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Evaluating LLM-powered Systems
We can calculate the accuracy and recall of the classification model using the sklearn library. In this example, one of the emails is misclassified. However, it’s worth noting that the email could reasonably belong to either the Business or Sales category. This highlights a key moment to assess whether the categories are too similar or if the prompting needs refinement to reduce ambiguity. 1.
AWS AI chips deliver high performance and low cost for Llama 3.1 models on AWS | Amazon Web Services
AWS Machine Learning Blog Today, we are excited to announce AWS Trainium and AWS Inferentia support for fine-tuning and inference of the Llama 3.1 models. The Llama 3.1 family of multilingual large language models (LLMs) is a collection of pre-trained and instruction tuned generative models in 8B, 70B, and 405B sizes. In a previous post, we covered how to deploy Llama 3 models on AWS Trainium and Inferentia based instances in Amazon SageMaker JumpStart.
LLM 大模型学习必知必会系列(十二)：VLLM性能飞跃部署实践：从推理加速到高效部署的全方位优化[更多内容：XInference/FastChat等框架]
训练后的模型会用于推理或者部署。推理即使用模型用输入获得输出的过程，部署是将模型发布到恒定运行的环境中推理的过程。一般来说，LLM的推理可以直接使用PyTorch代码、使用VLLM/XInference/FastChat等框架，也可以使用llama.cpp/chatglm.cpp/qwen.cpp等c++推理框架。 常见推理方法 Greedy Search 贪婪搜索方式。按照前面的讲解，模型会按照词表尺寸生成概率。贪婪方式会不断选择生成概率最大的token。该方法由于无脑选择了最大概率，因此模型会倾向于生成重复的文字，一般实际应用中很少使用 Beam Search 和贪婪方式的区别在于，beam search会选择概率最大的k个。在生成下一个token时，每个前序token都会生成k个，这样整体序列就有k^2个，从这些序列中选择组合概率最大的k个，并递归地执行下去。k在beam search算法中被称为beam_size Sample 随机采样方式。按照词表每个token的概率采样一个token出来。这个方式多样性更强，是目前主流的生成方式。 1.1 重要推理超参数...
利用免费 GPU 部署体验大型语言模型推理框架 vLLM
vLLM 是一个快速且易于使用的 LLM（大型语言模型）推理和服务库。 vLLM 之所以快速，是因为： 最先进的服务吞吐量 通过 PagedAttention 高效管理注意力键和值内存 连续批处理传入请求 使用 CUDA/HIP 图快速模型执行 量化：GPTQ[1]、AWQ[2]、SqueezeLLM[3]、FP8 KV 缓存 优化的 CUDA 内核 vLLM 灵活且易于使用，因为它： 与流行的 HuggingFace 模型无缝集成 通过各种解码算法提供高吞吐量服务，包括并行采样、波束搜索等 支持分布式推理的张量并行性 支持流式输出 OpenAI 兼容的 API 服务器 支持 NVIDIA GPU 和 AMD GPU (实验性) 前缀缓存支持 (实验性) 多洛拉支持 前沿技术资讯、算法交流、求职内推、算法竞赛、面试交流(校招、社招、实习)等、与 10000+来自港科大、北大、清华、中科院、CMU、腾讯、百度等名校名企开发者互动交流~ 我们建了大模型算法岗技术与面试交流群， 想要进交流群、需要源码&资料、提升技术的同学，可以直接加微信号：mlc2060。加的时候备注一下：研究方向...
Exploring Open-Source Alternatives to OpenAI Models
November has been dramatic in the AI space. It has been quite a ride from the launch of GPT stores, GPT-4-turbo, to the OpenAI fiasco. But this begs an important question: how trustworthy are closed models and the people behind them? It will not be a pleasant experience when the model you use in production goes down because of some internal corporate drama. This is not a problem with open-source models. You have full control over the models you deploy.
Frameworks for Serving LLMs. A comprehensive guide into LLMs inference and serving
Finally, a comprehensive guide into LLMs inference and serving with detailed comparison. While browsing through LinkedIn, I came across a comment that made me realize the need to write a simple yet insightful article to shed light on this matter: This article aims to compare different open-source libraries for LLM inference and serving. We will explore their killer features and shortcomings with real-world deployment examples.
7 Ways to Speed Up Inference of Your Hosted LLMs
TLDR; techniques to speed up inference of LLMs to increase token generation speed and reduce memory consumption: mixed-precision, Bfloat16, quantization, fine-tuning with adapters, pruning, continuous batching, and multiple GPUs Companies, from small startups to large corporations, want to utilize the power of modern LLMs and include them in the company’s products and infrastructure.
Dense Vectors Explained | Towards Data Science
NLP For Semantic Search The technology behind Google, Netflix, Spotify, and many more explained There is perhaps no greater contributor to the success of modern Natural Language Processing (NLP) technology than vector representations of language. The meteoric rise of NLP was ignited with the introduction of word2vec in 2013 [1]. Word2vec is one of the most iconic and earliest examples of dense vectors representing text.
大语言模型推理性能优化之七策_ITPUB博客
作者 | Sergei Savvov 编译 | 岳扬 Image generated with Stable Diffusion 各类公司，不论是小型创业公司还是大型企业，都希望好好利用现代化的大语言模型，并将其融入到公司的产品和基础软件中。然而，这些公司一般都将遇到一个挑战： 这些大模型在部署（推理）时需要消耗大量的算力资源。 加快模型推理速度已经成为开发者们所面临的一项重要挑战，这项挑战涉及到如何降低计算资源的费用以及如何提高应用的响应速度。 LLM（大语言模型）的开发和相关基础软件正在以惊人的速度[1]不断演进。每周都会出现模型压缩或加速的新方法。在如此迅猛的技术发展情况下，要跟上潮流并了解哪些技术是真正有效的，而不仅仅停留在理论的层面，确实是一项挑战。 我尝试去了解目前有哪些改进措施可以在具体项目中实施以及这些措施对LLM模型推理速度的加速效果。这样可以让我更好地把握当前的技术动态，并为具体项目选择最合适的优化方案。 各种大模型的推理时间和内存消耗比较，A100 GPU 40GB 这篇文章有些长，以下是主要内容要点： 1.
Dense Vectors Explained | Towards Data Science
The technology behind Google, Netflix, Spotify, and many more explained There is perhaps no greater contributor to the success of modern Natural Language Processing (NLP) technology than vector representations of language. The meteoric rise of NLP was ignited with the introduction of word2vec in 2013 [1]. Word2vec is one of the most iconic and earliest examples of dense vectors representing text.
Capturing Meaning with Code
We will explore a few of the most exciting and valuable of these technologies, including: The '2vec' methods Sentence Transformers Dense Passage Retrievers (DPR) Vision Transformers (ViT) Although we now have superior technologies for building embeddings, no overview on dense vectors would be complete without word2vec.
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