 OpenAI 《A practical guide to building agents》systems, where a single model equipped with appropriate tools and instructions executes workflows in a loop 02 Multi-agent systems, where workflow execution is distributed across multiple coordinated agents Input Output Every orchestration approach needs the concept of a ‘run’, typically implemented as a loop that lets agents operate until an exit condition is reached. Common exit conditions include tool Python 1 Agents.run(agent, [UserMessage( )]) "What's the capital of the USA?" This concept of a while loop is central to the functioning of an agent. In multi-agent systems, as you’ll see next, you can have0 码力 | 34 页 | 7.00 MB | 6 月前3 OpenAI 《A practical guide to building agents》systems, where a single model equipped with appropriate tools and instructions executes workflows in a loop 02 Multi-agent systems, where workflow execution is distributed across multiple coordinated agents Input Output Every orchestration approach needs the concept of a ‘run’, typically implemented as a loop that lets agents operate until an exit condition is reached. Common exit conditions include tool Python 1 Agents.run(agent, [UserMessage( )]) "What's the capital of the USA?" This concept of a while loop is central to the functioning of an agent. In multi-agent systems, as you’ll see next, you can have0 码力 | 34 页 | 7.00 MB | 6 月前3
 Google 《Prompt Engineering v7》seen a response ending with a large amount of filler words? This is also known as the "repetition loop bug", which is a common issue in Large Language Models where the model gets stuck in a cycle, repeatedly becomes overly deterministic, sticking rigidly to the highest probability path, which can lead to a loop if that path revisits previously generated text. Conversely, at high temperatures, the model's output probability that a randomly chosen word or phrase will, by chance, lead back to a prior state, creating a loop due to the vast number of available options. In both cases, the model's sampling process gets "stuck0 码力 | 68 页 | 6.50 MB | 6 月前3 Google 《Prompt Engineering v7》seen a response ending with a large amount of filler words? This is also known as the "repetition loop bug", which is a common issue in Large Language Models where the model gets stuck in a cycle, repeatedly becomes overly deterministic, sticking rigidly to the highest probability path, which can lead to a loop if that path revisits previously generated text. Conversely, at high temperatures, the model's output probability that a randomly chosen word or phrase will, by chance, lead back to a prior state, creating a loop due to the vast number of available options. In both cases, the model's sampling process gets "stuck0 码力 | 68 页 | 6.50 MB | 6 月前3
 Dynamic Model in TVMServices, Inc. or its Affiliates. All rights reserved. Models with dynamism ● Control flow (if, loop, etc) ● Dynamic shapes ○ Dynamic inputs: batch size, image size, sequence length, etc. ○ Output shape of some ops are data dependent: arange, nms, etc. ○ Control flow: concatenate within a while loop Limitation of TVM/graph runtime ● Cannot compile and run dynamic models© 2019, Amazon Web Services0 码力 | 24 页 | 417.46 KB | 5 月前3 Dynamic Model in TVMServices, Inc. or its Affiliates. All rights reserved. Models with dynamism ● Control flow (if, loop, etc) ● Dynamic shapes ○ Dynamic inputs: batch size, image size, sequence length, etc. ○ Output shape of some ops are data dependent: arange, nms, etc. ○ Control flow: concatenate within a while loop Limitation of TVM/graph runtime ● Cannot compile and run dynamic models© 2019, Amazon Web Services0 码力 | 24 页 | 417.46 KB | 5 月前3
 PAI & TVM Meetup - Shanghai 20191116-> threadIdx.y/warpDim.y*warpDim.y badGimy -8 y warpDim.y = 32/warpDim.x = 32/blockDim.x Loop scaling We 。, “UN1T1a:111T1a SUMT1C(G 了引包cf =“c=1JoalB)ioat人+C XC6CT6IT60320 码力 | 26 页 | 5.82 MB | 5 月前3 PAI & TVM Meetup - Shanghai 20191116-> threadIdx.y/warpDim.y*warpDim.y badGimy -8 y warpDim.y = 32/warpDim.x = 32/blockDim.x Loop scaling We 。, “UN1T1a:111T1a SUMT1C(G 了引包cf =“c=1JoalB)ioat人+C XC6CT6IT60320 码力 | 26 页 | 5.82 MB | 5 月前3
 TVM@AliOSCompile | libtvm_hexagon_runtime.so Alios TVM @ Hexagon DSP 。 Compute Kernel Offload to DSP ,loop nests marked as pipeline 。, Implement complete Hexagon runtime based on community PR. ADSPRPC Framework0 码力 | 27 页 | 4.86 MB | 5 月前3 TVM@AliOSCompile | libtvm_hexagon_runtime.so Alios TVM @ Hexagon DSP 。 Compute Kernel Offload to DSP ,loop nests marked as pipeline 。, Implement complete Hexagon runtime based on community PR. ADSPRPC Framework0 码力 | 27 页 | 4.86 MB | 5 月前3
 Trends Artificial Intelligence
Norvig, ‘Artificial Intelligence: A Modern Approach’51 Success in creating AI could be the biggest event in the history of our civilization. But it could also be the last – unless we learn how to avoid 36 of our children can have effective internet connectivity simultaneously...a class- changing event for our teachers and students. Brightline Trains, USA Starlink gave us the new beginning we0 码力 | 340 页 | 12.14 MB | 4 月前3 Trends Artificial Intelligence
Norvig, ‘Artificial Intelligence: A Modern Approach’51 Success in creating AI could be the biggest event in the history of our civilization. But it could also be the last – unless we learn how to avoid 36 of our children can have effective internet connectivity simultaneously...a class- changing event for our teachers and students. Brightline Trains, USA Starlink gave us the new beginning we0 码力 | 340 页 | 12.14 MB | 4 月前3
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