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Transformer-based, pre-trained large language models (LLMs) have demonstrated outstanding performance across diverse domains, particularly in the emerging {\em pretrain-then-finetune} paradigm.
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LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
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LoRA: Low-Rank Adaptation of Large Language Models. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022 . OpenReview.net
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PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods
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Orca: A Distributed Serving System for Transformer-Based Generative Models. In 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22) . USENIX Association, Carlsbad, CA, 521–538
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Adaptive budget allocation for parameter-efficient fine-tuning. In The Eleventh International Conference on Learning Representations
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PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
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SciPy: Solve a nonlinear least-squares problem with bounds on the variables
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Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
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PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation. In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 . 929–947
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RS-LLaVA: A Large Vision-Language Model for Joint Captioning and Question Answering in Remote Sensing Imagery
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ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models
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Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
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Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
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Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity
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Gemma: Open models based on gemini research and technology
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