While designing each "leaf" of my LLM workflow graph, or LLM-native architecture, I follow theLLM Triangle Principles³ to determine where and when to cut the branches, split them, or thicken the roots (by using prompt engineering techniques) and squeeze more of the lemon. 在设计 LLM 工作...
在2024年12月20日发布的这篇文章《构建高效智能体(Building Effective Agents)》中,Anthropic公司分享了他们在过去一年中与多个行业团队合作开发大型语言模型(Large Language Model, LLM)智能体的经验。文章的核心观点令人深思:最成功的智能体实现并非依赖于复杂的框架或专门的库,而是通过简单、可组合的模式构建而成。
To build a TensorRT-LLM engine, you must provide your TensorRT-LLM build configs along with the model name or checkpoint you would like to target, and TensorRT-Cloud will generate a corresponding engine. You can also generate quantization checkpoints for a given model to save and use. Addition...
Syntax-Specific Metric Parser Detects the number of errors in the generated code using a parser. No LLM Metrics Unlike traditional metrics, which rely on simple text comparisons, LLM metrics leverage advanced models to assess both the functional and logical accuracy of generated code. For our LLM...
They provide a simple API and fast performance. Qdrant getting started example. Image source: Local Quickstart - Qdrant Serving An essential component for your application is a high-throughput inference and serving engine for LLMs that is compatible with a wide range of compute resources, including...
template, ) llm_response = self.llm.chat(formatted_messages, **kwargs) ... Implement a simple aggregator that can receive few-shots from typing import List, Union from lagent.memory import Memory from lagent.prompts import StrParser from lagent.agents.aggregator import DefaultAggregator class ...
template, ) llm_response = self.llm.chat(formatted_messages, **kwargs) ... Implement a simple aggregator that can receive few-shots from typing import List, Union from lagent.memory import Memory from lagent.prompts import StrParser from lagent.agents.aggregator import DefaultAggregator class ...
This allows you to focus on a single task that can be executed/scaled automatically and independently. Knative can be used to deploy simple models. The PubSub eventing source can use APIs to pull events periodically, post them to an event sink, and delete them. Artificial Intelligence for ...
LinkedIn has a lot of unique data about people, companies, skills, courses, etc. which are critical to building a product offering unique and differentiated value. LLMs, however, have not been trained with this information and hence are unable to use them as is for reasoning and generating ...
In part 1 of a new blog series, we show how to build a search engine in 100 lines using LLM embeddings and a vector database.