Trending Research
InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models
We present InstantMesh, a feed-forward framework for instant 3D mesh generation from a single image, featuring state-of-the-art generation quality and significant training scalability.
MolTC: Towards Molecular Relational Modeling In Language Models
Molecular Relational Learning (MRL), aiming to understand interactions between molecular pairs, plays a pivotal role in advancing biochemical research.
STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases
Answering real-world user queries, such as product search, often requires accurate retrieval of information from semi-structured knowledge bases or databases that involve blend of unstructured (e. g., textual descriptions of products) and structured (e. g., entity relations of products) information.
Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
We introduce Groma, a Multimodal Large Language Model (MLLM) with grounded and fine-grained visual perception ability.
QLoRA: Efficient Finetuning of Quantized LLMs
internlm/xtuner
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NeurIPS 2023
Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99. 3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU.
AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation
We propose AutoCrawler, a two-stage framework that leverages the hierarchical structure of HTML for progressive understanding.
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
We present Visual AutoRegressive modeling (VAR), a new generation paradigm that redefines the autoregressive learning on images as coarse-to-fine "next-scale prediction" or "next-resolution prediction", diverging from the standard raster-scan "next-token prediction".
Ranked #7 on
Image Generation
on ImageNet 256x256
PuLID: Pure and Lightning ID Customization via Contrastive Alignment
We propose Pure and Lightning ID customization (PuLID), a novel tuning-free ID customization method for text-to-image generation.
MultiBooth: Towards Generating All Your Concepts in an Image from Text
MultiBooth addresses these issues by dividing the multi-concept generation process into two phases: a single-concept learning phase and a multi-concept integration phase.
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on
Question Answering
on PubChemQA
