Upload README.md

README.md

@@ -1,3 +1,145 @@
----
-license: llama3.1
----
+---
+license: llama3.1
+language:
+- en
+- pa
+metrics:
+- chrf
+base_model:
+- meta-llama/Meta-Llama-3.1-8B
+pipeline_tag: translation
+tags:
+- text-2-text translation
+- English2Punjabi
+---
+# 🦙📝 LLAMA-VaaniSetu-EN2PA: English to Punjabi Translation with Large Language Models
+
+### Overview
+
+This model, **LLAMA-VaaniSetu-EN2PA**, is a fine-tuned version of the LLaMA 3.1 8B architecture model, designed specifically for **English to Punjabi translation**. The model has been trained using the **Bharat Parallel Corpus Collection (BPCC)**, which contains around **10 million English<>Punjabi pairs**. The BPCC has been made available by [AI4Bharat](https://github.com/AI4Bharat/IndicTrans2).
+
+This model aims to bridge the gap in **open-source English to Punjabi translation models**, with potential applications in translating judicial documents, government orders, court judgments, and other documents to cater to Punjabi-speaking masses.
+
+### Model and Data Information
+
+- **Training Data**: 10 million English<>Punjabi parallel sentences from [AI4Bharat's Bharat Parallel Corpus Collection (BPCC)](https://github.com/AI4Bharat/IndicTrans2).
+- **Evaluation Data**: The model has been evaluated on **1503 samples** from the **IN22-Conv dataset**, which is also available via [IndicTrans2](https://github.com/AI4Bharat/IndicTrans2).
+- **Model Architecture**: Based on **LLaMA 3.1 8B** with BF16 precision.
+- **Score (chrF++)**: Achieved a **chrF++ score of 28.1** on the IN22-Conv dataset, which is an excellent score for an open-source model. The benchmark chrF++ score for Google Translate is 61.1 (as noted in [this paper](https://arxiv.org/pdf/2305.16307)).
+
+This is the **first release** of the model, and future updates aim to improve the chrF++ score for enhanced translation quality.
+
+### GPU Requirements for Inference
+
+To perform inference with this model, here are the **minimum GPU requirements**:
+- **Memory Requirements**: 16-18 GB of VRAM for inference in **BF16 (BFloat16)** precision.
+- **Recommended GPUs**:
+  - **NVIDIA A100 (20GB)**: Ideal for BF16 precision and efficiently handles large models like LLaMA 8B.
+  - Other GPUs with **at least 16 GB VRAM** may also work, but performance may vary based on memory availability.
+
+## Requirements
+
+- Python 3.8.10 or above
+- Required Python packages:
+  - `transformers`
+  - `torch`
+  - `huggingface_hub`
+
+### Installation Instructions
+
+To use this model, ensure you have the following dependencies installed:
+
+```bash
+pip install torch transformers huggingface_hub
+```
+
+### Model Usage Example
+
+Here's an example of how to load and use the **LLAMA-VaaniSetu-EN2PA** model for **English to Punjabi translation**:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+
+# Load model and tokenizer
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("rohitanurag/Llama-3.1-8B-VaaniSetu-EN2PA", use_auth_token=HF_TOKEN)
+    model = AutoModelForCausalLM.from_pretrained(
+        "rohitanurag/Llama-3.1-8B-VaaniSetu-EN2PA",
+        torch_dtype=torch.bfloat16,
+        device_map="auto",  # Automatically moves model to GPU
+    )
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+# Define the function for translation
+# Define the function for translation which translated from English to Punjabi
+def translate_to_punjabi(english_text):
+    # Create the  prompt
+    translate_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+    
+    ### Instruction:
+    {}
+    
+    ### Input:
+    {}
+    
+    ### Response:
+    {}"""
+    
+    # Format the prompt
+    formatted_input = translate_prompt.format(
+        "You are given the english text, read it and understand it. After reading translate the english text to Punjabi and provide the output strictly",  # Instruction
+        english_text,  # Input text to be translated
+        ""  # Output - leave blank for generation
+    )
+    
+    # Tokenize the input
+    inputs = tokenizer([formatted_input], return_tensors="pt").to("cuda")
+
+    # Generate the translation output
+    output_ids = model.generate(**inputs, max_new_tokens=500)
+
+    # Decode the output
+    translated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    fulloutput = translated_text.split("Response:")[-1].strip()
+    if not fulloutput:
+        fulloutput = ""
+    return fulloutput
+
+
+english_text = """
+Delhi is a beautiful place
+"""
+
+punjabi_translation = translate_to_punjabi(english_text)
+
+print(punjabi_translation)
+```
+
+### Notes
+
+- The translation function is designed to handle **English to Punjabi** translations. You can use this for various applications, such as translating judicial documents, government orders, and other documents into Punjabi.
+  
+### Performance and Future Work
+
+As this is the **first release** of the **LLAMA-VaaniSetu-EN2PA** model, there is room for improvement, particularly in increasing the chrF++ score. Future versions of the model will focus on optimizing performance, enhancing the translation quality, and expanding to additional domains.
+
+Stay tuned for updates, and feel free to contribute or raise issues on Hugging Face or the associated repositories!
+
+### Resources
+
+- **Training Data**: [Bharat Parallel Corpus Collection (BPCC)](https://github.com/AI4Bharat/IndicTrans2) by AI4Bharat.
+- **Evaluation Data**: [IN22-Conv dataset](https://github.com/AI4Bharat/IndicTrans2).
+- **Benchmarks**: [Translation Benchmarks Paper](https://arxiv.org/pdf/2305.16307).
+
+## Acknowledgements
+
+- [AI4Bharat](https://github.com/AI4Bharat/IndicTrans2): The training and evaluation data we took from.
+
+
+### License
+
+This model is licensed under the appropriate terms for the **LLaMA** architecture and any datasets used during fine-tuning.