README.md

---
base_model: viet-mistral/vinallama-2.7b-chat
library_name: peft
license: apache-2.0
language:
- vi
metrics:
- accuracy
- perplexity
pipeline_tag: text-generation
tags:
- code
- sft
- chat
- vietnamese
---
# Model Card for 522H0134-NguyenNhatHuy/Vinallama-2.7b-chat-SFT

This model is a fine-tuned version of **viet-mistral/vinallama-2.7b-chat** using **LoRA + PEFT**, targeting Vietnamese open-domain, instruction-following chat. It is aligned for **safe, helpful, and fluent conversations** in Vietnamese through supervised fine-tuning on high-quality prompt-response pairs.

---

## 🧠 Model Details

- **Base Model:** viet-mistral/vinallama-2.7b-chat
- **Model Type:** Causal Language Model (Chat)
- **Languages:** Vietnamese
- **License:** Apache 2.0
- **Fine-tuning Framework:** [PEFT](https://github.com/huggingface/peft) with LoRA
- **Training Dataset:** Custom Vietnamese SFT & DPO dataset (~10K SFT + 10K DPO + 1K test prompts)

---

## ✅ Intended Uses

### Direct Use
- Vietnamese open-domain dialogue
- Instruction-following tasks
- Educational or research-based QA

### Out-of-Scope Use
- Medical, legal, or financial advice
- Content moderation or safety-critical tasks
- English-centric prompts

---

## 🧪 Evaluation

### Test Data
The model was evaluated on a Vietnamese test set of **1,000 prompts** (60% safe / 40% adversarial) adapted from JailBreak, HarmBench, and OpenAssistant.

### Metrics
- **Helpfulness**
- **Toxicity (via Detoxify > 0.5)**
- **Appropriateness / Safety Rejection**

> Detoxify was used to filter harmful generations during evaluation.

### Summary
- 74% of generations were rated safe/aligned
- 86% rejection rate on highly toxic or adversarial prompts
- The model avoids unsafe completions better than its base model

---

## 🚀 How to Use the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model and LoRA adapter
tokenizer = AutoTokenizer.from_pretrained("viet-mistral/vinallama-2.7b-chat")
base_model = AutoModelForCausalLM.from_pretrained("viet-mistral/vinallama-2.7b-chat")
model = PeftModel.from_pretrained(base_model, "522H0134-NguyenNhatHuy/Vinallama-2.7b-chat-SFT")

# Chat example
prompt = "Xin chào, bạn có thể giúp tôi học tiếng Anh không?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))