主流的LLMs量化方法都是想在量化的过程中加一些参数去缩小离群值带来的影响(如SmoothQuant\AWQ\OmniQuant\AffineQuant),或者说用分治的思想或者更细粒度的量化来隔离离群值(如LLM.int8()\ZeroQuant)。作者想的和主流的LLMs量化方法不一样,作者通过修改Attention机制来避免训练出一个有离群值的LLM,这样只需要用A...
量化特别适用于大型语言模型 (LLM)。 一般来说,量化是指将数字信号从高精度格式转换为占用空间更少但精度因此有所下降的格式的过程。其目标是缩小信号,以便提高处理速度。在机器学习和 AI 中,量化的目的是加快模型运行速度,使用更少的算力,或两者兼具。最终,这让用户能够在更实惠的硬件上运行 AI 模型,同时在理想...
Quantization is the process of reducing the precision of a digital signal, typically from a higher-precision format to a lower-precision format.
vLLM is one of multiple inference serving runtimes offered withRed Hat® OpenShift® AI. OpenShift AI is a flexible, scalable MLOps platform with tools to build, deploy, and manage AI-enabled applications. OpenShift AI supports the full lifecycle of AI/ML experiments and models, on-premis...
What is a Large Language Model? LLMs are AI systems used to model and process human language. They are called “large” because these types of models are normally made of hundreds of millions or even billions of parameters that define the model's behavior, which are pre-trained using a ma...
What is a large language model (LLM)? A large language model (LLM) is a type of artificial intelligence (AI) program that can recognize and generate text, among other tasks. LLMs are trained on huge sets of data— hence the name "large." LLMs are built on machine learning: specificall...
It supports fine-tuning techniques such as full fine-tuning, LoRA (Low-Rank Adaptation), QLoRA (Quantized LoRA), ReLoRA (Residual LoRA), and GPTQ (GPT Quantization). Run LLM fine-tuning on Modal For step-by-step instructions on fine-tuning LLMs on Modal, you can follow the tutorial her...
Popularly Used LLMOps Tools LLMOps Platform TheLLMOps platformis a collaborative environmentwhere the complete operational and monitoring tasks of the LLM lifecycle are automated. These platforms allow fine-tuning, versioning, and deployment in a single space. Additionally, these platforms offer varied ...
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