Spaces:

validation
/

model-validation

Running

File size: 15,171 Bytes

79313fe

<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>Model Validation</title>
<meta name="description" content="Validate AI models across quality, robustness, hallucination, calibration, regression, latency, and deployment readiness." />
<style>
:root{
  --bg:#f7fbff;--panel:#fff;--text:#102235;--muted:#607286;--line:#dfeaf3;
  --a:#1685ff;--b:#17ba9c;--soft:#eef8ff;--good:#eefcf6;--shadow:0 16px 42px rgba(28,77,117,.10)
}
*{box-sizing:border-box}
body{margin:0;background:linear-gradient(180deg,#f9fdff,#eef8ff);font-family:Inter,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,"Segoe UI",sans-serif;color:var(--text)}
.container{max-width:1180px;margin:auto;padding:26px 18px 60px}
.hero{padding:34px;border:1px solid var(--line);border-radius:28px;background:linear-gradient(135deg,#fff 0%,#effaff 58%,#eef8ff 100%);box-shadow:var(--shadow)}
.eyebrow{font-size:13px;font-weight:800;letter-spacing:.12em;text-transform:uppercase;color:#2878c5}
h1{font-size:clamp(34px,5vw,60px);line-height:1.03;letter-spacing:-.04em;margin:9px 0 14px}
.lead{font-size:18px;line-height:1.65;color:#40546a;max-width:900px}
.badges{display:flex;gap:9px;flex-wrap:wrap;margin-top:18px}
.badge{padding:8px 11px;border:1px solid var(--line);border-radius:999px;background:#fff;font-size:13px;font-weight:750;color:#39536b}
.grid{display:grid;grid-template-columns:1fr 1fr;gap:22px;margin-top:24px}
.card{background:var(--panel);border:1px solid var(--line);border-radius:22px;padding:24px;box-shadow:0 10px 28px rgba(31,79,121,.07)}
.card h2{font-size:22px;margin:0 0 8px}
.card p{color:var(--muted);line-height:1.6}
label{display:block;font-weight:750;margin:17px 0 7px}
select{width:100%;padding:13px 14px;border:1px solid #cddce8;border-radius:13px;background:#fff;color:var(--text);font-size:15px}
button{margin-top:22px;border:0;border-radius:14px;padding:14px 17px;background:linear-gradient(135deg,var(--a),var(--b));color:#fff;font-weight:800;font-size:15px;cursor:pointer;box-shadow:0 8px 18px rgba(30,136,255,.18)}
button:hover{transform:translateY(-1px)}
.result-head{display:flex;justify-content:space-between;gap:16px;align-items:flex-start;border-bottom:1px solid var(--line);padding-bottom:16px;margin-bottom:16px}
.score{min-width:94px;text-align:center;padding:10px;border:1px solid #caecdf;background:var(--good);border-radius:16px}
.score strong{font-size:28px;display:block}
.small{font-size:13px;color:var(--muted)}
.section{margin-top:20px}
.section h3{font-size:18px;margin:0 0 10px}
.checks{display:grid;gap:10px}
.check{padding:13px 14px;border:1px solid var(--line);border-radius:14px;background:#fbfdff}
.check b{display:block;margin-bottom:4px}
.check span{color:var(--muted);font-size:14px;line-height:1.5}
.matrix{margin-top:26px;overflow:auto;border:1px solid var(--line);border-radius:18px;background:#fff}
table{width:100%;border-collapse:collapse;min-width:840px}
th,td{padding:13px 15px;text-align:left;border-bottom:1px solid var(--line);vertical-align:top}
th{background:#f3f9ff;font-size:13px;color:#486177}
td{font-size:14px;line-height:1.45}
.info{margin-top:24px;padding:20px 22px;border:1px solid #d8ebff;border-radius:18px;background:var(--soft);color:#34526c;line-height:1.65}
.footer{margin-top:34px;color:#6b7b8c;font-size:13px;line-height:1.6}
a{color:#0f71da}
@media(max-width:800px){.grid{grid-template-columns:1fr}.hero{padding:26px}.container{padding:16px 13px 45px}}
</style>
</head>
<body>
<div class="container">
  <section class="hero">
    <div class="eyebrow">Validation · Model Assurance</div>
    <h1>Model Validation</h1>
    <p class="lead">Validate AI models across task performance, robustness, hallucination behavior, calibration, regression, operational limits, and deployment readiness.</p>
    <div class="badges">
      <span class="badge">Quality</span><span class="badge">Robustness</span><span class="badge">Hallucination</span>
      <span class="badge">Calibration</span><span class="badge">Regression</span><span class="badge">Deployment</span>
    </div>
  </section>

  <div class="grid">
    <section class="card">
      <h2>Build a model validation plan</h2>
      <p>Select the model profile and deployment context. The tool will recommend the most important validation dimensions.</p>

