Data exploration and filtering with Nomic Atlas
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Alexander Visheratin
visheratin
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updated
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22 days ago
visheratin/mexma-siglip
updated
a model
22 days ago
visheratin/nllb-clip-large-siglip
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visheratin's activity
posted
an
update
9 months ago
Post
3241
Yesterday, xAI announced Grok-1.5 Vision - https://x.ai/blog/grok-1.5v. But more importantly, they also released a new VLM benchmark dataset - RealWorldQA. The only problem was that they released it as a ZIP archive. I fixed that! Now you can use it in your evaluations as a regular HF dataset:
visheratin/realworldqa
- 1 reply
reacted to
Smooke's
post with 🔥
9 months ago
Post
2819
Spent the morning designing a super important product: SMALL LANGUAGE MODEL BABY ONESIE https://www.shop.hackernoon.com/all-merch/p/small-language-model-baby-onesie
- 3 replies
posted
an
update
9 months ago
Post
1985
Look at the beauty in the video — four different embeddings on the same map! In another community blog post, I explore how you can use Nomic Atlas to view and clean your dataset. You can check it out here - https://huggingface.co/blog/visheratin/nomic-data-cleaning
- 1 reply
reacted to
osanseviero's
post with ❤️🚀
9 months ago
Post
1883
Diaries of Open Source. Part 7!
🔥Sakana releases Evolutionary Model Merge
Blog post: https://sakana.ai/evolutionary-model-merge/
Paper: Evolutionary Optimization of Model Merging Recipes (2403.13187)
Models and demo: https://hf.co/SakanaAI
🍞MixedBread releases new SoTA sentence embedding model
Announcement: https://www.mixedbread.ai/blog/mxbai-embed-large-v1
Model: mixedbread-ai/mxbai-embed-large-v1
🎥VideoMamba, a Mamba-based model for video understanding
Blog: https://hf.co/blog/vladbogo/video-mamba
Demo: OpenGVLab/VideoMamba
Model: OpenGVLab/VideoMamba
🔍 MathVerse, a visual math benchmark for multimodal LLMs
Paper page: https://mathverse-cuhk.github.io/
Dataset: AI4Math/MathVerse
Paper: MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems? (2403.14624)
🧠GraphWiz, a family of instruct-tuned LLMs to solve graph problems
Repos: https://hf.co/GraphWiz
Paper: GraphWiz: An Instruction-Following Language Model for Graph Problems (2402.16029)
🪆NLLB-SigLIP-MRL: a combination of NLLB and SigLIP trained with Matryoshka representation learning
Model: visheratin/nllb-siglip-mrl-large
Tweet: https://twitter.com/visheratin/status/1766643219909984734?s=46
🧍HDM and ProciGen: Template-free reconstruction of human-object interactions
Paper page: https://virtualhumans.mpi-inf.mpg.de/procigen-hdm/
Demo: xiexh20/HDM-interaction-recon
Models: xiexh20/HDM-models
🌎Models and data around the world
EagleX 7B, multi-lingual RNN-based model https://hf.co/spaces/recursal/EagleX-7B-1.7T-Gradio-Demo
Tamil LLM mervinpraison/tamil-large-language-model-7b-v1.0
🔥Sakana releases Evolutionary Model Merge
Blog post: https://sakana.ai/evolutionary-model-merge/
Paper: Evolutionary Optimization of Model Merging Recipes (2403.13187)
Models and demo: https://hf.co/SakanaAI
🍞MixedBread releases new SoTA sentence embedding model
Announcement: https://www.mixedbread.ai/blog/mxbai-embed-large-v1
Model: mixedbread-ai/mxbai-embed-large-v1
🎥VideoMamba, a Mamba-based model for video understanding
Blog: https://hf.co/blog/vladbogo/video-mamba
Demo: OpenGVLab/VideoMamba
Model: OpenGVLab/VideoMamba
🔍 MathVerse, a visual math benchmark for multimodal LLMs
Paper page: https://mathverse-cuhk.github.io/
Dataset: AI4Math/MathVerse
Paper: MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems? (2403.14624)
🧠GraphWiz, a family of instruct-tuned LLMs to solve graph problems
Repos: https://hf.co/GraphWiz
Paper: GraphWiz: An Instruction-Following Language Model for Graph Problems (2402.16029)
🪆NLLB-SigLIP-MRL: a combination of NLLB and SigLIP trained with Matryoshka representation learning
Model: visheratin/nllb-siglip-mrl-large
Tweet: https://twitter.com/visheratin/status/1766643219909984734?s=46
🧍HDM and ProciGen: Template-free reconstruction of human-object interactions
Paper page: https://virtualhumans.mpi-inf.mpg.de/procigen-hdm/
Demo: xiexh20/HDM-interaction-recon
Models: xiexh20/HDM-models
🌎Models and data around the world
EagleX 7B, multi-lingual RNN-based model https://hf.co/spaces/recursal/EagleX-7B-1.7T-Gradio-Demo
Tamil LLM mervinpraison/tamil-large-language-model-7b-v1.0
- 2 replies
reacted to
osanseviero's
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10 months ago
Post
Diaries of Open Source. Part 4!
