Vinallama-2.7b-chat-SFT

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.

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🧠 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 with LoRA
• Training Dataset: Custom Vietnamese SFT & DPO dataset (~10K SFT + 10K DPO + 1K test prompts)

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✅ 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

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🧪 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

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🚀 How to Use the Model

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))