DeciCoder-1b
Maintainer: Deci
246
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model Overview
DeciCoder-1b is a 1 billion parameter decoder-only code completion model developed by Deci. It was trained on the Python, Java, and Javascript subsets of the Starcoder Training Dataset. The model uses Grouped Query Attention and has a context window of 2048 tokens. It was trained using a Fill-in-the-Middle training objective.
The DeciCoder-1b model can be compared to similar code generation models like starcoder2-15b, starcoder, starcoderbase, and stable-code-3b. These models share capabilities around code generation, completion, and understanding, though they differ in their specific architectures, training data, and performance characteristics.
Model Inputs and Outputs
The DeciCoder-1b model is a text-to-text model, taking in textual prompts as input and generating continuations or completions as output.
Inputs
- Textual prompts related to code, such as function signatures, comments, or partial code snippets.
Outputs
- Continuations or completions of the input code, generated in an auto-regressive manner.
- The model can generate single or multi-line code completions based on the provided context.
Capabilities
The DeciCoder-1b model is capable of generating coherent and context-appropriate code completions for common programming languages like Python, Java, and JavaScript. It can leverage the provided context to continue or complete a code snippet in a sensible way, though the generated code may not always be fully correct or optimal.
What Can I Use it For?
The DeciCoder-1b model can be a useful tool for developers working on code-related tasks. Some potential use cases include:
- Code completion and suggestion during programming to boost productivity
- Generating boilerplate code or code templates based on a high-level description
- Prototyping new features or algorithms by providing a starting prompt
- Exploring novel code ideas by iterating on generated outputs
However, it's important to note that the generated code may not always be reliable or production-ready, and should be thoroughly tested and validated before deployment.
Things to Try
One interesting aspect of the DeciCoder-1b model is its ability to perform "fill-in-the-middle" generation. This allows you to provide a partial code snippet with placeholders, and have the model generate the missing middle portion. This can be a useful technique for exploring different ways to implement a specific logic or algorithm.
Another interesting experiment would be to compare the performance of DeciCoder-1b to other similar models like starcoder2-15b or stable-code-3b on specific coding tasks or benchmarks. This could help you understand the relative strengths and weaknesses of the different models.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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