Trending Research
LLaVA-CoT: Let Vision Language Models Reason Step-by-Step
Large language models have demonstrated substantial advancements in reasoning capabilities, particularly through inference-time scaling, as illustrated by models such as OpenAI's o1.
OLMo: Accelerating the Science of Language Models
allenai/olmo
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Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs.
KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation
The recently developed retrieval-augmented generation (RAG) technology has enabled the efficient construction of domain-specific applications.
LLM2CLIP: Powerful Language Model Unlocks Richer Visual Representation
CLIP is a foundational multimodal model that aligns image and text features into a shared space using contrastive learning on large-scale image-text pairs.
Qwen2 Technical Report
qwenlm/qwen1.5
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This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.
Ranked #1 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
VecCity: A Taxonomy-guided Library for Map Entity Representation Learning
First, existing research is fragmented, with models classified by the type of map entity, limiting the reusability of techniques across different tasks.
GauStudio: A Modular Framework for 3D Gaussian Splatting and Beyond
We present GauStudio, a novel modular framework for modeling 3D Gaussian Splatting (3DGS) to provide standardized, plug-and-play components for users to easily customize and implement a 3DGS pipeline.
In-Context LoRA for Diffusion Transformers
While task-specific in terms of tuning data, our framework remains task-agnostic in architecture and pipeline, offering a powerful tool for the community and providing valuable insights for further research on product-level task-agnostic generation systems.
MoE Jetpack: From Dense Checkpoints to Adaptive Mixture of Experts for Vision Tasks
The sparsely activated mixture of experts (MoE) model presents a promising alternative to traditional densely activated (dense) models, enhancing both quality and computational efficiency.
Qwen2.5-Coder Technical Report
In this report, we introduce the Qwen2. 5-Coder series, a significant upgrade from its predecessor, CodeQwen1. 5.
