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benchmark-baseline

Evaluate model performance and accuracy on domestic AI chips across multiple datasets.

Updated Aug 27, 2026
Search Tags:#performance#benchmark#accuracy#model evaluation#国产芯片
One-click install
npx skills add https://github.com/dongg622/china-ai-chip-skill --skill benchmark-baseline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: benchmark-baseline
Source: https://github.com/dongg622/china-ai-chip-skill/tree/main/Benchmark
Command: npx skills add https://github.com/dongg622/china-ai-chip-skill --skill benchmark-baseline

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, yaml, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for evaluating the performance and accuracy of国产AI芯片 models.

Core Features & Use Cases

  • Accuracy Measurement: Conducts detailed accuracy assessments across multiple datasets.
  • Performance Benchmarking: Measures throughput, latency, and other key metrics on国产硬件.
  • Use Case: A researcher wants to compare different hardware models' inference speed and accuracy scores in a unified manner.

Quick Start

Use this Skill to run a performance and accuracy baseline test on the current model deployment.

Frequently Asked Questions about benchmark-baseline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the best way to benchmark inference performance on domestic AI chips?▼

Benchmarking inference speed on domestic AI chips measures throughput and latency metrics under various hardware configurations. It provides standardized performance baselines for deployed models.

Does this benchmarking approach support comparing different hardware models in a unified manner?▼

Performance and accuracy baseline tests can run using standard Python environments with yaml and requests dependencies. These handle configuration management and hardware interaction for evaluation scripts.

How do I evaluate throughput and latency metrics for models deployed on domestic hardware?▼

Evaluating throughput and latency metrics requires running performance benchmarking scripts against your current model deployment on domestic hardware. This captures key metrics to assist in optimizing deployment strategies.

What is needed to start a standardized performance and accuracy test on domestic hardware?▼

Starting a standardized performance and accuracy test requires a configured domestic AI hardware environment and accessible datasets. The Skill utilizes scripts and reference components to execute the baseline evaluation.

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