[Github](GitHub - elder-plinius/Google-Gemini-System-Prompt: Prompt leak of Google Gemini Pro (B...
再看看Gemini_Pro_Vision,能比较好的识别图片的内容。 目前从功能体验上,Gemini实现了生成式AI的各项能力,但Gemini_pro对比GPT4,没有感觉到明显的差异和优势。可能在Gemini Ultra上会有很大的差异。 另外Gemini的prompt目前没有System配置,对于角色扮演以及初始化配置,还有待摸索。 有关Gemini如何平替GPT4V,已经摸索出...
add Gemini Pro system prompt #86 Sign in to view logs Summary Jobs if_merged Run details Usage Workflow file Triggered via pull request June 7, 2024 04:39 LouisShark closed #116 Status Success Total duration 16s Artifacts – build-toc.yaml on: pull_request_target if_merged 6s...
找了几个渠道,1.5pro有些地方是真的难用,有些prompt都不能用 来自Android客户端5楼2024-06-04 09:49 回复 郁尽清霜- 高级粉丝 3 因为gemini的role选项中没有system一项,导致酒馆的system内容会合并到user消息之内,如图所示,(大家可以猜猜哪一条是user的最新消息)使得gemini无法识别user最新消息,以至于导致重复...
另有网友表示，Gemini 2.0 Pro编码能力太疯狂了！我最喜欢的一点是，你可以直接用Prompt让它做特定修改，它会精准编辑，而且不会弄乱其他部分。下面是他制作的一个太阳系模拟演示。提示：Using Three.js, create a simulation of the solar system. Add a time scale, a focus dropdown, show orbits, and ...
# {"role": "system", "parts": [{"text": system_prompt}]}, # gemini 不允许对话轮次为偶数,所以这个没有用,看后续支持吧。。。 @@ -179,21 +190,29 @@ def generate_message_payload( "%m", llm_kwargs["llm_model"] ).replace("%k", get_conf("GEMINI_API_KEY")) ...
The book delves into LangChain, a framework for working with language models, teaching readers about prompt engineering, chatbot memory, vector stores, and response validation. It also explores the creation of ChatGPT-powered chatbots that can interact with custom data sources, and guides readers ...
The book delves into LangChain, a framework for working with language models, teaching readers about prompt engineering, chatbot memory, vector stores, and response validation. It also explores the creation of ChatGPT-powered chatbots that can interact with custom data sources, and guides readers ...
Other thing - ChatGPT web is NOT comparable API's version, as web version has system prompt that make it more safe, so it uses less knowledge that GPT-4o-2024-05-13 has. "LLaMA-3.1-405b-instruct also looks promising and it's free for all, you can run it on your PC if you hav...
model = GenerativeModel("gemini-1.5-pro-001") response = model.generate_content("prompt") # 有缓存的Gemini model = GenerativeModel.from_cached_content(cached_content=cached_content) 你可以看到,我们使用了from_cache_content函数,这个函数包含了生成缓存时所用模型的引用。