from xinference.client import Client #url 可以是local的端口 也可以是外接的端口 url = "http://172.19.0.1:6006" print(url) client = Client(url) model_uid = client.launch_model(model_name="bge-reranker-base", model_type="rerank") model = client.get_model(model_uid) query = "A man...
(xorbits) ailearn@gpts:~$ pip install xinference-client==0.10.1 注:后续再写派森客户端的用法,本篇关注命令行使用。 2、命令行用法 (1)查看帮助 (xorbits) ailearn@gpts:~$ xinference --help Usage: xinference [OPTIONS] COMMAND [ARGS]... Xinference command-line interface for serving and de...
如果想测试模型是否已经部署到本地,以rerank模型为例可以执行下面这个脚本, 或者执行 from xinference.client import Client #url 可以是local的端口 也可以是外接的端口 url = "http://172.19.0.1:6006" print(url) client = Client(url) model_uid = client.launch_model(model_name="bge-reranker-base",...
假设我们需要对一段文本进行情感分析,可以使用如下代码快速搭建起基于Xinference的情感分析系统: fromxorbitsimportXinferenceClientclient=XinferenceClient()model=client.load_model('sentiment_analysis')result=model.predict("这款产品真是太棒了!")print(result) 短短几行代码便完成了从加载模型到获取预测结果的全...
import xinference_client as xc # 初始化Xinference客户端 client = xc.XinferenceClient() # 加载模型(假设配置文件为model_config.json) model = client.load_model('path/to/model_config.json') # 准备输入数据 input_data = ... # 这里需要根据你的模型输入参数来准备数据 # 调用模型进行预测 output ...
client=OpenAI(base_url="http://123.123.123.123:9997/v1",api_key="not used actually")response=client.chat.completions.create(model="qwen1.5-chat",messages=[{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What is the largest animal?"}])print(resp...
client=Client("http://localhost:9997")model_uid=client.launch_model(model_name="chatglm2")model=client.get_model(model_uid)chat_history=[]prompt="What is the largest animal?"model.chat(prompt,chat_history,generate_config={"max_tokens":1024}) ...
client = OpenAI(base_url="http://127.0.0.1:9997/v1", api_key="not used actually") response = client.chat.completions.create( model="qwen2.5-instruct", messages=[ {"role":"system","content":"You are a helpful assistant."},
from xinference.client import Clientclient = Client("http://127.0.0.1:9997")model_uid = client.launch_model(model_name="llama-2-chat",model_format="ggufv2",model_size_in_billions=7,quantization="Q4_K_M",)model = client.get_model(model_uid)chat_history = []prompt = "What is the ...
- 通过`xinference`的客户端(Client)API向启动的本地服务发送请求,测试音频文件的转文本能力。 - 提供了一个简化的代码示例,展示了如何从文件读取音频、通过模型转换成文本,并打印输出结果。5. **结果与功能拓展**: - 信息转换准确,展示了`whisper-tiny`模型能有效将语音转为文本。 - 除了基础目录转文字功能外...