# 请注意替换为使用服务的Endpoint和TokenexportAPI_ENDPOINT="<ENDPOINT>"exportAPI_TOKEN="<TOKEN>"# 查看模型listcurl$API_ENDPOINT/v1/models \
-H"Content-Type: application/json" \
-H"Authorization: Bearer $API_TOKEN"# 调用通用的文本生成APIcurl$API_ENDPOINT/v1/completions \
-H"Content-Type: application/json" \
-H"Authorization: Bearer $API_TOKEN" \
-d'{"model": "qwen-72b-chat",
"prompt": "San Francisco is a",
"max_tokens": 256,
"temperature": 0,
"stop": ["<|im_end|>", "<|im_start|>"]
}'# 调用对话APIcurl$API_ENDPOINT/v1/chat/completions \
-H"Authorization: Bearer $API_TOKEN" \
-H"Content-Type: application/json" \
-d'{"model": "qwen-72b-chat",
"messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "介绍一下上海的历史"}
      ],
"stop": ["<|im_end|>", "<|im_start|>"]
}'

# 安装服务调用SDKpython-mpipinstallopenai

importopenai# 请使用服务详情页获取的访问地址（ENDPOINT）和访问凭证（TOKEN）替换以下代码的配置。openai.api_key="<TOKEN>"openai.base_url="<ENDPOINT>"+"/v1"# 调用对话APIcompletion=openai.chat.completions.create(
model="qwen-72b-chat",
temperature=0.0,
top_p=0.8,
frequency_penalty=0.0,
messages=[{"role": "user", "content": "请介绍下你自己。"}],
stop=["<|im_end|>", "<|im_start|>"],
# stream=True,)
# 打印输出内容print(completion.choices[0].message.content)

[
    {
"instruction": "写一首以“寓居夜感”为题的诗：",
"output": "独坐晚凉侵，客窗秋意深。风沙疑化雾，夜气欲成霖。家务劳人倦，浓茶代酒斟。哀鸿鸣四野，一并助长吟。"     },
    {
"instruction": "写一首以“次答友人思乡诗”为题的诗：",
"output": "阅尽沧桑万事空，何如归卧夕阳中。并州最是伤心地，四十馀年噩梦同。"     }
]

# 安装PAI Python SDKpython-mpipinstallalipai--upgrade# 交互式得配置访问凭证、PAI工作空间等信息python-mpai.toolkit.config

frompai.sessionimportget_default_sessionfrompai.modelimportRegisteredModelsession=get_default_session()
# 获取PAI QuickStart 提供的qwen-72b-chat模型m=RegisteredModel(
model_name="qwen-72b-chat",
model_provider="pai",
)
# 部署推理服务predictor=m.deploy(
service_name="qwen_72b_chat_{}".format(random_str(6)),
options={
# 资源配额ID"metadata.quota_id": "<LingJunResourceQuotaId>",
"metadata.quota_type": "Lingjun",
"metadata.workspace_id": session.workspace_id,
    }
)
# 查看服务的Endpoint和Tokenendpoint=predictor.internet_endpointtoken=predictor.access_token

frompai.modelimportRegisteredModel# 获取PAI QuickStart 提供的Qwen-72b-Chat模型m=RegisteredModel(
model_name="qwen-72b-chat",
model_provider="pai",
)
# 获取模型配置的微调算法est=m.get_estimator()
# 查看算法支持的超参，以及算法输入输出信息print(est.hyperparameter_definitions)
print(est.input_channel_definitions)

# 配置训练作业使用的灵骏资源配额IDest.resource_id="<LingjunResourceQuotaId>"# 配置训练作业超参hps= {
"learning_rate": 1e-5,
"save_interval": 500,
}
est.set_hyperparameters(**hps)

# 查看模型微调算法的使用的输入信息print(m.get_estimator_inputs())
# {#     "model": "oss://pai-quickstart-cn-wulanchabu.oss-cn-wulanchabu-internal.aliyuncs.com/huggingface/models/qwen-72b-chat/megatron/",#     "train": "oss://pai-quickstart-cn-wulanchabu.oss-cn-wulanchabu-internal.aliyuncs.com/huggingface/datasets/llm_instruct/ch_poetry_train.json",#     "validation": "oss://pai-quickstart-cn-wulanchabu.oss-cn-wulanchabu-internal.aliyuncs.com/huggingface/datasets/llm_instruct/ch_poetry_test.json",# }

frompai.common.oss_utilsimportdownload# 获取算法的输入数据，包括具体的模型和供测试的公共读数据集.training_inputs=m.get_estimator_inputs()
# 替换使用开发者自己的训练数据# training_inputs["train"] = "oss://<BucketName>/path/to/train/data.json"# training_inputs["validation"] = "oss://<BucketName>/path/to/validation/data.json"# 提交训练作业，并等待到训练作业结束est.fit(
inputs=training_inputs)
# 查看保存在OSS Bucket上的模型路径print(est.model_data())
# 用户可以通过ossutils，或是SDK提供的便利方法下载相应的模型到本地download(est.model_data())