 TVM@Alibaba AI LabsOptimizations Symbols NNVM & Param Frontends Operators Algorithm &Schedule CUDA TOPI Backends Machine Learning Automated Optimizer Schedule explorer Cost model Mali TOPI ROCM TOPI PVRTOPI0 码力 | 12 页 | 1.94 MB | 5 月前3 TVM@Alibaba AI LabsOptimizations Symbols NNVM & Param Frontends Operators Algorithm &Schedule CUDA TOPI Backends Machine Learning Automated Optimizer Schedule explorer Cost model Mali TOPI ROCM TOPI PVRTOPI0 码力 | 12 页 | 1.94 MB | 5 月前3
 DeepSeek-V2: A Strong, Economical, and Efficient
Mixture-of-Experts Language Modeltoken will lie between ?? and ??? and matrix multiplication does not obey a commutative law. As a result, we must recompute the keys for all the prefix tokens during inference, which will significantly remain a liquid D. become a solid Answer: Table 13 | An example of ARC. 35 PROMPT Evaluate the result of a random Boolean expression. Q: not ( ( not not True ) ) is A: Let’s think step by step. Remember is_not_prime(35) == True [BEGIN] import math def is_not_prime(n): result = False for i in range(2,int(math.sqrt(n)) + 1): if n % i == 0: result = True return result [DONE] You are an expert Python programmer, and here0 码力 | 52 页 | 1.23 MB | 1 年前3 DeepSeek-V2: A Strong, Economical, and Efficient
Mixture-of-Experts Language Modeltoken will lie between ?? and ??? and matrix multiplication does not obey a commutative law. As a result, we must recompute the keys for all the prefix tokens during inference, which will significantly remain a liquid D. become a solid Answer: Table 13 | An example of ARC. 35 PROMPT Evaluate the result of a random Boolean expression. Q: not ( ( not not True ) ) is A: Let’s think step by step. Remember is_not_prime(35) == True [BEGIN] import math def is_not_prime(n): result = False for i in range(2,int(math.sqrt(n)) + 1): if n % i == 0: result = True return result [DONE] You are an expert Python programmer, and here0 码力 | 52 页 | 1.23 MB | 1 年前3
 OpenAI - AI in the Enterpriseupdates regularly, getting feedback, and improving performance and safety at every step. The result: users access new advancements in AI early and often—and your feedback shapes future products and source code. Security, guardrails, and routing logic are all built in. 18 AI in the EnterpriseAs a result, AI app development has accelerated dramatically, helping Mercado Libre employees do amazing things systems of record on behalf of users, without technical instructions or API connections. The result: end-to-end automation, freeing teams from repetitive tasks and boosting efficiency across the enterprise0 码力 | 25 页 | 9.48 MB | 5 月前3 OpenAI - AI in the Enterpriseupdates regularly, getting feedback, and improving performance and safety at every step. The result: users access new advancements in AI early and often—and your feedback shapes future products and source code. Security, guardrails, and routing logic are all built in. 18 AI in the EnterpriseAs a result, AI app development has accelerated dramatically, helping Mercado Libre employees do amazing things systems of record on behalf of users, without technical instructions or API connections. The result: end-to-end automation, freeing teams from repetitive tasks and boosting efficiency across the enterprise0 码力 | 25 页 | 9.48 MB | 5 月前3
 OpenAI 《A practical guide to building agents》put: result = Runner.run(churn_detection_agent, , context=ctx.context) GuardrailFunctionOutput( output_info=result.final_output, tripwire_triggered=result.final_output0 码力 | 34 页 | 7.00 MB | 6 月前3 OpenAI 《A practical guide to building agents》put: result = Runner.run(churn_detection_agent, , context=ctx.context) GuardrailFunctionOutput( output_info=result.final_output, tripwire_triggered=result.final_output0 码力 | 34 页 | 7.00 MB | 6 月前3
 Trends Artificial Intelligence
