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--- |
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license: apache-2.0 |
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tags: |
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- code |
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- copilot |
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--- |
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# Model Card: GTamaraw Coder LLM |
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## Model Overview |
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**Model Name:** GTamaraw Coder LLM |
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**Version:** 1.0 |
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**Architecture:** DeciCoder-1b |
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**Trained on:** 1 billion models |
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**Fine-tuning Method:** PEFT (Parameter-Efficient Fine-Tuning) |
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**Languages:** Extended support for HTML, Go, Python, Java, Perl, Swift, and more. |
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## Model Description |
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GTamaraw Coder LLM is a language model developed by Finch Research and G2WP. It is a specialized version of the DeciCoder architecture, fine-tuned using the Parameter-Efficient Fine-Tuning (PEFT) technique. This model is designed to assist with a wide range of coding tasks, offering improved performance in various programming languages, including HTML, Go, Python, Java, Perl, Swift, and more. |
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## Model Training |
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The model was trained on a diverse and synthetic dataset containing code snippets from various programming languages. The training process involved generating a dataset using AI methods and then formatting it for fine-tuning. With a training corpus of 1 billion models, GTamaraw learned to understand and generate code in multiple programming languages, providing enhanced capabilities compared to its base architecture. |
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## Ethical Considerations |
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GTamaraw Coder LLM is a tool designed to aid programmers and developers in their coding tasks. However, like any AI model, there are ethical considerations to be aware of: |
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- **Bias:** The model's responses may inadvertently reflect biases present in the training data. Care should be taken to ensure fairness and inclusivity in its outputs. |
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- **Accuracy:** While the model is trained on a vast amount of data, it may still produce incorrect or nonsensical code snippets. It's important for users to review and validate its outputs. |
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- **Data Privacy:** The synthetic dataset used for training may contain fragments of real code. Efforts have been made to ensure data privacy, but sensitive information might inadvertently appear in generated outputs. |
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## Limitations |
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- GTamaraw's responses are based on patterns in the data it was trained on. It may not have a deep understanding of complex coding concepts and contexts. |
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- The model might generate code that compiles and runs, but may not follow best practices or be optimal in terms of performance. |
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- GTamaraw might struggle with generating code for very specific or niche programming languages or tasks not well-represented in its training data. |
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## How to Use |
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Developers and programmers can interact with GTamaraw Coder LLM through API integrations provided by Finch Research and G2WP. Users can input coding prompts or questions and receive code snippets as outputs. It's recommended to thoroughly review and test the generated code before integrating it into actual projects. |
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## Acknowledgments |
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GTamaraw Coder LLM is a product of collaborative efforts by Finch Research and G2WP. It is presented as a tribute to Buwan ng Wika, celebrating Filipino culture and achievements in technology. |
