README.md

---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
library_name: peft
datasets:
- Tensoic/Alpaca-Gujarati
- Tensoic/airoboros-3.2_kn
- ravithejads/samvaad-hi-filtered
- HydraIndicLM/hindi_alpaca_dolly_67k
- OdiaGenAI/Odia_Alpaca_instructions_52k
- OdiaGenAI/gpt-teacher-roleplay-odia-3k
- HydraIndicLM/punjabi_alpaca_52K
- HydraIndicLM/bengali_alpaca_dolly_67k
- abhinand/tamil-alpaca
- Telugu-LLM-Labs/telugu_alpaca_yahma_cleaned_filtered_romanized
---

# MISHANM/Multilingual_Llama-3-8B-Instruct

This model is fine-tuned for Multi languages , capable of answering queries and translating text from English to Multiple languages . It leverages advanced natural language processing techniques to provide accurate and context-aware responses.


## Model Details
This model is based on meta-llama/Llama-3.2-3B-Instruct and has been LoRA finetuned on Multi language datasets:
1. Gujarati
2. Kannada
3. Hindi
4. Odia
5. Punjabi
6. Bengali
7. Tamil
8. Telugu 



# Training Details

The model is trained on approx 321K instruction samples.
1. GPUs: 2*AMD Instinct™ MI210 Accelerators
  
   


 ## Inference with HuggingFace
 ```python3
 
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/Multilingual_Llama-3-8B-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 language expert and linguist, with same knowledge give response in ().", #In place of "()" write your desired language in which response is required. ",
        },
        {"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 = """Write a story about LLM ."""
translated_text = generate_text(prompt)
print(translated_text)


```

## Citation Information
```
@misc{MISHANM/Multilingual_Llama-3-8B-Instruct,
  author = {Mishan Maurya},
  title = {Introducing Fine Tuned LLM for Indic Languages},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  
}
```


- PEFT 0.12.0