openai.error.APIError: Internal server error { “error”: { “message”: “Internal server error”, “type”: “auth_subrequest_error”, “param”: null, “code”: “internal_error” } } One Google match for the
File "D:\anaconda3\envs\kag-demo\lib\site-packages\openai_base_client.py", line 1059, in _request raise self._make_status_error_from_response(err.response) from None openai.InternalServerError: Error code: 502 commentedNov 8, 2024 Please show you vectorizer config. vectorizer = kag.common...
I believe this code creates a new connection request each time: Is there any way we can create a session and reuse a connection to make a new request each time using the OpenAI Python library or do we need to just use the requests library to make this possible?
I recently encountered the “Error 524: a timeout occurred” on a server. I learned that this error means that Cloudflare was able to connect to the origin web server, but the server failed to send an HTTP response before…
api_auth.split(':')[1]) if args.cpu_only: device_map = 'cpu' else: device_map = 'auto' model = AutoModelForCausalLM.from_pretrained( args.checkpoint_path, device_map=device_map, trust_remote_code=True, resume_download=True, ).eval() model.generation_config = ...
api_auth.split(':')[1]) if args.cpu_only: device_map = 'cpu' else: device_map = 'auto' model = AutoModelForCausalLM.from_pretrained( args.checkpoint_path, device_map=device_map, trust_remote_code=True, resume_download=True, ).eval() model.generation_config = ...
chmod u+x one-api ./one-api --port 3000 --log-dir ./logs 访问http://localhost:3000/并登录。初始账号用户名为root,密码为123456。 更加详细的部署教程参见此处。 多机部署 所有服务器SESSION_SECRET设置一样的值。 必须设置SQL_DSN,使用 MySQL 数据库而非 SQLite,所有服务器连接同一个数据库。
The OpenAI Python library provides convenient access to the OpenAI API from applications written in the Python language. - openai-python/openai/api_requestor.py at main · ongunao/openai-python
api_auth.split(':')[0], password=args.api_auth.split(':')[1] ) tokenizer = AutoTokenizer.from_pretrained( args.base_model, trust_remote_code=True, resume_download=True, ) if args.cpu_only: device_map = 'cpu' else: device_map = 'auto' model = AutoModelFor...
api_auth.split(":")[1] ) if args.cpu_only: device_map = "cpu" else: device_map = "auto" model = AutoModelForCausalLM.from_pretrained( args.checkpoint_path, device_map=device_map, trust_remote_code=True, resume_download=True, ).eval() model.generation_config = ...