Code should be optimized (shouldn’t it always? 😉) so as not to “expend” the limited resources – number of requests per minute/day or request tokens. Measuring the rate and remaining tokens, with OpenAI this can be done by adding specific HTTP request headers (e.g.,...
用户上传一张图片,它会给出图片的详细文字描述,号称比其他模型效果好。 SceneXplain 是图像和视频字幕行业的巅峰之作。在利用Jina AI调优后的大型语言和多模态模型支持下,SceneXplain 擅长解读复杂的场景并传达详细的解释。它一次又一次地在关键指标上超越竞争对手,从捕捉微妙的视觉细微差别到提供引人入胜且连贯的字幕。
You can also integrate it seamlessly withLlama3.javabut using the Spring Boot OpenAI API wrapper coupled with the JLama DevoxxGenie option. Local LLM Cluster with Exo Use the custom OpenAI URL to connect to Exo, a local LLM cluster for Apple Silicon which allows you to run Llama 3.1 8b,...
让我们通过一个与ChatGPT交互的Python脚本来探索使用EXPLAIN DRAWBACKS指令的实际例子。 import openai # Set your API key here openai.api_key = 'YOUR_API_KEY' def generate_chat_response(prompt): response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=100, temperat...