MISHANM/Cantonese_eng_text_generation_Llama3_8B_instruction

This model has undergone meticulous fine-tuning for Cantonese language compatibility. It is equipped to handle question-answering and translation tasks between English and Cantonese. Leveraging sophisticated natural language processing methodologies, it delivers precise and context-sensitive responses, ensuring a comprehensive grasp of Cantonese nuances. Consequently, its outputs are dependable and pertinent across various scenarios.

Model Details

1. Language: Cantonese
2. Tasks: Question Answering(Cantonese to Cantonese) , Translation (Tibetan to Cantonese)
3. Base Model: meta-llama/Meta-Llama-3-8B-Instruct

Training Details

The model is trained on approx 109,942 instruction samples.

1. GPUs: 4*AMD Radeon™ PRO V620
2. Training Time: 61:07:36

Inference with HuggingFace

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the fine-tuned model and tokenizer
model_path = "MISHANM/Cantonese_eng_text_generation_Llama3_8B_instruction"

model = AutoModelForCausalLM.from_pretrained(model_path,device_map="auto")

tokenizer = AutoTokenizer.from_pretrained(model_path)

# Function to generate text
def generate_text(prompt, max_length=500, temperature=0.9):
   # Format the prompt according to the chat template
   messages = [
       {
           "role": "system",
           "content": "You are a Cantonese language expert and linguist, with same knowledge give response in Cantonese language.",
       },
       {"role": "user", "content": prompt}
   ]

   # Apply the chat template
   formatted_prompt = f"<|system|>{messages[0]['content']}<|user|>{messages[1]['content']}<|assistant|>"

   # Tokenize and generate output
   inputs = tokenizer(formatted_prompt, return_tensors="pt")
   output = model.generate(  
       **inputs, max_new_tokens=max_length, temperature=temperature, do_sample=True
   )
   return tokenizer.decode(output[0], skip_special_tokens=True)

# Example usage
prompt = """佢日日搭的士出入，好似幾百萬未開頭噉。"""
translated_text = generate_text(prompt)
print(translated_text)

Citation Information

@misc{MISHANM/Cantonese_eng_text_generation_Llama3_8B_instruction,
  author = {Mishan Maurya},
  title = {Introducing Fine Tuned LLM for Cantonese Language},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  
}

• PEFT 0.12.0