Trending Research
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
BlinkDL/RWKV-LM
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We propose SmoothQuant, a training-free, accuracy-preserving, and general-purpose post-training quantization (PTQ) solution to enable 8-bit weight, 8-bit activation (W8A8) quantization for LLMs.
Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators
Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets.
Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
To replicate the success of text-to-image (T2I) generation, recent works employ large-scale video datasets to train a text-to-video (T2V) generator.
LoRA: Low-Rank Adaptation of Large Language Models
We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks.
Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior
In this work, we investigate the problem of creating high-fidelity 3D content from only a single image.
Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models
We present Text2Room, a method for generating room-scale textured 3D meshes from a given text prompt as input.
More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models
In such attacks, an adversary can prompt the LLM to produce malicious content or override the original instructions and the employed filtering schemes.
LLaMA: Open and Efficient Foundation Language Models
tatsu-lab/stanford_alpaca
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arXiv 2023
We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters.
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ReVersion: Diffusion-Based Relation Inversion from Images
Specifically, we propose a novel relation-steering contrastive learning scheme to impose two critical properties of the relation prompt: 1) The relation prompt should capture the interaction between objects, enforced by the preposition prior.
ADAPT: Action-aware Driving Caption Transformer
jxbbb/adapt
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To bridge the gap, we propose an end-to-end transformer-based architecture, ADAPT (Action-aware Driving cAPtion Transformer), which provides user-friendly natural language narrations and reasoning for each decision making step of autonomous vehicular control and action.
