the go-to hub for recipes from developers and partners like Uber and IntBot.
Thank you to our amazing developer community for making this possible. Here's to pushing the boundaries of world foundation models together!
🧑🏻🍳Read the Cosmos Cookbook: https://nvda.ws/4qevli8
📚 Explore Models & Datasets: https://huggingface.co/collections/nvidia/nvidia-cosmos-2
In just one year, the Cosmos ecosystem has grown rapidly:
🧠 Cosmos Reason and Cosmos Predict have surpassed 2 MILLION downloads each on @HuggingFace , topping physical AI leaderboards
🔄 Cosmos Transfer is enabling adaptation across domains and tasks
🔮 Cosmos Cookbook is the go-to hub for recipes from developers and partners like Uber and IntBot.
Thank you to our amazing developer community for making this possible. Here's to pushing the boundaries of world foundation models together!
🧑🏻🍳Read the Cosmos Cookbook: https://nvda.ws/4qevli8
📚 Explore Models & Datasets: https://huggingface.co/collections/nvidia/nvidia-cosmos-2
reacted to their post with 👍 4 months ago
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Is it better to show a model too many images once (Diversity), or extract as much information from a small set of images?
I have always wanted to do an ablation study on this and recently I got the chance to do exactly that. Why? In applied domains like robotics, manufacturing, or banking, we rarely have the luxury of internet-scale diverse image datasets. We are often "Data Poor" in terms of diversity but "Data Rich" in depth.
The takeaway? Density is efficient for facts but dangerous for reasoning (logical collapse) if you don't have larger scale data.
More details:
https://huggingface.co/blog/Akhil-Theerthala/diversity-density-for-vision-language-models
I have always wanted to do an ablation study on this and recently I got the chance to do exactly that. Why? In applied domains like robotics, manufacturing, or banking, we rarely have the luxury of internet-scale diverse image datasets. We are often "Data Poor" in terms of diversity but "Data Rich" in depth.
The takeaway? Density is efficient for facts but dangerous for reasoning (logical collapse) if you don't have larger scale data.
More details:
https://huggingface.co/blog/Akhil-Theerthala/diversity-density-for-vision-language-models
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commented on Diversity Vs Density: A data strategy comparison for fine-tuning VLMs 4 months ago
Great.
For 1, Indeed, that was a mistake on my part, I have done the validations but I forgot to add them over here. I orignally had a new section in "The Setup" which talked about using Qwen3-8b-thikning as the baseline performance. It showed ~52.8% on the validation set. I was under the impression that I replaced the section with the updated figure. I missed the fact that the figure wasn't updated. Will go back and change the figure now with the baselines of Qwen3-8B-Thinking on them.
Indeed the various other categories for the validation is an interesting idea to work on, will add that. Mainly would start by a deeper analysis of question_format Vs Validation. Then would go around and think about adding more sections like target_object category, orientation etc.
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commented on Diversity Vs Density: A data strategy comparison for fine-tuning VLMs 4 months ago
Hey, Thanks a lot for the amazing response!
I plan to expand upon this. The reason I chose this as a checkpoint is because of two key reasons, one being that for the current data scale, this was the best I could do in the budgets that I had. Secondly, I wanted to test the waters first with a smaller scale data. I trained these until the losses kept diverging to have a good estimate.
I also have to get better teacher models, as currently the chain of thought is generated by GPT-5-mini for the training set. I would like to follow OpenThoughts and use Qwen3-32B or Qwen3-Next family models to get better inference.
Additionally I want to see add more VL models like Molmo2-8B and also gemma3 12b with kimi-vl. But again that scope creep had to be controlled.
As for training on differnet datasets after each epoch, that can be done. However, that's still out of scope for the main question as the goal was to see if density approach could help or should we strictly prefer diversity.
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commented on Diversity Vs Density: A data strategy comparison for fine-tuning VLMs 4 months ago
The collection of artefacts for the blogpost can be found here
https://hf.co/collections/Akhil-Theerthala/density-vs-diversity-blogpost
replied to their post 4 months ago
The collection of artefacts for the blogpost can be found here
https://hf.co/collections/Akhil-Theerthala/density-vs-diversity-blogpost