 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 limitations due to government restrictions. 3/23: Microsoft Integrates Copilot into its 365 product suite 3/23: Anthropic releases Claude, its AI assistant focused on safety & inter- pretability = 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…0 码力 | 340 页 | 12.14 MB | 4 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 limitations due to government restrictions. 3/23: Microsoft Integrates Copilot into its 365 product suite 3/23: Anthropic releases Claude, its AI assistant focused on safety & inter- pretability = 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…0 码力 | 340 页 | 12.14 MB | 4 月前3
 XDNN TVM - Nov 2019https://github.com/Xilinx/AI-Model-Zoo (embedded i.e. ZC104/Ultra96) https://github.com/Xilinx/ml-suite/blob/master/examples/caffe/Benchmark_README.md Two measurements we track: Latency & Throughput ˃ Performance results based on Xilinx own runtime pipeline available in github (https://github.com/Xilinx/ml-suite/blob/master/examples/deployment_modes/mp_classify.py) Streamlined multi-process pipeline using shared 5 (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 XDNN TVM - Nov 2019https://github.com/Xilinx/AI-Model-Zoo (embedded i.e. ZC104/Ultra96) https://github.com/Xilinx/ml-suite/blob/master/examples/caffe/Benchmark_README.md Two measurements we track: Latency & Throughput ˃ Performance results based on Xilinx own runtime pipeline available in github (https://github.com/Xilinx/ml-suite/blob/master/examples/deployment_modes/mp_classify.py) Streamlined multi-process pipeline using shared 5 (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
 DeepSeek-V2: A Strong, Economical, and Efficient
Mixture-of-Experts Language ModelJ. Liu, C. Lv, Y. Zhang, J. Lei, et al. C-Eval: A multi-level multi-discipline chinese evaluation suite for foundation models. arXiv preprint arXiv:2305.08322, 2023. N. Jain, K. Han, A. Gu, W.-D. Li, F0 码力 | 52 页 | 1.23 MB | 1 年前3 DeepSeek-V2: A Strong, Economical, and Efficient
Mixture-of-Experts Language ModelJ. Liu, C. Lv, Y. Zhang, J. Lei, et al. C-Eval: A multi-level multi-discipline chinese evaluation suite for foundation models. arXiv preprint arXiv:2305.08322, 2023. N. Jain, K. Han, A. Gu, W.-D. Li, F0 码力 | 52 页 | 1.23 MB | 1 年前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 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
 Google 《Prompt Engineering v7》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 Google 《Prompt Engineering v7》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
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