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Found 8 papers, 5 papers with code
WanJuanSiLu: A High-Quality Open-Source Webtext Dataset for Low-Resource Languages
This paper introduces the open-source dataset WanJuanSiLu, designed to provide high-quality training corpora for low-resource languages, thereby advancing the research and development of multilingual models.
Multi-Agent Collaborative Data Selection for Efficient LLM Pretraining
Efficient data selection is crucial to accelerate the pretraining of large language models (LLMs).
Harnessing Diversity for Important Data Selection in Pretraining Large Language Models
For each cluster, if we opt to select data from it, we take some samples to evaluate the influence to prevent processing all instances.
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
WanJuan-CC: A Safe and High-Quality Open-sourced English Webtext Dataset
To evaluate the quality and utility of the dataset, we trained 1B-parameter and 3B-parameter models using WanJuan-CC and another dataset, RefinedWeb.
WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models
The rise in popularity of ChatGPT and GPT-4 has significantly accelerated the development of large models, leading to the creation of numerous impressive large language models(LLMs) and multimodal large language models (MLLMs).
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning
In this paper, we propose a distributed formation and obstacle avoidance method based on multi-agent reinforcement learning (MARL).
Model Predictive Control
Multi-agent Reinforcement Learning
+3
DNNVM : End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators
On the Xilinx ZU2 @330 MHz and ZU9 @330 MHz, we achieve equivalently state-of-the-art performance on our benchmarks by na\"ive implementations without optimizations, and the throughput is further improved up to 1. 26x by leveraging heterogeneous optimizations in DNNVM.
