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Model: thanhdo881/qwen2.5-3b-vivu-travel-vn
Source: Original Platform

README.md

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+---
+language:
+- vi
+- en
+pipeline_tag: text-generation
+tags:
+- qwen
+- qwen2.5
+- slm
+- RAG
+- travel
+- vietnamese
+- unsloth
+- anti-hallucination
+base_model: Qwen/Qwen2.5-3B-Instruct
+---
+
+# qwen2.5-3b-vivu-travel-vn
+
+## Overview
+`qwen2.5-3b-vivu-travel-vn` is a 3B-parameter Small Language Model (SLM) fine-tuned for the **Vietnamese Tourism Domain**. Built on `Qwen2.5-3B-Instruct` using Unsloth (PEFT/LoRA), it acts as **ViVu**, an intelligent travel assistant optimized for **Advanced RAG** pipelines.
+
+### Key Features
+* **Strict Anti-Hallucination:** Zero-tolerance for fabrication; strictly grounds answers in the retrieved context and politely declines out-of-scope queries.
+* **RAG-Optimized:** Perfectly synthesizes Vector DB chunks into clean, structured Vietnamese (Markdown supported).
+* **Resource Efficient:** Deployable on consumer-grade GPUs (e.g., RTX 3060, T4) with low VRAM footprint.
+
+## Model Details
+* **Base Model:** Qwen/Qwen2.5-3B-Instruct
+* **Architecture:** Causal LM, 32k context length.
+* **Training Method:** LoRA Instruction-tuning via Unsloth.
+* **Language:** Vietnamese, English.
+
+## Quickstart
+
+```bash
+pip install transformers vllm accelerate
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "thanhdo881/qwen2.5-3b-vivu-travel-vn" 
+
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# 1. Prepare RAG Context & Query
+context = "Đà Lạt nằm trên cao nguyên Lâm Viên, nổi tiếng với khí hậu ôn đới và Hồ Xuân Hương."
+question = "Đà Lạt có những đặc điểm gì nổi bật?"
+prompt = f"Dựa vào thông tin sau:\n{context}\n\nHãy trả lời câu hỏi: {question}"
+
+# 2. Build Messages
+messages = [
+    {"role": "system", "content": "Bạn là ViVu, trợ lý du lịch Việt Nam. Chỉ trả lời dựa trên ngữ cảnh được cung cấp."},
+    {"role": "user", "content": prompt}
+]
+
+# 3. Generate
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.3, repetition_penalty=1.1)
+response = tokenizer.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0]
+
+print(response)