| --- |
| 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)) |
| |
