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README.md

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+---
+language:
+- vi
+- en
+license: apache-2.0
+base_model: Qwen/Qwen2.5-3B-Instruct
+tags:
+- vietnamese
+- english
+- customer-support
+- instruction-tuning
+- lora
+- unsloth
+pipeline_tag: text-generation
+---
+
+# 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
+```text
+Instruction:
+{instruction}
+
+User:
+{input}
+
+Assistant:
+```
+
+## Quick usage (PEFT)
+```python
+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