transformers safetensors llama text-generation te dataset:ravithejads/telugu_alpaca_ft autotrain_compatible endpoints_compatible text-generation-inference region:us
---
library_name: transformers
datasets:
- ravithejads/telugu_alpaca_ft
language:
- te
---

# Model Card for llama2_telugu

Welcome to the `eswardivi/llama2_telugu` model page on Hugging Face. This model is a result of fine-tuning the powerful `PosteriorAI/godavari-telugu-llama2-7B` model on the `ravithejads/telugu_alpaca_ft` dataset to better serve the Telugu-speaking community.

## Overview

- **Base Model**: `PosteriorAI/godavari-telugu-llama2-7B`, a state-of-the-art Telugu language model based on the LLaMA architecture, offering advanced natural language understanding and generation capabilities.
- **Fine-Tuning Dataset**: `ravithejads/telugu_alpaca_ft`, a curated dataset specifically designed to fine-tune language models for Telugu language tasks .
- **Target Application**: Enhanced communication, education, and technology access for the Telugu-speaking community, addressing the gap in AI for Indic languages .

## Usage

To use this model, you can leverage the Hugging Face API, SDK, or Transformers library. Below is a simple Python example using the Transformers library:

```python
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="eswardivi/llama2_telugu",
    device_map="auto",
    model_kwargs={"load_in_8bit": True}
)

def create_prompt(instruction: str, input: str = "") -> str:
    prompt = f"""
    You are a helpful assistant.
    ### Instruction:
    {instruction}

    ### Input:
    {input}

    ### Response:
    """
    return prompt

instruction = "Krindi samaacharam prakaram google news app eppudu release ayyindi?"
input = "Google News is a news aggregator service developed by Google. It presents a continuous flow of links to articles organized from thousands of publishers and magazines. Google News is available as an app on Android, iOS, and the Web. Google released a beta version in September 2002 and the official app in January 2006."

prompt = create_prompt(instruction, input)
print(prompt)
out = pipe(
    prompt,
    num_return_sequences=1,
    max_new_tokens=1024,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

print(out[0]['generated_text'])
```

## Training Details

This model was fine-tuned using Axolotl.


## Contributions

Contributions to this model are welcome. Feel free to submit issues, feature requests, or pull requests via Huggingface.

## Contact

For inquiries or collaborations, please contact the model maintainer at `eswar.divi.902@gmail.com`.

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