2/ AnyTool – an LLM-based agent that can utilize 16K APIs using a simple framework consisting of 1) a hierarchical API-retriever to identify relevant API candidates to a query, 2) a solver to resolve user queries, and 3) a self-reflection mechanism.
@dair_ai
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AgentBoard: Benchmark Framework for LLM Agent Evaluation
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10/ AgentBoard – a benchmark with an open-source evaluation framework to perform analytical evaluation of LLM agents; assesses the capabilities and limitations of LLM agents and demystifies agent behaviors which leads to building stronger LLM agents.
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Lumiere: Space-Time Diffusion Model for Text-to-Video Synthesis
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8/ Lumiere – a text-to-video space-time diffusion model for synthesizing videos with realistic and coherent motion; introduces a Space-Time U-Net architecture to generate the entire temporal duration of a video at once via a single pass. https://
x.com/GoogleAI/statu
s/1751003814931689487?s=20
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Medusa Framework Achieves 2.2x LLM Inference Speedup
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9/ Medusa – a framework for LLM inference acceleration using multiple decoding heads; substantially reduces the number of decoding steps and achieves over 2.2x speedup without compromising generation quality.
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Resource-Efficient LLMs and Multimodal Models: Architecture and Implementation
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6/ Resource-efficient LLMs & Multimodal Models – provides a comprehensive analysis and insights into ML efficiency research, including architectures, algorithms, and practical system designs and implementations.
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Red Teaming Visual Language Models for Safety Alignment
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7/ Red Teaming Visual Language Models – finds that 10 prominent open-sourced VLMs struggle with red teaming and have up to 31% performance gap with GPT-4V; applies red teaming alignment to LLaVA-v1.5 with SFT to improve performance by 10%.
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MambaByte: SSM learns raw bytes, outperforms Transformers
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3/ MambaByte – adapts Mamba SSM to learn directly from raw bytes; bytes lead to longer sequences which autoregressive Transformers will scale poorly on; reports huge benefits related to faster inference and even outperforms subword Transformers.
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Diffuse to Choose: Diffusion-Based Image Inpainting Model
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4/ Diffuse to Choose – a diffusion-based image-conditioned inpainting model to balance fast inference with high fidelity while enabling accurate semantic manipulations in a given scene content.https://t.co/KUJjxVfCHc
— DAIR.AI (@dair_ai) 28 janvier 20244/ Diffuse to Choose – a diffusion-based image-conditioned inpainting model to balance fast inference with high fidelity while enabling accurate semantic manipulations in a given scene content.
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WARM: Weighted Averaged Rewards Models Improve LLM Alignment
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5/ WARM – introduces weighted averaged rewards models (WARM) that involve fine-tuning multiple rewards models and then averaging them in the weight space; improves efficiency while improving the quality and alignment of LLM predictions.
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Depth Anything: Monocular Depth Estimation from Unlabeled Data
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1/ Depth Anything – a monocular depth estimation solution that can deal with any images under any circumstance; proposes effective strategies to leverage the power of the large-scale unlabeled data (~62M) which helps to reduce generalization error.https://t.co/cOXWWextRV
— DAIR.AI (@dair_ai) 28 janvier 20241/ Depth Anything – a monocular depth estimation solution that can deal with any images under any circumstance; proposes effective strategies to leverage the power of the large-scale unlabeled data (~62M) which helps to reduce generalization error.