December 12, 2023

Creating Custom Elixir Copilots through Model Fine-Tuning

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Creating Custom Elixir Copilots through Model Fine-Tuning

Source: dockyard.com

Elixir programmers can harness the power of fine-tuned code completion models to enhance productivity and cater specifically to the nuances of their codebases. While pre-trained models offer generality, they may not always excel in every language ecosystem, especially ones with fewer examples like Elixir. By fine-tuning your own model, such as the 'deepseek-coder-1.3b-base' using Elixir's machine learning libraries like Axon and Bumblebee, and leveraging hardware like Fly GPUs, you gain not only better contextual understanding for your codebase but also the viability of using the model in proprietary environments. The process involves setting up a functional development environment, collecting a dataset, applying the Fill-In-Middle (FIM) task for training, and finally, executing the fine-tuning using a coded training loop. This approach, although experimental and with scope for enhancement, showcases the capabilities within the Elixir machine learning ecosystem and opens up opportunities for more language-specific tooling.

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