MISHANM/telugu_langmodel_Translator_Llama-3.2-3B-Instruct
Model Details
This model is based on meta-llama/Llama-3.2-3B-Instruct and has been LoRA finetuned on telugu dataset
Training Details
The model is trained on approx 29K instruction samples.
- GPU: AMD Instinct MI210
- Training Time: 4 Hours
Inference with HuggingFace
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Set the device
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load the fine-tuned model and tokenizer
model_path = "MISHANM/telugu_langmodel_Translator_Llama-3.2-3B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_path)
# Wrap the model with DataParallel if multiple GPUs are available
if torch.cuda.device_count() > 1:
print(f"Using {torch.cuda.device_count()} GPUs")
model = torch.nn.DataParallel(model)
# Move the model to the appropriate device
model.to(device)
tokenizer = AutoTokenizer.from_pretrained(model_path)
# Function to generate text
def generate_text(prompt, max_length=1000, temperature=0.9):
# Format the prompt according to the chat template
messages = [
{
"role": "system",
"content": "You are a telugu language expert and linguist, with same knowledge give response in telugu 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").to(device)
output = model.module.generate( # Use model.module for DataParallel
**inputs, max_new_tokens=max_length, temperature=temperature, do_sample=True
)
return tokenizer.decode(output[0], skip_special_tokens=True)
# Example usage
prompt = """One morning the busy doctor invited Sue into the hallway with a shaggy, gray eyebrow. ."""
translated_text = generate_text(prompt)
print(translated_text)
Citation Information
@misc{MISHANM/telugu_langmodel_Translator_Llama-3.2-3B-Instruct,
author = {Mishan Maurya},
title = {Introducing Fine Tuned LLM for Telugu Language},
year = {2024},
publisher = {Hugging Face},
journal = {Hugging Face repository},
}
Model tree for MISHANM/telugu_langmodel_Translator_Llama-3.2-3B-Instruct
Base model
meta-llama/Llama-3.2-3B-Instruct