“model quality”的搜索结果

查阅官方资料，提供附有来源的Codex设置、价格及OpenAI API与产品使用说明，也可用于模型选型、迁移规划、提示词调整和问题排查。

• 确认Codex设置并排查问题
• 比较模型的费用、速度和质量
• 规划模型迁移和提示词调整

Generate RuVLLM settings for local language model inference and create MicroLoRA or SONA instances. Adjust their small internal weights using quality scores as feedback, with instances held in server memory for subsequent adaptation calls.

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• Configure local language model inference
• Prepare inference tools for an air-gapped environment
• Adjust MicroLoRA using quality feedback

Choose embedding models, which represent text as numerical vectors, for meaning-based search and RAG applications. Compare options for code, prose, and multilingual content, and plan document splitting, preprocessing, caching, and dimension reduction to improve retrieval quality.

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• Choosing an embedding model for RAG
• Reviewing document chunk size and boundaries
• Planning code or specialist document search

Evaluate LLM applications using automated scores, human reviews, and model-based judging. Build repeatable checks to compare models or prompts, assess answer quality, and detect declines in performance before deployment.

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• Comparing models and prompts
• Checking quality after prompt changes
• Detecting performance declines before deployment

Build an end-to-end evaluation pipeline for Python AI applications using pixie-qa and real language-model calls. Define criteria, prepare realistic test data, score application runs, and analyze the evidence to produce a prioritized improvement plan.

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• Setting up quality checks for Python AI applications
• Creating realistic scenarios for known failure modes
• Scoring full application runs with real language-model calls

Check security audit findings against code evidence and trace how untrusted input moves through a system. Guide a second review, document verification gaps, and assess draft reports against a minimum quality score before delivery.

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• Verifying evidence behind security review findings
• Rechecking input flows after an initial analysis
• Assessing report quality before delivery

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

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• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Turn a feature into an agent skill or improve an existing one. Define purpose-specific quality criteria, review outputs with models from three providers, then add tests and check that user requests reach the intended skill.

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• Turn a reusable feature into an agent skill
• Audit an existing skill for missing parts
• Check quality after fixing or extending a skill

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

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• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Configure GSD through one command, covering model choice, research, plan checks, verification, and branching. Additional modes handle detailed workflow settings, external service integrations, and model profiles, which are named groups of model settings.

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• Adjusting common GSD workflow settings through guided questions
• Changing planning details, timeouts, and branch templates
• Configuring external APIs and code review tools

Audit existing public equity investment models and spreadsheets for formula errors, source support, assumptions, and scenario quality. Receive a readiness verdict, an issue log, and prioritized fixes showing whether the model supports its intended decision.

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• Check formulas and controls in an existing investment workbook
• Trace important assumptions to supporting sources
• Review valuation scenarios and downside risks

Assess whether data is trustworthy enough for analysis, dashboards, experiments, or modeling. Check missing values, duplicates, consistency, freshness, and changes over time, then explain the evidence, downstream risks, likely causes, and practical fixes.

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• Check missing values and duplicate records
• Investigate sudden changes in data quality
• Reconcile conflicting sources and metric definitions

Use when the user wants to migrate code that calls OpenAI, Gemini/Google AI, or the Anthropic API to Amazon Bedrock — a pure model/SDK rewrite. End-to-end: assesses the codebase, rewrites SDK calls, evaluates output quality against Bedrock, and delivers a ready-to-merge git branch. Also has an access-only mode for checking or enabling Bedrock model access for specific models (e.g. 'request access to Claude on Bedrock', 'which models do I turn on') with no code migration — it checks each model and walks the right access step, then stops. Not for agent runtime selection or agent migration planning (use agent-advisor), nor standalone cost estimates or infra-only migration. The full migration REQUIRES gcp-to-aws OR azure-to-aws installed alongside this skill (gcp-to-aws preferred when both are present); access-only mode needs neither.

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Resolve model profile (quality/balanced/budget) at orchestration start and map agents to specific models. Enables cost/quality tradeoffs by selecting appropriate AI models for each agent role.

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Collect and assess quality metrics for Simulink models using the metric.Engine API. Use when evaluating model design quality (maintainability, complexity, architecture) or testing completeness (unit testing, SIL, PIL, coverage, pass rates) after edits or test runs. Covers metric discovery, collection, report generation, and diagnostics. Triggers on: "collect metrics", "quality metrics", "metric.Engine", "testing metrics", "maintainability metrics", "SIL metrics", "PIL metrics", "what metrics are available", "assess quality", "coverage metrics", "generate metric report", "diagnose metric errors".

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Use when comparing Scenario models on the same brief through MCP: a bake-off with shared prompts and inputs, cost, generation time and quality measured side by side, choosing a default model, testing a new release against the incumbent, or finding which model handles a style, edit, or reference image best. Keywords: model comparison, benchmark, bake-off, A/B test, contact sheet, cost per asset, latency.

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Run AI agent and LLM evaluations in CI/CD pipelines — automated quality gates that fail the build when AI output quality drops. Use when someone asks to "test my AI agent", "add evals to CI", "catch prompt regressions", "compare models", "evaluate LLM output quality", "set up AI quality gates", or "benchmark my agent before deploying". Covers eval frameworks (Cobalt, Promptfoo, Braintrust), LLM-as-judge scoring, threshold-based assertions, and GitHub Actions integration.

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通过改变提示词和输入表达，检验经API访问的大语言模型对安全过滤绕过尝试的响应，并比较多个模型的回答及保存Hermes设置。

• 评估模型抵御安全绕过的能力
• 比较不同输入表达下的响应
• 比较多模型的拒答、质量和速度

使用稀疏自编码器将语言模型内部的激活分解为少量特征，研究模型学到的概念。涵盖预训练SAE分析、自定义训练、重建质量评估，以及利用特征调整输出的实验。

• 使用预训练SAE研究特征
• 针对特定模型层训练SAE
• 比较不同输入文本的特征响应

分析开放权重语言模型的拒答机制，支持无需重新训练的权重修改实验，并根据计算环境选择模型与方法，比较修改前后的拒答率和回答质量。

• 研究语言模型的拒答机制
• 无需重新训练开展权重修改实验
• 按计算环境选择模型与方法

支持开发让 AI 使用外部服务的 MCP 服务器，从 API 调研、工具设计与实现，到功能检查和评估题编写，帮助检验多次工具调用下的实际使用效果。

• 将外部 API 接入 AI 智能体
• 用 TypeScript 或 Python 开发 MCP
• 改进工具说明、输入和错误提示

让Claude、GPT和Gemini执行同一技能或提示词，以表格比较响应时间、词元用量和费用，并可选用质量评分，帮助选择适合任务的模型。

• 为技能选择合适的模型
• 比较响应时间与费用
• 通过可选的AI评分比较质量

Restore image quality using restoration models hosted by fal.ai. The skill covers reducing blur and noise, fixing faces, and restoring old document images, with the full workflow available through the upstream skill bundle.

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• Reducing blur in images
• Removing image noise
• Fixing facial image quality

Compare actual AI session costs with estimates for using only Haiku, Sonnet, or Opus. The skill reports savings amounts and percentages, helping teams assess model selection costs while leaving differences in answer quality and retries outside the calculation.

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• Report model selection savings during cost reviews
• Check savings after changing model selection settings
• Use JSON results to enforce a savings threshold in CI