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--- |
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language: en |
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tags: |
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- pytorch |
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- gpt2 |
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- text-generation |
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license: mit |
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datasets: |
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- Skylion007/openwebtext |
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model-index: |
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- name: chatMachineProto |
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results: [] |
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--- |
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# NanoGPT Personal Experiment |
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This repository contains my personal experiment with training and fine-tuning a GPT-2 style language model. This project was undertaken as a learning exercise to understand transformer-based language models and explore the capabilities of modern AI architectures. |
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## Model Description |
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This model is based on the nanoGPT implementation, which is a minimal, clean implementation of GPT-2 style models. The architecture follows the original GPT-2 design principles while being more accessible and easier to understand. |
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### Technical Details |
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- Base Architecture: GPT-2 |
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- Training Infrastructure: 8x A100 80GB GPUs |
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- Parameters: ~124M (similar to GPT-2 small) |
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### Training Process |
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The model underwent a multi-stage training process: |
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- Initial training on a subset of the OpenWebText dataset |
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- Experimentation with different hyperparameters and optimization techniques |
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### Features |
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- Clean, minimal implementation of the GPT architecture |
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- Efficient training utilizing modern GPU capabilities |
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- Configurable generation parameters (temperature, top-k sampling) |
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- Support for both direct text generation and interactive chat |
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## Use Cases |
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This model is primarily an experimental project and can be used for: |
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- Educational purposes to understand transformer architectures |
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- Text generation experiments |
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- Research into language model behavior |
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- Interactive chat experiments |
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## Limitations |
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As this is a personal experiment, please note: |
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- The model may produce inconsistent or incorrect outputs |
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- It's not intended for production use |
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- Responses may be unpredictable or contain biases |
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- Performance may vary significantly depending on the input |
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## Development Context |
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This project was developed as part of my personal exploration into AI/ML, specifically focusing on: |
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- Understanding transformer architectures |
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- Learning about large-scale model training |
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- Experimenting with different training approaches |
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- Gaining hands-on experience with modern AI infrastructure |
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## Acknowledgments |
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This project builds upon the excellent work of: |
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- The original GPT-2 paper by OpenAI |
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- The nanoGPT implementation by Andrej Karpathy |
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- The broader open-source AI community |
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## Disclaimer |
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This is a personal experimental project and should be treated as such. It's not intended for production use or as a replacement for more established language models. The primary goal was learning and experimentation. |
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--- |
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Feel free to explore the model and provide feedback. Remember that this is an experimental project, and results may vary significantly from more established models. |
