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--- |
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title: Kaggle Q&A Gemma Model |
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tags: |
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- autotrain |
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- kaggle-qa |
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- text-generation |
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- peft |
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datasets: |
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- custom |
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library_name: transformers |
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widget: |
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- messages: |
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- role: user |
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content: How do I submit to a Kaggle competition? |
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license: other |
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--- |
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## Overview |
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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. |
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## Key Features |
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- **Kaggle-Specific Knowledge**: Designed to offer insights and guidance on using Kaggle, from competition submissions to data exploration. |
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- **Powered by AutoTrain**: Utilizes Hugging Face's AutoTrain for efficient and effective training, ensuring high-quality responses. |
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- **PEFT Enhanced**: Benefits from PEFT for improved performance and efficiency, making it highly scalable and robust. |
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## Usage |
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The following Python code snippet illustrates how to use this model to answer your Kaggle-related questions: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_path = "theoracle/autotrain-kaggle" |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, |
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device_map="auto", |
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torch_dtype='auto' |
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).eval() |
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tokenizer.pad_token = tokenizer.eos_token |
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prompt = ''' |
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### How do I prepare for Kaggle competitions?\n ### Answer: |
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''' |
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encoding = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True, max_length=500, add_special_tokens=True) |
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input_ids = encoding['input_ids'] |
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attention_mask = encoding['attention_mask'] |
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output_ids = model.generate( |
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input_ids.to('cuda'), |
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attention_mask=attention_mask.to('cuda'), |
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max_new_tokens=300, |
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pad_token_id=tokenizer.eos_token_id |
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) |
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response = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
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print(response) |
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``` |
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## Application Scenarios |
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This model is particularly useful for: |
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- Kaggle competitors seeking advice on strategy and submissions. |
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- Educators and students looking for a tool to facilitate learning through Kaggle competitions. |
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- Data scientists requiring quick access to information about Kaggle datasets and competitions. |
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## About AutoTrain and PEFT |
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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. |
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## License |
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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. |