      <label for="modelType">Model type</label>
      <select id="modelType">
        <option value="llm">Language model / LLM</option>
        <option value="vision">Vision model</option>
        <option value="audio">Audio / speech model</option>
        <option value="multimodal">Multimodal / omnimodal model</option>
        <option value="embedding">Embedding / retrieval model</option>
        <option value="world">World model</option>
      </select>

      <label for="stage">Lifecycle stage</label>
      <select id="stage">
        <option value="dev">Development</option>
        <option value="pre" selected>Pre-production</option>
        <option value="prod">Production</option>
        <option value="change">Revalidation after a model change</option>
      </select>

      <label for="impact">Operational impact</label>
      <select id="impact">
        <option value="low">Low — advisory or exploratory</option>
        <option value="medium" selected>Medium — business workflow impact</option>
        <option value="high">High — significant autonomous or real-world impact</option>
      </select>

      <button onclick="buildPlan()">Generate model validation plan</button>
      <p class="small">Planning aid only; not a certification or legal assessment.</p>
    </section>

    <section class="card">
      <div class="result-head">
        <div>
          <h2>Your model validation plan</h2>
          <p id="summary">Generate a plan to see recommended validation dimensions.</p>
        </div>
        <div class="score"><span>Coverage</span><strong id="score">—</strong><span class="small">dimensions</span></div>
      </div>
      <div id="content" class="small">The plan will cover capability, reliability, operational evidence, and revalidation triggers.</div>
    </section>
  </div>

  <section class="card" style="margin-top:24px">
    <h2>Core model validation matrix</h2>
    <p>A strong validation program combines benchmark evidence with robustness, regression, operational, and use-case-specific testing.</p>
    <div class="matrix">
      <table>
        <thead><tr><th>Dimension</th><th>Question</th><th>Evidence</th><th>Typical failure</th></tr></thead>
        <tbody>
          <tr><td>Task performance</td><td>Does the model perform well on the intended task?</td><td>Representative benchmarks, task metrics, human review</td><td>Strong public benchmark, weak domain performance</td></tr>
          <tr><td>Robustness</td><td>Does performance hold under variation and noise?</td><td>Prompt variants, perturbations, edge cases</td><td>Sharp degradation under small input changes</td></tr>
          <tr><td>Hallucination / grounding</td><td>Are claims supported when evidence is required?</td><td>Grounded QA, citation checks, factuality tests</td><td>Confident unsupported outputs</td></tr>
          <tr><td>Calibration</td><td>Does confidence reflect actual correctness?</td><td>Reliability curves, abstention tests, confidence analysis</td><td>High confidence on wrong answers</td></tr>
          <tr><td>Regression</td><td>Did a new version break important behavior?</td><td>Versioned regression suite</td><td>Improvement on one metric with hidden degradation elsewhere</td></tr>
          <tr><td>Operational limits</td><td>Can the model meet production constraints?</td><td>Latency, throughput, memory, context, cost</td><td>Good quality but unusable production characteristics</td></tr>
          <tr><td>Safety boundaries</td><td>Does the model remain within defined constraints?</td><td>Policy tests, adversarial tests, refusal analysis</td><td>Unsafe behavior on uncommon inputs</td></tr>
          <tr><td>Reproducibility</td><td>Can the result be repeated and explained?</td><td>Versioned config, datasets, prompts, metrics</td><td>Scores without enough context to reproduce them</td></tr>
        </tbody>
      </table>
    </div>
  </section>

  <div class="info">
    <strong>Working definition:</strong> Model validation is the process of establishing evidence that an AI model meets defined capability, reliability, robustness, operational, and risk requirements for a specific intended use.
  </div>

  <div class="footer">
    Open technical resource by the <strong>Validation</strong> organization on Hugging Face.<br>
    Research & industry collaborations: <a href="mailto:agenten@magenta.de">agenten@magenta.de</a>
  </div>
</div>

