starcoder2-15b
Maintainer: bigcode
505
📊
| 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
The starcoder2-15b model is a 15B parameter model trained on 600+ programming languages from the The Stack v2 dataset, with opt-out requests excluded. The model uses Grouped Query Attention, a context window of 16,384 tokens with a sliding window attention of 4,096 tokens, and was trained using the Fill-in-the-Middle objective on 4+ trillion tokens. The model was trained using the NVIDIA NeMo Framework on the NVIDIA Eos Supercomputer built with NVIDIA DGX H100 systems.
The starcoder2-15b model is an evolution of the earlier StarCoder model, which was a 15.5B parameter model trained on 80+ programming languages. Both models were developed by the BigCode team.
Model inputs and outputs
Inputs
- Text prompts in any of the 600+ programming languages the model was trained on
Outputs
- Generated code in response to the input prompt
Capabilities
The starcoder2-15b model is capable of generating code in a wide variety of programming languages. It can be used for tasks like code completion, code generation, and even open-ended programming challenges. The model's large size and extensive training data allow it to handle complex programming concepts and idioms across many languages.
What can I use it for?
The starcoder2-15b model could be useful for a variety of applications, such as:
- Building programming assistants to help developers write code more efficiently
- Generating example code snippets for educational or documentation purposes
- Prototyping new ideas and quickly iterating on code-based projects
- Integrating code generation capabilities into no-code or low-code platforms
Things to try
One interesting aspect of the starcoder2-15b model is its ability to handle long-form context. By training on a 16,384 token context window, the model can generate code that is coherent and consistent over a large number of lines. You could try providing the model with a partially completed function or class definition and see if it can generate the remaining implementation.
Another interesting experiment would be to fine-tune the starcoder2-15b model on a specific programming language or domain-specific dataset. This could allow the model to develop specialized knowledge and skills tailored to your particular use case.
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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