🌏Cohere and Cohere4AI release Command-R, a 35B model that is multilingual, RAG-optimized, and can manage tools!
Model: CohereForAI/c4ai-command-r-v01
Blog post: https://txt.cohere.com/command-r/
🧑🍳StarChat2: A powerful code model that is conversational
Try it out: HuggingFaceH4/starchat2-playground
Repos: HuggingFaceH4/starchat2-15b-65f068417b330fafad751fce
Training code: https://github.com/huggingface/alignment-handbook/tree/main/recipes/starchat2-15b
🐲Yi-9B: trained on 3 trillion tokens, this english-chinese LLM is quite good and with a very nice detailed report!
Model: 01-ai/Yi-9B
Paper: Yi: Open Foundation Models by 01.AI (2403.04652)
🐋DeepSeek-VL, 1.3B and 7B VLMs
Paper: DeepSeek-VL: Towards Real-World Vision-Language Understanding (2403.05525)
Large model: deepseek-ai/deepseek-vl-7b-chat
✍️Writer releases OmniACT: a dataset for multimodal agents for desktop and web.
Dataset: Writer/omniact
Paper: OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web (2402.17553)
🍎Apple releases MobileCLIP: fast image-text models! https://github.com/apple/ml-mobileclip
🦙💪LlamaGym - fine-tune LLM agents with RL in just a few lines of code! https://github.com/KhoomeiK/LlamaGym
🖼️New multimodal leaderboard ConTextual https://huggingface.co/blog/leaderboard-contextual
🎁 Design2Code: benchmark for multimodal LLMs for automating front-end development.
Dataset SALT-NLP/Design2Code
Paper Design2Code: How Far Are We From Automating Front-End Engineering? (2403.03163)
Project https://salt-nlp.github.io/Design2Code/
You can find the previous part at https://huggingface.co/posts/osanseviero/633758457910104
🌏Cohere and Cohere4AI release Command-R, a 35B model that is multilingual, RAG-optimized, and can manage tools!
Model: CohereForAI/c4ai-command-r-v01
Blog post: https://txt.cohere.com/command-r/
🧑🍳StarChat2: A powerful code model that is conversational
Try it out: HuggingFaceH4/starchat2-playground
Repos: HuggingFaceH4/starchat2-15b-65f068417b330fafad751fce
Training code: https://github.com/huggingface/alignment-handbook/tree/main/recipes/starchat2-15b
🐲Yi-9B: trained on 3 trillion tokens, this english-chinese LLM is quite good and with a very nice detailed report!
Model: 01-ai/Yi-9B
Paper: Yi: Open Foundation Models by 01.AI (2403.04652)
🐋DeepSeek-VL, 1.3B and 7B VLMs
Paper: DeepSeek-VL: Towards Real-World Vision-Language Understanding (2403.05525)
Large model: deepseek-ai/deepseek-vl-7b-chat
✍️Writer releases OmniACT: a dataset for multimodal agents for desktop and web.
