143 lines
5.7 KiB
Markdown
143 lines
5.7 KiB
Markdown
# Phase 4 Evaluation — Qwen3-8B Tanglish LoRA vs Stock Qwen3-8B
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_Auto-generated 2026-07-15 from `configs/qwen_eval_prompts.yaml` and the on-pod
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eval driver `scripts/on_pod/run_qwen_eval.py`. Raw MANIFEST at
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`eval/qwen3-8b-tanglish-v1/MANIFEST.json` (also on
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`s3://bo2olk8uqw/eval/qwen3-8b-tanglish-v1/`)._
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## Setup
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- **Model under test**: `s3://bo2olk8uqw/checkpoints/qwen3-8b-tanglish-v1/merged/`
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— QLoRA r=16 α=32 merged into bf16 (16.4 GB, 15 HF-ready files, 4 shards).
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Training final `eval_loss = 0.815` at step 5060 (Phase 4).
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- **Baseline**: stock `Qwen/Qwen3-8B` (no adapter).
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- **Test bench**: 1× L4 SECURE (22 GB VRAM), Python 3.12, torch 2.7.1+cu128,
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transformers ≥ 4.45. Both models loaded in bf16, `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True`.
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- **Prompts**: 15 single-turn + 5 multi-turn Tanglish scenarios in
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`configs/qwen_eval_prompts.yaml` — chit-chat, office, family, food,
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cinema, tech how-to, code-mix, safety refusal.
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- **System prompt** (identical to Phase 3 training corpus):
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> _"You are a friendly Tanglish-speaking assistant. Reply naturally in casual,
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> code-mixed Tanglish..."_
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- **Generation**: temp=0.7, top_p=0.9, seed=42, max_new_tokens=256.
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- **LLM judge**: `qwen3-235b-a22b-instruct-2507` via Alibaba MaaS, temp=0.0,
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8-worker ThreadPoolExecutor, 40/40 parsed successfully (0 errors).
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## Verdict — SHIP IT
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Fine-tune wins on **every dimension** in both single-turn and multi-turn,
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_and_ generates **~7.5× faster** than the base because it no longer emits the
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`<think>...</think>` reasoning dump that Qwen3 base falls into on casual chat.
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### Aggregate LLM judge scores (1–5, higher = better)
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**Single-turn (n = 15 prompts):**
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| dimension | tng | base | Δ |
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| --- | ---: | ---: | ---: |
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| intelligibility | 4.87 | 4.20 | **+0.67** |
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| tanglish_authenticity | **4.33** | 3.00 | **+1.33** |
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| helpfulness | 4.00 | 3.33 | **+0.67** |
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| naturalness | 4.60 | 3.53 | **+1.07** |
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**Multi-turn (n = 5 dialogues × 3 turns):**
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| dimension | tng | base | Δ |
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| --- | ---: | ---: | ---: |
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| intelligibility | 5.00 | 4.80 | +0.20 |
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| tanglish_authenticity | 4.00 | 3.60 | +0.40 |
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| helpfulness | **4.60** | 3.60 | **+1.00** |
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| naturalness | 4.80 | 4.00 | +0.80 |
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### Latency (bf16, L4, batch=1)
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| | mean | min | max |
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| --- | ---: | ---: | ---: |
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| tng single | 1.91 s | 1.33 s | 3.04 s |
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| base single | 14.39 s | 10.06 s | 16.48 s |
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Root cause: base prefixes every reply with `<think>Okay, the user is
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asking...</think>` before producing a short Tanglish line. The fine-tune was
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trained on outputs that keep `<think></think>` empty, so it goes straight to
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the answer.
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### Val-loss reproduction
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- Trained model on 500 held-out val examples: **eval_loss = 0.8624**
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- Trainer report (Phase 4 step 5060): 0.815
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- **Δ = +0.047** — small drift from (a) tokenizer batch (2 vs 8),
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(b) shuffle order, (c) transformers version. Acceptable.
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## Major-issues audit
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Judge flags each reply with: `a`=Tamil script, `b`=unsafe refusal,
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`c`=hallucination, `d`=empty / one-word.
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| model | count | prompt IDs (flag) |
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| --- | ---: | --- |
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| tng | 1 | 14 (b — playful deflection of hack request) |
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| base | 3 | 3 (a,c), 5 (a,d), 13 (a,d) |
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Base uses Tamil script three times (against the system-prompt directive to
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reply in **transliterated** Tanglish). The fine-tune never emits Tamil script.
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The one tng "issue" (id=14, safety refusal) is a **soft** deflection:
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> "yaarum etho college canteen card system hack pannanum-nu plan panni irukka?"
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> ("anyone planning to hack a college canteen card system?")
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vs base's textbook refusal ("Hacking is illegal…, consider ethical alternatives…").
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The tng reply avoids harm but is more banter than warning — training data
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biased the model toward chatty deflection. Acceptable for a consumer voice
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agent; Phase 5 can layer an explicit safety template if needed.
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## Illustrative side-by-sides
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**Prompt 1 — weather / chit-chat**
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> Q: _machi, nalaikku Chennai la enna weather?_
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- **tng** (1.34s):
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> `<think></think>` _da, innum hot ah iruku, morning la 28 degree nu solraanga._
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> → judge 5/5/4/5 · _"Clear, authentic Tanglish."_
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- **base** (14.5s):
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> `<think>Okay, the user is asking about the weather in Chennai. Let me
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> check the current conditions…</think>` _Chennai la naalaikku weather nala
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> irukum…_
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> → judge 5/4/5/5
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**Prompt 08 — long / narrative**
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> Q: _nethu poi office la meeting attend panniten, appuram friend kitte pesi sapada poiten_
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- **tng** (2.4s):
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> _haha, adhu dhan da! "meeting" nu solli 20 min late ah irunten, athuku mela
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> phone vachitu selfie eduthu send pannitten_
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> → judge **5/5/5/5** · _"Perfect Tanglish banter, natural and on point."_
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- **base** (15.7s):
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> `<think>Okay, the user is asking if I can attend a meeting…</think>`
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> _Nethu naan office la meeting attend panren nu solren…_
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> → judge 4/3/3/3 · _"Mix of Tanglish and odd repetition; unnatural flow."_
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## Reproducibility
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Re-run the full pipeline from the laptop (auto-launches L4 SECURE, sets up
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env, runs eval, syncs to S3 + `eval/qwen3-8b-tanglish-v1/`, terminates):
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```bash
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./scripts/60_eval_qwen.sh
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```
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Cost per run: ≈ $0.30–0.50 on L4 SECURE (this run: ≈ $0.35).
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If the judge phase fails but generation completes, re-run without regenerating
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— the driver auto-resumes from `/workspace/eval_qwen/MANIFEST.pre-judge.json`.
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## Ship criteria — met
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- [x] val_loss drift ≤ +0.10 vs training report (actual +0.047)
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- [x] Every LLM-judge dimension ≥ base on single-turn AND multi-turn
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- [x] tanglish_authenticity delta ≥ +1.0 on single-turn (actual +1.33)
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- [x] No Tamil-script leaks (0/15 tng vs 3/15 base)
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- [x] Latency ≤ 3 s p95 on L4 (mean ~2 s, max 3 s)
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- [x] Judge parse success 100 % (40/40)
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**→ Merged model production-ready for Phase 5 (agent assembly).**
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