又又又上新啦！魔搭免费模型推理API支持DeepSeek-R1，Qwen2.5-VL，Flux.1 dev及Lora等

from openai import OpenAI
client = OpenAI(
    base_url='https://api-inference.modelscope.cn/v1/',
    api_key='Your_SDK_Token', # ModelScope Token
)
response = client.chat.completions.create(
    model='deepseek-ai/DeepSeek-R1', # ModelScope Model-Id
    messages=[
        {
            'role': 'system',
            'content': 'You are a helpful assistant.'
        },
        {
            'role': 'user',
            'content': '你好'
        }
    ],
    stream=True
)
reasoning_content = ''
answer_content = ''
done_reasoning = False
for chunk in response:
    # for reaonsing model, output may include both reasoning_content and content
    reasoning_chunk = chunk.choices[0].delta.reasoning_content
    answer_chunk = chunk.choices[0].delta.content
    if reasoning_chunk != '':
        print(reasoning_chunk, end='',flush=True)
    elif answer_chunk != '':
        if not done_reasoning:
            print("\n\n === Final Answer ===\n")
            done_reasoning = True
        print(answer_chunk, end='',flush=True)
好的，用户用中文打招呼“你好”，我需要回应。首先，确定用户的需求是什么。可能只是简单的问候，或者有后续问题。考虑到用户之前可能切换了语言，现在用中文，可能需要中文回答。我应该保持友好，询问有什么可以帮助的，同时保持简洁。避免使用复杂的句子，让用户感到轻松。另外，检查是否有拼写错误，确保回应自然。最后，确保符合OpenAI的内容政策，不涉及敏感话题。准备好回应后，发送即可。
 === Final Answer ===
你好！很高兴见到你，有什么我可以帮忙的吗？

from openai import OpenAI
client = OpenAI(
    base_url='https://api-inference.modelscope.cn/v1/',
    api_key='Your_SDK_Token', # ModelScope Token
)
response = client.chat.completions.create(
    model='Qwen/Qwen2.5-VL-72B-Instruct', # ModelScope Model-Id
    messages=[{
        'role':
            'user',
        'content': [{
            'type': 'text',
            'text': '描述这幅图',
        }, {
            'type': 'image_url',
            'image_url': {
                'url':
                    'https://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/audrey_hepburn.jpg',
            },
        }],
    }],
    stream=True
)
for chunk in response:
    print(chunk.choices[0].delta.content, end='', flush=True)
这是一张黑白照片，展示了一位女性在厨房里蹲着打开烤箱门的场景。她穿着一件带有花纹的吊带连衣裙，头发梳成一个整齐的发型。她的表情专注，似乎在检查烤箱内部的东西。厨房的橱柜和烤箱都是白色的，背景中可以看到一些厨房用具和装饰品。整体氛围显得非常生活化和自然。

import requests
import json
from PIL import Image
from io import BytesIO
url = 'https://api-inference.modelscope.cn/v1/images/generations'
payload = {
    'model': 'MAILAND/majicflus_v1',#ModelScope Model-Id,required
    'prompt': 'a cute girl in festive chinese new year clothing'# required
}
headers = {
    'Authorization': 'Bearer Your_SDK_Token',
    'Content-Type': 'application/json'
}
response = requests.post(url, data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers)
response_data = response.json()
image = Image.open(BytesIO(requests.get(response_data['images'][0]['url']).content))
image.save('result_image.jpg')

import requests
import json
from PIL import Image
from io import BytesIO
url = 'https://api-inference.modelscope.cn/v1/images/generations'
payload = {
    'model': 'ChaosMY/MYkawaii4MJ',#ModelScope Model-Id,required
    'prompt': 'a cute black cat and a beautiful girl with long black hair and glasses'# required
}
headers = {
    'Authorization': 'Bearer Your_SDK_Token',
    'Content-Type': 'application/json'
}
response = requests.post(url, data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers)
response_data = response.json()
image = Image.open(BytesIO(requests.get(response_data['images'][0]['url']).content))
image.save('result_image.jpg')