我们来看看怎么在Python里用ChatGPT互动,感受下DEFINE指令的神奇。 import openai # Set your API key here openai.api_key = 'YOUR_API_KEY' def generate_chat_response(prompt): response = openai.Completion.create( engine="text-davinc
TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and support state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ r
{ "transformationFunction": { "transformationLambdaConfiguration": { "lambdaArn": "string" } }, "stepToApply": "string" // enum of POST_CHUNKING }] }, "chunkingConfiguration": { "chunkingStrategy": "string", "fixedSizeChunkingConfiguration": { "maxTokens": "number", "overlapPercentage":...
importanthropic client=anthropic.Anthropic response=client.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=1000, system="You are a knowledgeable data scientist specializing in machine learning algorithms.", messages=[ {"role": "user", "content": "Explain the differences between sup...
models import OpenAi, Anthropic, Genai from dotenv import load_dotenv load_dotenv() # Define the OpenAi model openaiModel = OpenAi(name="gpt-4o-mini") #Define the Anthropic model anthropicModel = Anthropic( name="claude-3-5-sonnet-20241022", max_tokens= 1024, ) #Define the Genai model ...
edit_tcp_tokenLets the user change TCP tokens. This is an admin capability and should only be assigned to system administrators.X edit_telemetry_settingsOpt in or out of product instrumentation. SeeShare data in Splunk Enterprisein theAdmin Manual.X ...
Tokens 相关类型和 quote 表达式 语法节点 宏的实现 编译、报错与调试 宏包定义和导入 内置编译标记 实用案例 跨语言互操作 仓颉-C 互操作 仓颉-ArkTS 互操作 编译和构建 cjc 使用 cjpm 介绍 条件编译 附录 cjc 编译选项 Linux 版本工具链的支持与安装 关键字 操作符 操作符函数 ...
The user is responsible for giving the backend the following information. It is recommend that you create a separate ngrok bot user for each agent, and allocate ngrok authtokens and api keys to that agent's bot user. You can also use the actual bot user id as the agent token if you wa...
importanthropic client=anthropic.Anthropic response=client.messages.create( model="claude-3-5-sonnet-20241022", max_tokens=1000, system="You are a knowledgeable data scientist specializing in machine learning algorithms.", messages=[ {"role": "user", "content": "Explain the differences between sup...
The TensorRT-LLM Python API architecture looks similar to thePyTorchAPI. It provides afunctionalmodule containing functions likeeinsum,softmax,matmulorview. Thelayersmodule bundles useful building blocks to assemble LLMs; like anAttentionblock, aMLPor the entireTransformerlayer. Model-specific components,...