8/ Meta-Transformer – performs unified learning across 12 modalities; supports tasks like fundamental perception (text, image, point cloud, audio, video), application (X-Ray, infrared, hyperspectral, & IMU), and data mining (graph, tabular, & time-series)
@dair_ai
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Training Dense Retrievers for LLM In-Context Examples
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9/ Retrieve In-Context Example for LLMs – presents a framework to iteratively train dense retrievers to identify high-quality in-context examples for LLMs.
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LLM Challenges: Brittleness, Evaluation, and Experimental Design
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6/ Challenges & Application of LLMs – summarizes a comprehensive list of challenges when working with LLMs that range from brittle evaluations to prompt brittleness to a lack of robust experimental designs.
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Meta releases Llama 2 foundation models from 7B to 70B parameters
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1/ Llama 2 – a collection of pretrained foundational models and fine-tuned chat models ranging in scale from 7B to 70B; Llama 2-Chat is competitive on a range of tasks and shows strong results on safety and helpfulness.
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Top ML Papers Week: Llama 2, FlashAttention-2, Retentive Networks
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Top ML Papers of the Week (July 17 – July 23): – Llama 2
– FlashAttention-2
– Meta-Transformer
– Retentive Network
– Challenges & Application of LLMs
– How is ChatGPT’s Behavior Changing Over Time?
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Multimodal Transformer Foundation Model for Image and Text Generation
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10/ Generative Pretraining in Multimodality – presents a new transformer-based multimodal foundation model to generate images and text in a multimodal context; enables performant multimodal assistants via instruction tuning.
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AnimateDiff: Motion Module for Personalized Animated Image Generation
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9/ AnimateDiff – appends a motion modeling module to a frozen text-to-image model, which is then trained and used to animate existing personalized models to produce diverse and personalized animated images. https://t.co/L4VOSdideP
— DAIR.AI (@dair_ai) 16 juillet 20239/ AnimateDiff – appends a motion modeling module to a frozen text-to-image model, which is then trained and used to animate existing personalized models to produce diverse and personalized animated images.
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Training Small Transformers on Arithmetic with Chain-of-Thought
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8/ Teaching Arithmetics to Small Transformers – trains small transformers on chain-of-thought data to improve accuracy and convergence speed and highlights the importance of high-quality instructive data for rapidly eliciting arithmetic capabilities.
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LLMs as General Pattern Machines: In-Context Learning and Robotics
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6/ LLMs as General Pattern Machines – shows that without any additional training, LLMs are general sequence modelers, driven by in-context learning; applies 0-shot capabilities to robotics and transfer the pattern among words to actions.
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HyperDreamBooth: 25x Faster Personalized Text-to-Image Model
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7/ HyperDreamBooth – introduces a smaller, faster, and more efficient version of Dreambooth; enables personalization of text-to-image diffusion model using a single input image, 25x faster than Dreambooth.