---
license: apache-2.0
tags:
- nora
- trinidad-tobago
- tstt
- qwen3.5
- layer-selective
- ties-merge
- paraphrase-robust
language:
- en
base_model: Qwen/Qwen3.5-4B
datasets:
- dmvevents/tt-training-v3.2
---

# Nora v3.2 — T&T AI Assistant (paraphrase-robust)

v3.2 targets paraphrase robustness on top of v3.1's gap-closing gains.
Starts from pristine Qwen3.5-4B, trained on the v3.2 mix (23,273 rows:
sft_v4 + v3.1 corrective + 1,353 paraphrase-augmented rows targeting
stressed/negation/multi-part prompt styles + new corrective from v3.1
failures), 2-seed TIES + SLERP anchor.

## Ship-gate Results (2026-05-06)

| Eval set | Claude Sonnet 4.5 | Δ vs v3.1 | Δ vs v2 |
|---|---|---|---|
| 1,420 paraphrases | **89.2%** | **+2.1pp** | **+4.3pp** |
| 143-base         | 89.9% | -0.3pp | +5.0pp |

Pass-rate @4/5: 89.8% (v3.1 was 86.8%, +3.0pp).

### Per-category, 1,420-paraphrase set (Claude)

| Category | v3.1 | **v3.2** | Δ |
|---|---:|---:|---:|
| safety | 86.3 | **91.3** | **+5.03** ← target |
| government_service | 80.3 | **83.9** | **+3.62** ← target |
| creole_handling | 85.6 | **88.2** | **+2.60** ← target |
| identity | 95.2 | **97.6** | +2.42 |
| education_general | 87.7 | **89.2** | +1.45 |
| healthcare | 87.5 | 87.7 | ~0 |
| csec_math | 92.0 | 90.5 | -1.57 |

All three gap-targeted categories improved by +2.6 to +5.0pp. 6 of 7
categories improved; csec_math regressed within noise (still 90.5%).

v3.2 wins 300 prompts, loses 200, ties 913 (57% win rate on non-ties).

## Training recipe (unchanged from v3.1)

- Base: **pristine Qwen3.5-4B** (NOT v3.1 — avoid compounded drift)
- Layer-selective (DeltaNet frozen, Attention + MLP + norms + lm_head trained)
- Completions-only loss
- LR 5e-6, 1 epoch on 23,273 rows
- 2 seeds (42, 1337), 20-min launch stagger
- Step checkpoints @ 150/300/364; promote checkpoint-364 → final (sidesteps
  trainer.save_model() hang on Qwen3.5 hybrid FSDP)
- TIES merge (density=0.5, trim=0.2, scale=1.0) + SLERP anchor t=0.35

## Data recipe vs v3.1

**The v3.2 innovation**: 1,353 paraphrase-augmented rows. For each v3.1
corrective training row, Bedrock Opus generated 3 new user-turn rephrases
hitting styles 5 (stressed/frustrated), 6 (negation probe), and 9
(multi-part compound). Assistant turn preserved verbatim. This targets the
v3.1 paraphrase-style brittleness identified in the gap analysis.

Also: 882 new 7-pattern corrective rows from v3.1 failure buckets, 45
polarity hard-negatives on 5 new fact cards, 5 fact-card content fixes,
and a "use verified cards verbatim" rule added to the retrieval router.

## Files

- `model.safetensors` — 11.1 GB bf16
- `config.json`, `tokenizer.json`, etc.
- GGUF variants: [dmvevents/nora-4b-v3.2-GGUF](https://huggingface.co/dmvevents/nora-4b-v3.2-GGUF)
- Eval artifacts: [dmvevents/tt-eval-v3.2-results](https://huggingface.co/datasets/dmvevents/tt-eval-v3.2-results)

## Licence

Apache 2.0 (inherited from Qwen3.5-4B).