Denglish-8B-Instruct is a fine-tuned LoRA adapter based on unsloth/llama-3-8b-Instruct-bnb-4bit. It is specifically designed to act as a strict yet friendly AI Language Tutor, assisting Vietnamese students in learning English and German.

This model excels at identifying grammatical, spelling, and contextual errors from user inputs, explaining the mistakes clearly in Vietnamese, and providing perfectly corrected sentences in the target language.

🚀 Model Details

Model Type: Causal Language Model (Fine-tuned LoRA Adapter)
Base Model: Meta Llama 3 (8B-Instruct 4-bit quantized via Unsloth)
Primary Languages: Vietnamese (Explanations), English (Target), German (Target)
Training Framework: TRL (Transformer Reinforcement Learning) & PEFT
Architecture: Optimized for multi-modal integrations (Text, OCR/Images, and STT/Voice processing ecosystems).

💡 Intended Uses & Ecosystem

This model is the core "Brain" of the Denglish Omnichannel Platform (integrated via RunPod Serverless, FastAPI, Telegram, and Facebook Messenger). It is intended to process inputs such as:

Direct Text: User types a sentence in English or German.
Transcribed Audio (Whisper STT): Correcting conversational mistakes from spoken language.
Extracted Text from Images (OCR): Grading handwritten or printed homework.
🗣️ Face-to-Face Speaking Practice: Role-play as a native speaker, engage in natural conversations, and receive direct pronunciation/grammar correction.
📝 Test Generation: Automatically generate tests from A1 to C2 levels as required.
👩‍🏫 Strict Grading: Evaluate the work, provide detailed scoring, point out errors, and provide correct answers.
🌐 Trilingual: Understand and respond flexibly between Vietnamese, English, and German.

🛠️ How to Use (Quick Start)

Since this is a LoRA adapter, you need to load the base model first and then merge it with this adapter using peft.

Prerequisites

pip install torch transformers peft accelerate bitsandbytes

Inference Code

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# 1. Define the Model ID
base_model_id = "unsloth/llama-3-8b-Instruct-bnb-4bit"
lora_model_id = "phgrouptechs/Denglish-8B-Instruct"

print("Loading Tokenizer and Base Model...")
tokenizer = AutoTokenizer.from_pretrained(base_model_id)

# Load Base Model (Use bfloat16 for optimization on newer GPUs.)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

print("Currently using the English LoRA Adapter....")
# Kết hợp LoRA vào Base Model
model = PeftModel.from_pretrained(base_model, lora_model_id)

# 2. Building a Prompt (Using the Llama 3 Chat Template)
system_prompt = """You are Denglish AI - an AI specializing in face-to-face English speaking practice for Vietnamese learners.

YOUR RESPONSIBILITIES:
1. Provide concise and natural responses.

2. If the user makes a mistake, point out the weakness in Vietnamese.

3. Always end with an open-ended question."""

user_message = "Hello teacher, my name is Hoang. I want learn English today."

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_message}
]

# Prepare input data
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([prompt], return_tensors="pt").to("cuda")

print("AI is thinking...")
# 3.Generate text (Generate)
with torch.no_grad():
    outputs = model.generate(
        **inputs, 
        max_new_tokens=512, 
        temperature=0.4, 
        top_p=0.9,
        pad_token_id=tokenizer.eos_token_id
    )

# 4. Print results
response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
print("\n--- DENGLISH AI'S RESPONSE ---")
print(response.strip())

📝 Example Output

Example 1

Input (User): "Hello, my name is John and I is a student." Target Language: English

Output (AI):

Máy phát hiện lỗi sử dụng động từ to be. Chủ ngữ "I" phải đi với "am" thay vì "is".

Câu đúng: "Hello, my name is John and I am a student."

Example 2

Input (User): "Give me a test." Target Language: German Target Count: 1 Target Level: A1

Output Text (AI): Hãy viết lại câu tiếng Đức sau đây theo một cách khác nhưng giữ nguyên ý nghĩa:\nInput:\nIch bin so froh, dass du da bist.

Output Audio (AI):

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Training procedure

Visualize in Weights & Biases

This model was trained with SFT.

⚠️ Limitations

Quantization Constraints: The base model is 4-bit quantized. While it is highly efficient, extremely complex logical reasoning might be slightly degraded compared to the FP16 base model.
Language Scope: The model is highly optimized for English/German to Vietnamese explanations. Using it for other language pairs might yield suboptimal results.

Framework versions

PEFT 0.18.1
TRL: 0.29.0
Transformers: 5.2.0
Pytorch: 2.8.0+cu128
Datasets: 4.6.0
Tokenizers: 0.22.2

👨‍💻 Developed by

PHGROUP TECHNOLOGY SOLUTIONS CO., LTD - Building AI-driven educational and omnichannel solutions.