# Aitune Validate

> Use when verifying that a tuned model's outputs match the baseline eager model within numerical tolerance — run after each backend compilation before accepting it as a deployment candidate.

- Skill: `ai-dynamo/aitune-validate` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ai-dynamo/aitune-validate`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ai-dynamo/aitune-validate/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: Apache-2.0
- Author: ai-dynamo (https://skillmd.com/u/ai-dynamo)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/ai-dynamo/aitune-validate

---


# Correctness Validation

Write and execute a correctness script using a held-out sample (different from tuning input):

```python
import json, torch

with torch.no_grad():
    baseline_out = eager_model(validation_input)
    tuned_out = tuned_model(validation_input)

max_diff = (baseline_out - tuned_out).abs().max().item()
rel_error = ((baseline_out - tuned_out).abs() / baseline_out.abs().clamp(min=1e-8)).max().item()

# thresholds: fp32 → atol=1e-3, fp16 → atol=1e-2
atol = 1e-2  # adjust per precision
correctness_pass = max_diff <= atol

print(
    json.dumps({
        "max_abs_diff": max_diff,
        "max_rel_error": rel_error,
        "atol_threshold": atol,
        "correctness_pass": correctness_pass,
    })
)
```

If `correctness_pass` is false: log as "correctness failure", advance to next backend.