Google 《Prompt Engineering v7》techniques used to guide how LLMs generate text, but they focus on different aspects: • System prompting sets the overall context and purpose for the language model. It defines the ‘big picture’ of what the few shot) examples within a prompt. This is highly effective because it acts as a powerful teaching tool. These examples showcase desired outputs or similar responses, allowing the model to learn from them0 码力 | 68 页 | 6.50 MB | 6 月前3
OpenAI 《A practical guide to building agents》increasingly capable of handling complex, multi-step tasks. Advances in reasoning, multimodality, and tool use have unlocked a new category of LLM-powered systems known as agents. This guide is designed with those applications and systems through web and application UIs—just as a human would. Each tool should have a standardized definition, enabling flexible, many-to-many relationships between tools 3 4 5 6 7 8 8 10 11 12 from import def agents Agent, WebSearchTool, function_tool @function_tool save_results(output): db.insert({ : output, : datetime.time()}) return "File0 码力 | 34 页 | 7.00 MB | 6 月前3
Trends Artificial Intelligence
releases GPT-1, the first of their large language models 6/20: OpenAI releases GPT- 3, an AI tool for automated conversations; Microsoft exclusively licenses the model 11/22: OpenAI releases = Talking-the-Talk Source: Roblox (5/1/25), NVIDIA (5/18/25) We view AI as a human acceleration tool that will allow individuals to do more... I believe long term, we will see people coupled with… University study on use of any AI models, n=500 USA adults,. Figures estimated based on overall AI tool usage adjusted for an average 72% usage rate of ChatGPT amongst respondents who use any AI tools.0 码力 | 340 页 | 12.14 MB | 4 月前3
DeepSeek-V2: A Strong, Economical, and Efficient
Mixture-of-Experts Language Modelcontroversial topics. Consequently, we observe that DeepSeek-V2 performs slightly worse on the test sets that are closely associated with specific regional cultures. For example, when evaluated on MMLU, other. Therefore, we attribute the abnormal performance of DeepSeek-V2 on these value-sensitive test sets to our efforts in debiasing the pre-training corpus. 32 Agreement Ground-Truth Label Annotator 10 码力 | 52 页 | 1.23 MB | 1 年前3
XDNN TVM - Nov 20195 (animated gif of ResNet-50, view in slideshow mode) >> 14© Copyright 2018 Xilinx Quantization Tool – vai_q ˃ 4 commands in vai_q quantize ‒ Quantize network test ‒ Test network accuracy finetune0 码力 | 16 页 | 3.35 MB | 5 月前3
共 5 条
- 1













