VALIDATION.md

# Validating marvy-1-14B

This guide gives you three independent ways to confirm the fine-tune actually
learned the ServiceNow delivery style — from a 60-second smoke test to a
quantitative base-vs-marvy comparison on a held-out, customer-disjoint test set.

> TL;DR: run `bash docs/validate.sh` (from the model repo) for the quick path,
> or follow the manual steps below.

---

## What "working" means here

marvy-1-14B is a **specialist drafting model**. A successful fine-tune should show:

1. **Format fidelity** — it emits the delivery artifact shape on cue (user
   stories with acceptance criteria, SDD sections, test cases with
   pre-conditions/steps/expected results) without being told the structure.
2. **Domain voice** — OOTB-first framing, ServiceNow tables/plugins, ITIL/CSDM
   vocabulary, `sys_id` citations where relevant.
3. **Lower loss than the base** on held-out ServiceNow delivery text.

The base model (Qwen2.5-14B-Instruct) is a strong generalist and will produce
*plausible* answers — the point of validation is to show marvy is **more
on-format, more domain-specific, and lower-perplexity** on this task.

---

## Test 1 — 60-second smoke test (qualitative)

Prompt the model with a bare instruction and check it produces a correctly
structured artifact with no format coaching.

### LM Studio (local)

```bash
lms load MainStack/marvy-1-14B
lms server start          # OpenAI-compatible on http://localhost:1234/v1

curl -s http://localhost:1234/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "marvy-1-14B",
    "temperature": 0.4,
    "messages": [
      {"role": "system", "content": "You are a senior ServiceNow delivery consultant. You produce precise, implementation-grade artifacts and favor out-of-the-box capabilities."},
      {"role": "user", "content": "Write a user story with acceptance criteria for auto-escalating P1 incidents that breach a 15-minute response SLA."}
    ]
  }' | python3 -c "import sys,json;print(json.load(sys.stdin)['choices'][0]['message']['content'])"
```

### MLX (Apple Silicon)

```bash
python -m mlx_lm generate --model MainStack/marvy-1-14B \
  --system-prompt "You are a senior ServiceNow delivery consultant..." \
  --prompt "Write a user story with acceptance criteria for auto-escalating P1 incidents that breach a 15-minute response SLA." \
  --max-tokens 512 --temp 0.4
```

### Pass criteria

- [ ] Output is a **user story** (`As a … I want … so that …`) followed by
      discrete, testable **acceptance criteria**.
- [ ] References ServiceNow concretely (e.g. `incident`, SLA definitions,
      `sla_definition`, escalation/notification, assignment groups).
- [ ] No meta-chatter ("Sure, here is…") dominating the answer; it reads like a
      backlog item, not a chatbot reply.

---

## Test 2 — Task-coverage probes (qualitative, one per skill)

Run each prompt with the recommended system prompt. Each should yield the
artifact named, in the right shape.

| # | Prompt | Expect |
|---|--------|--------|
| 1 | "Draft the Incident Management section of an SDD for a greenfield ITSM implementation. Include assignment rules and SLA design." | SDD section: architecture/process, assignment rules (condition/action/order), SLA table |
| 2 | "Extract structured requirements (id, category, priority, target phase, success metric) from: 'We need to replace email-based access requests with a catalog item routed for manager approval.'" | Tabular/structured requirements with priorities & metrics |
| 3 | "Write a test case for the story: 'Restrict the Assignment Group field on incidents to groups with the itil role.'" | Test case: pre-conditions, steps, expected results, pass/fail |
| 4 | "We are migrating CMDB to CSDM. Produce the foundation-data load sequence and the CI classes involved." | CSDM/CMDB sequence, classes (cmdb_ci_*), foundation order |
| 5 | "Validate this requirement against best practice and list follow-up questions: 'All incidents must auto-close after 3 days.'" | Critique + concrete follow-up questions + risks |

### Pass criteria
At least **4 of 5** produce the correct artifact type with ServiceNow-specific,
implementation-grade content (not generic ITSM prose).

---

## Test 3 — Quantitative: base vs marvy on the held-out test set

This is the strongest signal. The test split is **customer-disjoint** — two
customers that never appear in training or validation — so it measures
generalization, not memorization.

### With the MLX training kit (in the source repo)

```bash
cd training

# marvy (fine-tuned adapter on the base)
python -m mlx_lm lora \
  --model mlx-community/Qwen2.5-14B-Instruct-4bit \
  --adapter-path train/adapters \
  --data train/data --test --test-batches 50
# -> Test loss 2.573, Test ppl 13.107   (lower is better)

# base (no adapter) for comparison
python -m mlx_lm lora \
  --model mlx-community/Qwen2.5-14B-Instruct-4bit \
  --data train/data --test --test-batches 50
# -> expect a HIGHER loss/ppl than marvy
```

### Pass criteria
- [ ] marvy's **test perplexity is meaningfully lower** than the base on the
      same held-out split.
- [ ] No data leakage: the test customers (`Customer-CHEM-01`,
      `Customer-FININST-01`) are absent from `train.jsonl` / `valid.jsonl`.

> Reference result for this release: **test loss 2.573 / ppl 13.107** on 50
> batches of the project-disjoint test split (two sequences >2048 tokens are
> truncated by the eval harness, so this is a slight upper bound).

---

## Interpreting results

| Symptom | Likely cause | Action |
|---|---|---|
| Generic ITSM prose, no ServiceNow specifics | wrong/short system prompt | use the full recommended system prompt; temp 0.3–0.5 |
| Rambling, no artifact structure | temperature too high | lower to 0.3–0.4 |
| Invents `sys_id`s / plugin IDs | expected limitation | verify against a real instance; never trust IDs blindly |
| marvy ppl ≈ base ppl | adapter not applied / wrong checkpoint | confirm `--adapter-path` points at the trained adapter (iter-150) |

marvy-1-14B is a first-draft assistant. All output must be reviewed by a qualified
ServiceNow consultant before client delivery or production configuration.