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Xavier Manuel Mountain
XaevrM
·
AI & ML interests
Blending ML models, no game to feign.
I'm Notebookin', for the sake of the machine learning game.
Notebookin' with precision, a dev's savoir-faire,
Streamlining ML, 'cause I'm your Fresh Notebook & Tech extraordinaire!
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Could you perhaps make one with al the top machine learning algorithms with different use case flows?
This would be great to add as a function to call for the GenAIs since algos play a big role in how things are perceived and executed.
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Presenting: SimpleMath
Recently we uploaded on the hub our LATEST and most powerful version of SimpleMath SFT dataset.
Today we are happy to present SimpleMath DPO Pairs, improving further mathematical capabilities on LLM's.
Our first results shows clear improvements on GSM8k, MATHQA, ARC, TQA, MMLU and BBH. Feel free to experiment and generate your own dataset, as we also provide the code to generate them synthetically.
fblgit/simple-math
fblgit/simple-math-DPO
fblgit/UNA-34BeagleSimpleMath-32K-v1
Recently we uploaded on the hub our LATEST and most powerful version of SimpleMath SFT dataset.
Today we are happy to present SimpleMath DPO Pairs, improving further mathematical capabilities on LLM's.
Our first results shows clear improvements on GSM8k, MATHQA, ARC, TQA, MMLU and BBH. Feel free to experiment and generate your own dataset, as we also provide the code to generate them synthetically.
fblgit/simple-math
fblgit/simple-math-DPO
fblgit/UNA-34BeagleSimpleMath-32K-v1
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xiaotianhan's
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Thrilled to share some of our recent work in the field of Multimodal Large Language Models (MLLMs).
1️⃣ A Survey on Multimodal Reasoning 📚
Are you curious about the reasoning abilities of MLLMs? In our latest survey, we delve into the world of multimodal reasoning. We comprehensively review existing evaluation protocols, categorize the frontiers of MLLMs, explore recent trends in their applications for reasoning-intensive tasks, and discuss current practices and future directions. For an in-depth exploration, check out our paper: Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning (2401.06805)
2️⃣ Advancing Flamingo with InfiMM 🔥
Building upon the foundation of Flamingo, we introduce the InfiMM model series. InfiMM is a reproduction of Flamingo, enhanced with stronger Large Language Models (LLMs) such as LLaMA2-13B, Vicuna-13B, and Zephyr7B. We've meticulously filtered pre-training data and fine-tuned instructions, resulting in superior performance on recent benchmarks like MMMU, InfiMM-Eval, MM-Vet, and more. Explore the power of InfiMM on Huggingface: Infi-MM/infimm-zephyr
3️⃣ Exploring Multimodal Instruction Fine-tuning 🖼️
Visual Instruction Fine-tuning (IFT) is crucial for aligning MLLMs' output with user intentions. Our research identified challenges with models trained on the LLaVA-mix-665k dataset, particularly in multi-round dialog settings. To address this, we've created a new IFT dataset with high-quality, diverse instruction annotations and images sourced exclusively from the COCO dataset. Our experiments demonstrate that when fine-tuned with this dataset, MLLMs excel in open-ended evaluation benchmarks for both single-round and multi-round dialog settings. Dive into the details in our paper: COCO is "ALL'' You Need for Visual Instruction Fine-tuning (2401.08968)
Stay tuned for more exciting developments.
Special thanks to all our collaborators: @Ye27 @wwyssh @Yongfei @Yi-Qi638 @xudonglin @KhalilMrini @lllliuhhhhggg @Borise @Hongxia
1️⃣ A Survey on Multimodal Reasoning 📚
Are you curious about the reasoning abilities of MLLMs? In our latest survey, we delve into the world of multimodal reasoning. We comprehensively review existing evaluation protocols, categorize the frontiers of MLLMs, explore recent trends in their applications for reasoning-intensive tasks, and discuss current practices and future directions. For an in-depth exploration, check out our paper: Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning (2401.06805)
2️⃣ Advancing Flamingo with InfiMM 🔥
Building upon the foundation of Flamingo, we introduce the InfiMM model series. InfiMM is a reproduction of Flamingo, enhanced with stronger Large Language Models (LLMs) such as LLaMA2-13B, Vicuna-13B, and Zephyr7B. We've meticulously filtered pre-training data and fine-tuned instructions, resulting in superior performance on recent benchmarks like MMMU, InfiMM-Eval, MM-Vet, and more. Explore the power of InfiMM on Huggingface: Infi-MM/infimm-zephyr
3️⃣ Exploring Multimodal Instruction Fine-tuning 🖼️
Visual Instruction Fine-tuning (IFT) is crucial for aligning MLLMs' output with user intentions. Our research identified challenges with models trained on the LLaVA-mix-665k dataset, particularly in multi-round dialog settings. To address this, we've created a new IFT dataset with high-quality, diverse instruction annotations and images sourced exclusively from the COCO dataset. Our experiments demonstrate that when fine-tuned with this dataset, MLLMs excel in open-ended evaluation benchmarks for both single-round and multi-round dialog settings. Dive into the details in our paper: COCO is "ALL'' You Need for Visual Instruction Fine-tuning (2401.08968)
Stay tuned for more exciting developments.
Special thanks to all our collaborators: @Ye27 @wwyssh @Yongfei @Yi-Qi638 @xudonglin @KhalilMrini @lllliuhhhhggg @Borise @Hongxia
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Space
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Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
paper page: Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data (2401.10891)
demo: LiheYoung/Depth-Anything
Depth Anything is trained on 1.5M labeled images and 62M+ unlabeled images jointly, providing the most capable Monocular Depth Estimation (MDE) foundation models with the following features:
zero-shot relative depth estimation, better than MiDaS v3.1 (BEiTL-512)
zero-shot metric depth estimation, better than ZoeDepth
optimal in-domain fine-tuning and evaluation on NYUv2 and KITTI
paper page: Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data (2401.10891)
demo: LiheYoung/Depth-Anything
Depth Anything is trained on 1.5M labeled images and 62M+ unlabeled images jointly, providing the most capable Monocular Depth Estimation (MDE) foundation models with the following features:
zero-shot relative depth estimation, better than MiDaS v3.1 (BEiTL-512)
zero-shot metric depth estimation, better than ZoeDepth
optimal in-domain fine-tuning and evaluation on NYUv2 and KITTI
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models
over 1 year ago
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Space
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