<script>
const profiles = {
  llm: [
    ["Task performance","Evaluate representative language, reasoning, extraction, classification, or generation tasks."],
    ["Hallucination & grounding","Measure unsupported claims, citation quality, and grounded answer behavior."],
    ["Prompt robustness","Test rephrasing, formatting changes, context ordering, and instruction variation."],
    ["Context behavior","Validate long-context use, truncation behavior, and retrieval integration."],
    ["Structured output","Check schema conformance and semantic correctness for machine-consumed outputs."]
  ],
  vision: [
    ["Task performance","Validate classification, detection, segmentation, OCR, or generation quality for the target domain."],
    ["Image robustness","Test lighting, compression, occlusion, blur, crop, and distribution changes."],
    ["Domain coverage","Validate performance across relevant devices, environments, and visual conditions."],
    ["False-positive / false-negative analysis","Measure failure classes that matter operationally."],
    ["Resolution sensitivity","Test input-size, scaling, and preprocessing effects."]
  ],
  audio: [
    ["Task performance","Measure speech recognition, classification, generation, or audio understanding for the intended use."],
    ["Noise robustness","Test background noise, channel quality, accents, speed, overlap, and degraded audio."],
    ["Temporal consistency","Check performance across long or segmented audio sequences."],
    ["Language and speaker coverage","Validate representative languages, accents, speakers, and recording conditions."],
    ["Latency","Measure streaming or near-real-time performance where relevant."]
  ],
  multimodal: [
    ["Cross-modal consistency","Check whether text, image, audio, and video evidence agree."],
    ["Grounding","Validate that outputs remain tied to the actual multimodal inputs."],
    ["Missing-modality behavior","Test absent, degraded, delayed, or contradictory modalities."],
    ["Temporal reasoning","Validate ordering, timing, and consistency for video and audio."],
    ["Any-to-any output","Test quality and consistency across supported output modalities."]
  ],
  embedding: [
    ["Retrieval quality","Measure recall, precision, ranking quality, and semantic relevance."],
    ["Domain transfer","Validate embeddings on the target corpus and terminology."],
    ["Distribution shift","Test performance on new topics, languages, and data sources."],
    ["Vector stability","Check whether version changes materially alter retrieval behavior."],
    ["Latency & scale","Measure indexing and query performance under expected load."]
  ],
  world: [
    ["State representation","Check whether the model captures relevant world state correctly."],
    ["Predictive consistency","Validate future-state predictions under realistic dynamics."],
    ["Spatial reasoning","Test geometry, object relationships, occlusion, and persistence."],
    ["Temporal coherence","Evaluate continuity across generated or predicted future states."],
    ["Action relevance","Check whether predictions preserve properties required for planning or control."],
    ["Sim-to-real validity","Compare simulation evidence with real-world behavior where applicable."]
  ]
};

const stageAdds = {
  dev: [["Development reproducibility","Version datasets, prompts, checkpoints, metrics, and configuration."]],
  pre: [["Release criteria","Define explicit pass/fail thresholds before deployment."],["Stress testing","Test edge cases, degraded inputs, and failure boundaries."]],
  prod: [["Continuous validation","Use drift, incidents, user corrections, and production telemetry as evidence."],["Monitoring linkage","Connect production observability to validation thresholds."]],
  change: [["Regression suite","Compare old and new model versions on decision-relevant behavior."],["Change impact analysis","Identify which previous validation assumptions no longer hold."]]
};

const impactAdds = {
  low: [],
  medium: [["Operational acceptance criteria","Define quality, latency, failure-rate, and fallback thresholds."]],
  high: [
    ["Independent review","Add a second validation path or human review for critical requirements."],
    ["Adversarial testing","Test manipulation, boundary cases, and difficult failure scenarios."],
    ["Fallback behavior","Validate abstention, escalation, safe failure, and human handoff."]
  ]
};

function buildPlan(){
  const t=document.getElementById("modelType").value;
  const s=document.getElementById("stage").value;
  const i=document.getElementById("impact").value;
  const list=[...profiles[t],...stageAdds[s],...impactAdds[i]];
  const names={llm:"language model / LLM",vision:"vision model",audio:"audio / speech model",multimodal:"multimodal / omnimodal model",embedding:"embedding / retrieval model",world:"world model"};

  document.getElementById("summary").textContent=`Recommended validation coverage for a ${names[t]}.`;
  document.getElementById("score").textContent=list.length;
  document.getElementById("content").innerHTML=`
    <div class="section"><h3>Recommended dimensions</h3><div class="checks">
      ${list.map(x=>`<div class="check"><b>${x[0]}</b><span>${x[1]}</span></div>`).join("")}
    </div></div>
    <div class="section"><h3>Evidence to preserve</h3><div class="checks">
      <div class="check"><b>Version context</b><span>Model, checkpoint, tokenizer or processor, dataset, prompt, dependencies, and environment.</span></div>
      <div class="check"><b>Evaluation context</b><span>Task definition, dataset version, metrics, thresholds, sample counts, and methodology.</span></div>
      <div class="check"><b>Failure evidence</b><span>Keep representative failure cases, severity, limitations, and unresolved risks.</span></div>
    </div></div>
    <div class="section"><h3>Revalidate when</h3><div class="checks">
      <div class="check"><b>Model changes</b><span>New model, checkpoint, fine-tune, quantization, tokenizer, or inference configuration.</span></div>
      <div class="check"><b>Use-case changes</b><span>New domain, population, language, modality, task, or operating condition.</span></div>
      <div class="check"><b>Production evidence changes</b><span>Drift, incidents, increased corrections, new failure patterns, or degraded performance.</span></div>
    </div></div>`;
}
buildPlan();
</script>
</body>
</html>