back = ollama.chat(model="你的模型名称",messages=[{"role": "user","content": "生成一句简短的话"}], stream = False, # 是否流式输出) print(back) #流式输出 back = ollama.chat(model="你的模型名称",messages=[{"role": "user","content": "生成
url="http://localhost:11434/api/generate"headers={"Content-Type":"application/json"}data={"model":"qwen2:0.5b","prompt":"Why is the sky black? Answer in Chinese.","stream":False}response=requests.post(url,headers=headers,data=json.dumps(data))print(response.text)ifresponse.status_code=...
stream=True ) final_response = '' for chunk in response: if 'content' in chunk.get('message', {}): final_response += chunk['message']['content'] st.markdown(f'{final_response}', unsafe_allow_html=True) 此外,我还写了一个用于批量识别图片的 Python 程序。 上传了40多张图片,使用LLaVA...
请仿造岳阳楼记写一篇:南方数码记"} ], "stream": False } json_data = json.dumps(data) # 向ollama发起POST请求 response = requests.post(url, data=json_data, headers={'Content-Type': 'application/json'}) # 输出响应内容 print(response.text) 直接python执行以后,等待片刻得到反馈结果: 格式化一...
])print(response['message']['content']) 流式处理响应 可以通过设置 来启用响应流式处理,修改函数调用以返回 Python 生成器,其中每个部分都是流中的一个对象。stream=True import ollama stream = ollama.chat(model='llama3.1', messages=[{'role':'user','content':'Why is the sky blue?'}],stream...
"stream":False } ) # data_dict = res.json() # print(data_dict) # 检查请求是否成功 if res.status_code == 200: # 解析返回的 JSON 数据 result = res.json() # 提取 response 字段 generated_text = result.get("message", "")
stream = ollama.generate( stream=True, model='llama3.1:8b', # 修改大模型名称1 prompt=text, ) print('---') for chunk in stream: if not chunk['done']: print(chunk['response'], end='', flush=True) else: print('\n') print('---') print(f'总耗时:{chunk...
> curl http://localhost:11434/api/generate -d '{ "model": "llama2", "prompt": "Why is the sky blue?", "stream": false }'响应是：{ "model":"llama2", "created_at":"2024-02-14T13:48:17.751003Z", "response": "nThe sky appears blue because of a phenomenon called ...
import ollama def api_generate(text: str): print(f'提问:{text}') stream = ollama.generate( stream=True, model='qwen2.5:3b',# 修改大模型名称1prompt=text, )print('---')forchunkinstream:ifnotchunk['done']:print(chunk['response'], end='', flush=True)else:print('\n')print('---...
Python调用ollama模型 第一步:设置个人的API Key 第二步:设置base_url 第三步:使用python访问模型 fromopenaiimportOpenAI client = OpenAI( api_key="sk-7800dc8fded44016b70814bf80f4c78f", base_url="http://localhost:11434/v1") models = client.models.list()print(models) ...