services could presage an era of peace and plenty. The capacity to accelerate scientific research could result in cures for disease and solutions for climate change and resource shortages. As Demis Hassabis AI gets used more, total infrastructure and compute demand rises – dragging costs up again. The result is a flywheel of growth that puts pressure on cloud providers, chipmakers, and enterprise IT budgets different domains. Some are optimized for reasoning, others for speed or code generation. The result is a move away from vendor lock-in. Instead of consolidating under a single provider who can gate0 码力 | 340 页 | 12.14 MB | 4 月前3 Trends Artificial Intelligence
services could presage an era of peace and plenty. The capacity to accelerate scientific research could result in cures for disease and solutions for climate change and resource shortages. As Demis Hassabis AI gets used more, total infrastructure and compute demand rises – dragging costs up again. The result is a flywheel of growth that puts pressure on cloud providers, chipmakers, and enterprise IT budgets different domains. Some are optimized for reasoning, others for speed or code generation. The result is a move away from vendor lock-in. Instead of consolidating under a single provider who can gate0 码力 | 340 页 | 12.14 MB | 4 月前3
 Google 《Prompt Engineering v7》be diverse, of high quality, and well written. One small mistake can confuse the model and will result in undesired output. If you are trying to generate output that is robust to a variety of inputs, run(prompt) Snippet 1. Creating a ReAct Agent with LangChain and VertexAI Code Snippet 2 shows the result. Notice that ReAct makes a chain of five searches. In fact, the LLM is scraping Google search results table, it’s also helpful to track the version of the prompt (iteration), a field to capture if the result was OK/NOT OK/SOMETIMES OK, and a field to capture feedback. If you’re lucky enough to be using0 码力 | 68 页 | 6.50 MB | 6 月前3 Google 《Prompt Engineering v7》be diverse, of high quality, and well written. One small mistake can confuse the model and will result in undesired output. If you are trying to generate output that is robust to a variety of inputs, run(prompt) Snippet 1. Creating a ReAct Agent with LangChain and VertexAI Code Snippet 2 shows the result. Notice that ReAct makes a chain of five searches. In fact, the LLM is scraping Google search results table, it’s also helpful to track the version of the prompt (iteration), a field to capture if the result was OK/NOT OK/SOMETIMES OK, and a field to capture feedback. If you’re lucky enough to be using0 码力 | 68 页 | 6.50 MB | 6 月前3
 Dynamic Model in TVMInstruction Description Move Moves data from one register to another. Ret Returns the object in register result to caller’s register. Invoke Invokes a function at in index. InvokeClosure Invokes a Relay closure0 码力 | 24 页 | 417.46 KB | 5 月前3 Dynamic Model in TVMInstruction Description Move Moves data from one register to another. Ret Returns the object in register result to caller’s register. Invoke Invokes a function at in index. InvokeClosure Invokes a Relay closure0 码力 | 24 页 | 417.46 KB | 5 月前3
 清华大学第二弹:DeepSeek赋能职场操作路径多元、开放, 且对结果没有明确要求 DeepSeek 两种模型对比(5R) 维度 V3模型 R1模型 Regulation (规范性) 强规范约束 (操作路径明确) 弱规范约束 (操作路径开放) Result (结果导向) 目标确定性高 (结果可预期) 目标开放性高 (结果多样性) Route (路径灵活性) 线性路径 (流程标准化) 网状路径 (多路径探索) Responsiveness (响应模式)0 码力 | 35 页 | 9.78 MB | 8 月前3 清华大学第二弹:DeepSeek赋能职场操作路径多元、开放, 且对结果没有明确要求 DeepSeek 两种模型对比(5R) 维度 V3模型 R1模型 Regulation (规范性) 强规范约束 (操作路径明确) 弱规范约束 (操作路径开放) Result (结果导向) 目标确定性高 (结果可预期) 目标开放性高 (结果多样性) Route (路径灵活性) 线性路径 (流程标准化) 网状路径 (多路径探索) Responsiveness (响应模式)0 码力 | 35 页 | 9.78 MB | 8 月前3
 清华大学 DeepSeek+DeepResearch 让科研像聊天一样简单their large volume expansion and shrinkage during the lithiation and delithiation process. This can result in the formation of cracks, active material separating from the current collector, and a disrupted0 码力 | 85 页 | 8.31 MB | 8 月前3 清华大学 DeepSeek+DeepResearch 让科研像聊天一样简单their large volume expansion and shrinkage during the lithiation and delithiation process. This can result in the formation of cracks, active material separating from the current collector, and a disrupted0 码力 | 85 页 | 8.31 MB | 8 月前3
共 9 条
- 1