Dataset: Writer/omniact
Paper: OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web (2402.17553)
🍎Apple releases MobileCLIP: fast image-text models! https://github.com/apple/ml-mobileclip
🦙💪LlamaGym - fine-tune LLM agents with RL in just a few lines of code! https://github.com/KhoomeiK/LlamaGym
🖼️New multimodal leaderboard ConTextual https://huggingface.co/blog/leaderboard-contextual
🎁 Design2Code: benchmark for multimodal LLMs for automating front-end development.
Dataset SALT-NLP/Design2Code
Paper Design2Code: How Far Are We From Automating Front-End Engineering? (2403.03163)
Project https://salt-nlp.github.io/Design2Code/
You can find the previous part at https://huggingface.co/posts/osanseviero/633758457910104
replied to
their
post
10 months ago
It uses the same vision encoder, so I expect that nothing changes.
posted
an
update
10 months ago
Post
Keep stacking cool stuff and getting better results! After I changed the standard vision encoder to SigLIP, NLLB-CLIP got a 10% average performance improvement. And now, I added matryoshka layers (https://arxiv.org/abs/2205.13147) to enable smaller embeddings and got another 6% performance boost! Plus, thanks to MRL, 4.5x smaller embeddings retain 90%+ quality.
The large model is finally SoTA for both image and text multilingual retrieval!
The models are available on the hub:
- visheratin/nllb-siglip-mrl-base
- visheratin/nllb-siglip-mrl-large
The large model is finally SoTA for both image and text multilingual retrieval!
The models are available on the hub:
- visheratin/nllb-siglip-mrl-base
- visheratin/nllb-siglip-mrl-large
-
- 2 replies
reacted to
osanseviero's
post with 👍
10 months ago
Post
Diaries of Open Source. Part 3! OS goes to the moon!
💻 OpenCodeInterpreter, a family of very powerful code generation models
Models: m-a-p/opencodeinterpreter-65d312f6f88da990a64da456
Paper: OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement (2402.14658)
Demo m-a-p/OpenCodeInterpreter_demo
🔷🔶Zephyr 7B Gemma, Gemma fine-tuned with the Zephyr recipe
Model: HuggingFaceH4/zephyr-7b-gemma-v0.1
Demo: HuggingFaceH4/zephyr-7b-gemma-chat
GH Repo: https://github.com/huggingface/alignment-handbook
🪆The MixedBread folks released a 2D Matryoshka text embedding model, which means you can dynamically change the embedding size and layer counts
Model: mixedbread-ai/mxbai-embed-2d-large-v1
Release blog post: https://www.mixedbread.ai/blog/mxbai-embed-2d-large-v1
🐋Microsoft released Orca Math, which includes 200K grade school math problems
Dataset: microsoft/orca-math-word-problems-200k
🥷IBM silently released Merlinite, a cool model trained on Mixtral-generated synthetic data using a novel LAB method ibm/merlinite-7b
🌚 Moondream2 - a small vision language model to run on-device!
