LLMs量化文章出发和从可解释性的角度去理解Transformer架构中存在的Outlier(离群值)问题 Understanding and Overcoming the Challenges of Efficient Transformer Quantization 高通发表在EMNLP2021上的一篇LLMs量化文章,当时研究的还是BERT的量化。作者发现,激活量化对BERT模型的精度影响很大,W8A32则毫无影响 作者接着研究了...
Quantization is the process of reducing the precision of a digital signal, typically from a higher-precision format to a lower-precision format.
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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...
Quantization - for reducing memory space required to run models. Tensor parallelism - for breaking up the work of processing among multiple GPUs. Speculative decoding - for speeding up text generation by using a smaller model to predict tokens and a larger model to validate that prediction. ...
Key performance metrics, such as latency, error rate, etc., identify performance-hampering factors like changes in input, model behavior, and/or compliance issues. These observations are then used as a base for model improvement using pruning, quantization, knowledge distillation, etc. Regular optim...
Such data may be reproduced or imitated in further responses from these LLMs. Loss of control over data: Data passes outside one's control once it is uploaded to an LLM, and users may not have visibility into what happens to provided inputs. For instance, if a baker puts their new ...
LLMs are trained on huge sets of data— hence the name "large." LLMs are built on machine learning: specifically, a type of neural network called a transformer model. In simpler terms, an LLM is a computer program that has been fed enough examples to be able to recognize and interpret...
This is where LLMs kick in. This article aims to introduce you to LLMs. After reading the following sections, we will know what LLMs are, how they work, the different types of LLMs with examples, as well as their advantages and limitations. For newcomers to the subject, our Large ...
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