replit-code-v1_5-3b
Maintainer: replit
279
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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
replit-code-v1_5-3b is a 3.3 billion parameter Causal Language Model developed by Replit, Inc. that is focused on code completion. Compared to similar models like replit-code-v1-3b and stable-code-3b, replit-code-v1_5-3b has been trained on a broader set of 30 programming languages and uses a custom trained vocabulary optimized for improved compression and coverage.
Model inputs and outputs
replit-code-v1_5-3b takes text as input and generates text as output. The model can be used to complete partially written code snippets, generate new code, or continue existing code. The context size of the model is 4096 tokens, which allows it to consider a sizable amount of context when generating new text.
Inputs
- Partial code snippets or text prompts
Outputs
- Completed code snippets
- Generated code in one of the 30 supported programming languages
Capabilities
replit-code-v1_5-3b demonstrates strong performance on a variety of coding tasks, from completing simple function definitions to generating more complex program logic. It can be particularly helpful for tasks like filling in missing parts of code, expanding on high-level ideas, and generating boilerplate code. The model's broad language support also makes it a versatile tool for developers working across different programming environments.
What can I use it for?
Developers can use replit-code-v1_5-3b as a foundational model for building a variety of applications that require code generation or completion, such as intelligent code editors, programming assistants, or even low-code/no-code platforms. The model's capabilities could be further enhanced through fine-tuning on domain-specific data or integrating it with other tools and workflows.
Things to try
Experiment with different decoding techniques and parameters, such as adjusting the temperature, top-k, and top-p values, to see how they impact the quality and diversity of the generated code. You can also try prompting the model with high-level descriptions of functionality and see how it translates those into working code. Additionally, exploring the model's performance across the 30 supported languages could yield interesting insights.
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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