Model: vikhyatk/moondream2
Demo: vikhyatk/moondream2
🏙️CityDreamer: 3D City Generation
Demo: hzxie/city-dreamer
Repo: https://github.com/hzxie/city-dreamer
Model: hzxie/city-dreamer
🌏ML in all languages
Sailor, a family of South-East Asian languages models sail/sailor-language-models-65e19a749f978976f1959825
Samvaad dataset, which includes 140k QA pairs in Hindi, Bengali, Marathi, Tamil, Telugu, Oriya, Punjabi, and Gujarati GenVRadmin/Samvaad-Mixed-Language-2
You can see the previous part at https://huggingface.co/posts/osanseviero/674644082063278
💻 OpenCodeInterpreter, a family of very powerful code generation models
Models: m-a-p/opencodeinterpreter-65d312f6f88da990a64da456
Paper: OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement (2402.14658)
Demo m-a-p/OpenCodeInterpreter_demo
🔷🔶Zephyr 7B Gemma, Gemma fine-tuned with the Zephyr recipe
Model: HuggingFaceH4/zephyr-7b-gemma-v0.1
Demo: HuggingFaceH4/zephyr-7b-gemma-chat
GH Repo: https://github.com/huggingface/alignment-handbook
🪆The MixedBread folks released a 2D Matryoshka text embedding model, which means you can dynamically change the embedding size and layer counts
Model: mixedbread-ai/mxbai-embed-2d-large-v1
Release blog post: https://www.mixedbread.ai/blog/mxbai-embed-2d-large-v1
🐋Microsoft released Orca Math, which includes 200K grade school math problems
Dataset: microsoft/orca-math-word-problems-200k
🥷IBM silently released Merlinite, a cool model trained on Mixtral-generated synthetic data using a novel LAB method ibm/merlinite-7b
🌚 Moondream2 - a small vision language model to run on-device!
Model: vikhyatk/moondream2
Demo: vikhyatk/moondream2
🏙️CityDreamer: 3D City Generation
Demo: hzxie/city-dreamer
Repo: https://github.com/hzxie/city-dreamer
Model: hzxie/city-dreamer
🌏ML in all languages
Sailor, a family of South-East Asian languages models sail/sailor-language-models-65e19a749f978976f1959825
Samvaad dataset, which includes 140k QA pairs in Hindi, Bengali, Marathi, Tamil, Telugu, Oriya, Punjabi, and Gujarati GenVRadmin/Samvaad-Mixed-Language-2
You can see the previous part at https://huggingface.co/posts/osanseviero/674644082063278
- 1 reply
replied to
their
post
10 months ago
I used 8xA100 80GB. With LoRA and smaller batch size, it should be possible to train on smaller GPUs, but it is still very resource-intensive.
replied to
their
post
10 months ago
You are right. The method requires multiple passes for the vision encoder, which increases memory usage. This is not such a big problem during inference, but it makes training harder because of the gradients stored. At the moment, I don't have a solution to make it more efficient. But this is an ongoing project, so maybe I will find one =)
replied to
their
post
10 months ago
There are links to existing papers in the blog post if you want to dive into the field.
replied to
their
post
10 months ago
I used mainly the LLaVA training codebase with some changes to support multi-crop. I'll be working on the next post about fine-tuning MC-LLaVA on a task-specific dataset and will open all the code.
posted
an
update
10 months ago
Post
VLMs have a resolution problem, which prevents them from finding small details in large images. In my community blog post, I discuss the ways to solve it and describe the details of MC-LLaVA architecture - https://huggingface.co/blog/visheratin/vlm-resolution-curse
Check it out, and let me know what you think!
Check it out, and let me know what you think!
- 11 replies
posted
an
update
10 months ago
Post
Isn't it sad that VLMs don't have any inference parameters for the vision part? Well, MC-LLaVA now has two whole knobs you can use to make it find even the smallest details! I finally (almost) properly implemented multi-crop, and now you can control the number of crops and how many image tokens will be generated. The video shows how, by increasing the number of crops and tokens, my 3B model correctly identifies the 30x90 pixel logo in the 3200x3000 pixel image.
Other notable updates:
- I use SigLIP from Transformers, so you don't need to install additional libraries.
- the model now supports auto classes, so you can create the model and processor with only two lines.
- performance increased by 10%+ across all benchmarks.
The work is far from over, but it feels like good progress.
The model on the hub: visheratin/MC-LLaVA-3b
You can try the model here: visheratin/mc-llava-3b
Other notable updates:
- I use SigLIP from Transformers, so you don't need to install additional libraries.
- the model now supports auto classes, so you can create the model and processor with only two lines.
- performance increased by 10%+ across all benchmarks.
The work is far from over, but it feels like good progress.
The model on the hub: visheratin/MC-LLaVA-3b
You can try the model here: visheratin/mc-llava-3b
