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---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
library_name: peft
---
# MISHANM/Sindhi_text_generation_Llama3_8B_instruct
This model is fine-tuned for the Sindhi language, capable of answering queries and translating text Between English and Sindhi . It leverages advanced natural language processing techniques to provide accurate and context-aware responses.
## Model Details
1. Language: Sindhi
2. Tasks: Question Answering, Translation (English to Sindhi )
3. Base Model: meta-llama/Meta-Llama-3-8B-Instruct
# Training Details
The model is trained on approx 29K instruction samples.
1. GPUs: 2*AMD Instinct™ MI210 Accelerators
## Inference with HuggingFace
```python3
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the fine-tuned model and tokenizer
model_path = "MISHANM/Sindhi_text_generation_Llama3_8B_instruct"
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 Sindhi language expert and linguist, with same knowledge give response in Sindhi 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 = """Write a short note on LLM ."""
translated_text = generate_text(prompt)
print(translated_text)
```
## Citation Information
```
@misc{MISHANM/Sindhi_text_generation_Llama3_8B_instruct,
author = {Mishan Maurya},
title = {Introducing Fine Tuned LLM for Sindhi Language},
year = {2024},
publisher = {Hugging Face},
journal = {Hugging Face repository},
}
```
- PEFT 0.12.0 |