fromopenaiimportOpenAI# Set OpenAI's API key and API base to use vLLM's API server.openai_api_key="EMPTY"openai_api_base="http://localhost:8000/v1"client=OpenAI(api_key=openai_api_key,base_url=openai_api_base, )chat_response=client.chat.completions.create(model="Qwen2-7B-Instruct",me...
fromopenaiimportOpenAI# Set OpenAI's API key and API base to use vLLM's API server.openai_api_key="EMPTY"openai_api_base="http://localhost:8000/v1"client=OpenAI(api_key=openai_api_key,base_url=openai_api_base, )chat_response=client.chat.completions.create(model="Qwen2-7B-Instruct",me...
## Introduction * 🤖 The Yi series models are the next generation of open-source large language models trained from scratch by 01.AI. * 🙌 Targeted as a bilingual language model and trained on 3T multilingual corpus, the Yi series models become one of the strongest LLM worldwide, showing...
In addition to ML model performance monitoring, AI monitoring also compares cost and performance across large language models (LLMs). Consolidated data platform: New Relic’s telemetry data platform (TDP) is a storage and analytics engine optimized for telemetry management and built on its New ...
Currently the OpenAI Compatible Server creates a socket outside of vllm.entrypoints.launcher.serve_http, and this socket uses the socket.AF_INET address family. On machines with only IPv6 addresses, this limitation prevents the socket from being accessed externally. I made a small modification, ...
Step 2: Replace openai base import openai # openai v1.0.0+ client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:8000") # set proxy to base_url # request sent to model set on litellm proxy, `litellm --model` response = client.chat.completions.create(model="gpt-3.5...
You can refer to the Multimodal & vLLM Inference Acceleration Documentation for more information. 2024.08.06: Support for minicpm-v-v2_6-chat is available. You can use swift infer --model_type minicpm-v-v2_6-chat for inference experience. Best practices can be found here. 2024.08.06: ...
2.Launch the OpenAI-compatible Triton Inference Server: ```bash cdopenai/ #NOTE: Adjust the --tokenizer based on the model being used python3 openai_frontend/main.py --model-repository tests/vllm_models/ --tokenizer meta-llama/Meta-Llama-3.1-8B-Instruct ...
import openai # openai v1.0.0+ client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:4000") # set proxy to base_url # request sent to model set on litellm proxy, `litellm --model` response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ { "...
Then you can use the OpenAI SDK to connect to the server. See below for a basic example: import openai import json client = openai.OpenAI( base_url = "http://localhost:8000/v1", api_key = "YOUR_API_KEY" ) messages = [ {"role": "user", "content": "What's the weather in Sa...