Downloads · 30 days
17
19% of all-time downloads
MISHANM/Tamil_NLP_text_generation_Llama3_8B_instruction
Tamil_NLP_text_generation_Llama3_8B_instruction is a machine learning model from MISHANM. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft.
This model is fine-tuned for the Tamil language, capable of answering queries and translating text Between English and Tamil . It leverages advanced natural language processing techniques to provide accurate and conte…
Downloads · 30 days
17
19% of all-time downloads
All-time downloads
90
Public
Repo size
185 MB
Likes
0
Public
Click a slice to open those files.
.safetensors168 MB · 91%
From the Hugging Face model README
This model is fine-tuned for the Tamil language, capable of answering queries and translating text Between English and Tamil . It leverages advanced natural language processing techniques to provide accurate and context-aware responses.
The model is trained on approx 52K instruction samples.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the fine-tuned model and tokenizer
model_path = "MISHANM/Tamil_NLP_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=1000, temperature=0.9):
# Format the prompt according to the chat template
messages = [
{
"role": "system",
"content": "You are a Tamil language expert and linguist, with same knowledge give response in Tamil 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( # 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 = """What is LLM ."""
translated_text = generate_text(prompt)
print(translated_text)
@misc{MISHANM/Tamil_NLP_text_generation_Llama3_8B_instruction,
author = {Mishan Maurya},
title = {Introducing Fine Tuned LLM for Tamil Language},
year = {2024},
publisher = {Hugging Face},
journal = {Hugging Face repository},
}