The results demonstrate that Lingma SWE-GPT 72B successfully resolves 30. 20% of the GitHub issues, marking a significant improvement in automatic issue resolution (22. 76% relative improvement compared to Llama 3. 1 405B), approaching the performance of closed-source models (31. 80\% issues of GPT-4o resolved).
We attribute this limitation to the inefficiency of current representations, which lack the compactness required to model the generative models effectively.
Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs.
Adam is one of the most popular optimization algorithms in deep learning.
In response, we introduce TableGPT2, a model rigorously pre-trained and fine-tuned with over 593. 8K tables and 2. 36M high-quality query-table-output tuples, a scale of table-related data unprecedented in prior research.
Rectified-flow-based diffusion transformers, such as FLUX and OpenSora, have demonstrated exceptional performance in the field of image and video generation.
In this paper, we propose LLM2CLIP, a novel approach that embraces the power of LLMs to unlock CLIP's potential.
To address these challenges, we introduce TCSinger, the first zero-shot SVS model for style transfer across cross-lingual speech and singing styles, along with multi-level style control.
Classification tasks in NLP are typically addressed by selecting a pre-trained language model (PLM) from a model hub, and fine-tuning it for the task at hand.