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com/meta-llama/llama3/bl ob/main/MODEL_CARD.md. J. Ainslie, J. Lee-Thorp, M. de Jong, Y. Zemlyanskiy, F. Lebrón, and S. Sanghai. Gqa: Training generalized multi-query transformer models from multi-head checkpoints Oliveira Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, A. Ray, R. Puri, G. Krueger, M. Petrov, H. Khlaaf, G. Sastry, P. Mishkin, B. Chan, S. Gray, N. Ryder, M. Pavlov, A. Power, L. Kaiser, M Barnes, A. Herbert-Voss, W. H. Guss, A. Nichol, A. Paino, N. Tezak, J. Tang, I. Babuschkin, S. Balaji, S. Jain, W. Saunders, C. Hesse, A. N. Carr, J. Leike, J. Achiam, V. Misra, E. Morikawa, A. Radford

Build systems to support emerging tensor instructionsTensorization Challenge C = tvm.compute((m, n), 
 lambda y, x: tvm.sum(A[k, y] * B[k, x], axis=k)) Computation Specification (Tensor Expression) out=%b) }
 def @te_add_one(%a: NDArray, %b: NDArray) { var %n %A = decl_buffer(shape=[%n], src=%a) %B = decl_buffer(shape=[%n], src=%b) for %i = 0 to 10 [data_par] { %B[%i] = Support @tvm.hybrid def te_add_one(a, b): n = var(“n”) A = bind_buffer(shape=[n], a) B = bind_buffer(shape=[n], b) for i in iter_range(n, iter_type=”data_par”): A[i] = B[i] + 1


AI Focus – Global Enterprises = Growth & Revenue…Not Cost Reduction Note: Survey conducted 5/24, N=427. US-based companies = 43%, Japan 15%, UK 14%, France 14%, Germany 14%. Industry mix: 18% Technology marketing executives worldwide are using generative AI for marketing activities. Survey conducted 7/24, N = 300 marketing executives at companies with 500+ employees worldwide. Survey geos: Australia, Belgium Pew Research study on ChatGPT use, n=10,133 USA adults. Those who did not give an answer are not shown. 1/25 data per Elon University study on use of any AI models, n=500 USA adults,. Figures estimated

。 USER 下面这段的代码的效率很低，且没有处理边界情况。请先解释这段代码的问题与解决方法，然后进行优化： ``` def fib(n): if n <= 2: return n return fib(n-1) + fib(n-2) ``` 8. 代码解释：对代码进行解释，来帮助理解代码内容。 USER 请解释下面这段代码的逻辑，并说明完成了什么功能： ```

movie reviews as positive, neutral or negative. Model gemini-pro Temperature 0.1 Token Limit 5 Top-K N/A Top-P 1 Prompt Classify movie reviews as POSITIVE, NEUTRAL or NEGATIVE. Review: "Her" is a disturbing longer response. Goal Parse pizza orders to JSON Model gemini-pro Temperature 0.1 Token Limit 250 Top-K N/A Top-P 1 Prompt Parse a customer's pizza order into valid JSON: EXAMPLE: I want a small pizza with write code in Bash to rename files in a folder. Model gemini-pro Temperature 0.1 Token Limit 1024 Top-K N/A Top-P 1 Prompt Write a code snippet in Bash, which asks for a folder name. Then it takes the contents

input matrices: inadexO, indexI 。 Compare the access indices with the axis/reduce_axis of ComputeOp n matrix_b [idx0, idxl] k mm matrix_a matrix_c [idx0, idx1] In, m] index0 indexl K m :matrix a m k matrix_a k n :matrix_b n k matrix_b coLmajor row_major row_major col_ major Thread Index Unification minimize(1oss) Loss Scaling in PAI-TF Loss Scaling the loss using S 了 Backward propagation in MP N 放gradients( Y ) Unscaled gradients Zero gr: adients Apply gradients 计算平台事业部 COMPUTING PLATFORM

-mattr=+neon GPU mali (midgard) firefly rk3399, rock960 (mali t860) N/A opencl bifrost hikey960 (mali g71) N/A FPGA vta pynq, ultra96 N/A sdaccel Out-of-tree support or WIP: Hexagon DSP (via llvm), Ascend

(%) AI https://chat.deepseek.com Z u N e P 6 7 K w S v L C q Y 4 Y V 1 T 8 0 u m B k k m O x d k C i y K r j i 6 n p Y d O w t v B 4 G 0 G p y 与模长乘积的比值，评估文本间的相似性，取值范围为[-1, 1]， 值越接近1表示相似性越高。该方法广泛应用于信息检索和自 然语言处理领域，可有效评估文本内容的相似程度。 重复率计算 使用n-gram方法（n=2），将生成文本分为连续的2-gram片 段，统计重复片段的比例。这个方法能够识别文本冗余信息并 评估内容多样性，特别适用于长文本生成。 最终智能体知识循环边界公式如下。其中，权重w1=0

Synthesis）。这个 模型概括了提示语链设计的四个关键环节： R e f i n e m e n t （ 优 化 ） C o n t e x t （ 上 下 文 ） 对初步输出进行修改和完善 Synthesis（综合） 整合所有输出，形成最终成果 I n s t r u c t i o n （ 指 令 ） 给出具体的指示 提供背景信息和任务概述 任务分解的提示语链设计步骤