viet-customer-support-model

qwen2.5-3b-viet-customer-support-lora

Vietnamese-first bilingual customer support LoRA adapter fine-tuned from Qwen2.5-3B-Instruct with Unsloth.

What this model is for

• Vietnamese customer support conversations
• English fallback responses
• Polite, concise, next-step-oriented support messaging

Base model

• Qwen/Qwen2.5-3B-Instruct

Training data (high-level)

• OPUS-100 EN↔VI parallel pairs (filtered)
• Synthetic customer support instruction examples (order status, refunds, shipping delays, account issues, billing)
• Final split: 176k train / 4k eval

Training setup

• Framework: Unsloth + TRL SFTTrainer
• Precision: bf16
• Quantization for training: 4-bit base model
• LoRA: r=32, alpha=64, dropout=0.0
• Max seq len: 768

Prompt format

Instruction:
{instruction}

User:
{input}

Assistant:

Quick usage (PEFT)

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = "Qwen/Qwen2.5-3B-Instruct"
adapter = "REPLACE_WITH_HF_REPO"

tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

prompt = """Instruction:
Bạn là nhân viên chăm sóc khách hàng tiếng Việt. Trả lời lịch sự, đồng cảm, và nêu bước tiếp theo rõ ràng.

User:
Xin chào, đơn hàng của tôi bị trễ 5 ngày. Mã đơn #A12345.

Assistant:
"""

inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=180, temperature=0.3, do_sample=True)
print(tok.decode(out[0], skip_special_tokens=True))

Limitations

• Not a legal/compliance authority
• Can still hallucinate policy details if product policy is ambiguous
• Should be paired with retrieval or hard policy checks in production

Recommended production guardrails

• Ground responses on your real policy KB
• Enforce redaction/PII handling
• Add escalation rules for sensitive requests