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lab21-qwen35-triage-vi

README

License: apache-2.0

Verdict: FAILED

The gate is not "did the loss go down". It is: does the fine-tune beat the well-prompted base model on the task, without losing general capability?

target Δ +0.115 · regression Δ -0.622 (tolerance 0.020)

general capability regressed by 0.622 (tolerance 0.020). See deck §14.3 — add 1-5% replay data.

Four-group evaluation

Scored on 50 held-out tickets + 15 general-knowledge probes, greedy decode.

Table with columns: run, target, regression, format, latency (ms)
run	target	regression	format	latency (ms)
(a) base + naive prompt	0.000	0.622	0.000	1509
(b) base + optimized prompt	0.450	0.622	1.000	535
(c) LoRA fine-tune	0.565	0.000	1.000	720

(a) base + a naive prompt · (b) base + a carefully engineered prompt · (c) this adapter
target = per-field accuracy over the 4 triage fields · regression = keyword recall on general-knowledge questions the fine-tune must not break · format = required keys present

Misconfiguration autopsy — scored on the task, not on training loss

Table with columns: run, target, format, latency (ms)
run	target	format	latency (ms)
`correct`	0.565	1.000	720
`attn_only`	0.545	1.000	452
`wrong_lr`	0.185	0.800	1278

attn_only carries the same trainable-parameter budget as correct (rank raised to match), so it isolates placement from rank. wrong_lr changes exactly one number: the learning rate, from the LoRA scale back to the full-fine-tune scale.

Training

Table

Base	`Qwen/Qwen3.5-0.8B`
Placement	`text-linear` — every text-decoder linear layer, vision tower excluded
r / alpha	16 / 32
Trainable params	10,822,656
Learning rate	0.0001
Optimizer steps	58
Precision	bf16
Peak VRAM	3.15 GB

The loss mask was verified by decoding it back, not assumed: the answer is inside the supervised span (True) and the question is not (True).

Usage

python

from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base_id = "Qwen/Qwen3.5-0.8B"tok = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(base_id, trust_remote_code=True,                                             dtype="auto", device_map="auto")model = PeftModel.from_pretrained(model, "anybody12345678/lab21-qwen35-triage-vi") msgs = [    {"role": "system", "content": "Phân loại ticket sau."},    {"role": "user", "content": "Shop ơi, mình đặt bàn phím cơ mã đơn DH123456. "                                 "Giao hàng chậm. Đã 3 ngày rồi. Nhờ shop kiểm tra."},]text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True,                               enable_thinking=False)out = model.generate(**tok(text, return_tensors="pt").to(model.device),                     max_new_tokens=160, do_sample=False)print(tok.decode(out[0][len(tok(text)["input_ids"]):], skip_special_tokens=True))

The system prompt matters. This adapter was trained with "Phân loại ticket sau." as the system message and the bare ticket as the user turn. Send it a different scaffold and it is being asked to continue a prompt it never saw.

Limitations — read these before using it

General capability is not preserved. See the regression column. Trained on bare ticket → JSON with no instruction, the model learns that any input means "emit triage JSON", and it answers general-knowledge questions in JSON too. The remedy is 1–5% replay data; this adapter does not have it.
Trained on a synthetic, template-generated Vietnamese corpus, not production tickets. The label space is a local convention ("hoàn lại" is filed under doi_tra, not hoan_tien) and will not match yours.
Small base model, short training budget. This is a teaching artifact.

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Model Details

Model Provider

anybody12345678

Model Tree

Base

Qwen/Qwen3.5-0.8B

Adapter

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality