README.md

---
title: Kaggle Q&A Gemma Model
tags:
- autotrain
- kaggle-qa
- text-generation
- peft
datasets:
- custom
library_name: transformers
widget:
  - messages:
      - role: user
        content: How do I submit to a Kaggle competition?
license: other
---

## Overview

Developed with the cutting-edge AutoTrain and PEFT technologies, this model is specifically trained to provide detailed answers to questions about Kaggle. Whether you're wondering how to get started, how to submit to a competition, or how to navigate the datasets, this model is equipped to assist.

## Key Features

- **Kaggle-Specific Knowledge**: Designed to offer insights and guidance on using Kaggle, from competition submissions to data exploration.
- **Powered by AutoTrain**: Utilizes Hugging Face's AutoTrain for efficient and effective training, ensuring high-quality responses.
- **PEFT Enhanced**: Benefits from PEFT for improved performance and efficiency, making it highly scalable and robust.

## Usage

The following Python code snippet illustrates how to use this model to answer your Kaggle-related questions:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "theoracle/autotrain-kaggle"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()

tokenizer.pad_token = tokenizer.eos_token

prompt = '''
### How do I prepare for Kaggle competitions?\n ### Answer: 
'''

encoding = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True, max_length=500, add_special_tokens=True)
input_ids = encoding['input_ids']
attention_mask = encoding['attention_mask']

output_ids = model.generate(
    input_ids.to('cuda'),
    attention_mask=attention_mask.to('cuda'),
    max_new_tokens=300,
    pad_token_id=tokenizer.eos_token_id
)

response = tokenizer.decode(output_ids[0], skip_special_tokens=True)

print(response)
```

## Application Scenarios

This model is particularly useful for:
- Kaggle competitors seeking advice on strategy and submissions.
- Educators and students looking for a tool to facilitate learning through Kaggle competitions.
- Data scientists requiring quick access to information about Kaggle datasets and competitions.

## About AutoTrain and PEFT

AutoTrain by Hugging Face streamlines the model training process, making it easier and more efficient to develop state-of-the-art models. PEFT enhances this by providing a framework for efficient model training and deployment. Together, they enable this model to deliver fast and accurate responses to your Kaggle inquiries.

## License

This model is distributed under an "other" license, allowing diverse applications while encouraging users to review the license terms for compliance with their project requirements.