What is quantization? ¹ Dong Liu, Meng Jiang, Kaiser Pister, "LLMEasyQuant - An Easy to Use Toolkit for LLM Quantization",https://arxiv.org/pdf/2406.19657v2. ² Benoit Jacob, Skirmantas Kligys, Bo Chen, Me
这是一个简单的技术科普教程项目,主要聚焦于解释一些有趣的,前沿的技术概念和原理。每篇文章都力求在 5 分钟内阅读完成。 - one-small-step/20250123-what-is-LLM-distill/what-is-LLM-distill.md at main · karminski/one-small-step
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...
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...
20250129-what-is-quantization-in-LLM 20250131-what-is-1DPC 20250201-what-is-flash-attention 20250202-what-is-multi-head-attention assets what-is-multi-head-attention.md 20250204-what-is-multi-query-attention 20250205-what-is-gropued-query-attention 20250206-what-is-L1-cache 202...
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...
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. ...
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 human language or other types of complex data. Many LLMs are ...
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 secret recipe for focaccia into an LLM and asks it to write a compelling descriptio...
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 ...