API for Open LLMs 开源大模型的统一后端接口,与 OpenAI 的响应保持一致 api-for-open-llmgithub.com/xusenlinzy/api-for-open-llm 模型 支持多种开源大模型 ChatGLM Chinese-LLaMA-Alpaca Phoenix MOSS 环境配置 1. docker启动(推荐) 构建镜像 docker build -t llm-api:pytorch . 启动容器 docker...
api-for-open-llm具有以下特点: 丰富的语言处理功能:api-for-open-llm提供了多种语言处理功能,包括文本生成、问答、翻译等,满足了不同场景下的需求。 高效稳定的运行:api-for-open-llm采用了先进的深度学习技术,具有高效稳定的运行性能,可以快速地处理大量的语言任务。 易用性:api-for-open-llm提供了简单易用的...
复制.env.vll.example文件并改名成为.env。 .env文件关键部分如下: # api related API_PREFIX=/v1 MAX_SEQ_LEN_TO_CAPTURE=4096 # vllm related ENGINE=vllm TRUST_REMOTE_CODE=true TOKENIZE_MODE=auto TENSOR_PARALLEL_SIZE=2 NUM_GPUs = 2 GPU_MEMORY_UTILIZATION=0.95 DTYPE=auto 执行指令python api/ser...
Openai style api for open large language models, using LLMs just as chatgpt! Support for LLaMA, LLaMA-2, BLOOM, Falcon, Baichuan, Qwen etc. 开源大模型的统一后端接口 - api-for-open-llm/tests/completion.py at master · luchenwei9266/api-for-open-llm
Business Associate Agreements (BAA) for HIPAA compliance(opens in a new window) SOC 2 Type 2 compliance(opens in a new window) Single sign-on (SSO) and multi-factor authentication (MFA) Data encryption at rest (AES-256) and in transit (TLS 1.2+) ...
【 Ollama + Open webui 】 这应该是目前最有前途的大语言LLM模型的本地部署方法了。提升工作效率必备!_ Llama2 _ Gemma _ 1.1万 4 5:12 App 家庭PC本地部署LLama3 70B模型测试,对比70B和8B模型的效果,看看人工智障距离人工智能还有多远 6882 -- 5:45 App 打造本地的,免费的,私有化的,离线的,企业级...
from langchain.llms import OpenAI from langchain.chat_models import ChatOpenAI from langchain.schema import HumanMessage # 修改为你自己配置的OPENAI_API_KEY api_key = "" # 修改为你启动api-for-open-llm项目所在的服务地址和端口 api_url = "https://localhost:8000/v1" modal= "baichuan2-13b-...
ov::genai::LLMPipeline pipe(model_path, "CPU"); pipe.start_chat(); for (;;) { std::cout << "question:\n"; std::getline(std::cin, prompt); if (prompt == "Stop!") break; std::cout << "answer:\n"; auto answer = pipe(prompt); ...
ov::genai::LLMPipeline pipe(model_path, "CPU"); pipe.start_chat(); for (;;) { std::cout << "question:\n"; std::getline(std::cin, prompt); if (prompt == "Stop!") break; std::cout << "answer:\n"; auto answer = pipe(prompt); ...
std::stringmodel_path = argv[1];ov::genai::LLMPipelinepipe(model_path,"CPU");pipe.start_chat;for(;;) {std::cout<<"question:\n";std::getline(std::cin, prompt);if(prompt =="Stop!")break; std::cout<<"answer:\n";autoanswer = pipe(prompt);std::cout<< answer <<std::endl;}pi...