CrystalCoder

Maintainer: LLM360

Total Score

66

Last updated 5/17/2024

🏷️

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

CrystalCoder is a 7B parameter language model developed by LLM360, a project focused on comprehensive and open-sourced large language models. CrystalCoder is distinctively trained on the SlimPajama and StarCoder datasets, allowing it to excel at balancing natural language processing and coding capabilities. Despite being trained on a smaller dataset of 1.4 trillion tokens compared to LLaMA 2's 2 trillion, CrystalCoder surpasses LLaMA 2 in some challenging English and coding tasks, demonstrating superior performance in benchmarks like MMLU, HumanEval, and MBPP.

Model inputs and outputs

CrystalCoder is a text-to-text model, capable of generating natural language and code based on the provided input prompt. The model supports a wide range of inputs, including natural language instructions, code snippets, and prompts that combine both.

Inputs

  • Natural language: Prompts in English or other languages for tasks like code generation, summarization, or translation.
  • Code snippets: Partial code that the model can complete or expand upon.
  • Mixed prompts: Prompts that combine natural language and code, such as "Write a function in Python that calculates the factorial of a number."

Outputs

  • Generated text: The model can produce natural language, such as explanations, summaries, or responses to queries.
  • Generated code: The model can generate code in a variety of programming languages, including but not limited to Python, C++, Java, and JavaScript.

Capabilities

CrystalCoder's key strength is its ability to excel at both natural language processing and coding tasks. For example, the model can generate high-quality code snippets in response to natural language prompts, or provide clear and concise explanations of code. This balance is reflected in its strong performance on benchmarks like MMLU, HumanEval, and MBPP, where it outperforms larger models like LLaMA 2.

What can I use it for?

CrystalCoder can be a powerful tool for a variety of applications, such as:

  • Code generation: Automatically generate code in multiple programming languages based on natural language descriptions or partial code.
  • Code explanation and documentation: Summarize and explain code snippets in natural language.
  • Programming assistance: Assist developers by providing code completion, translation, and debugging suggestions.
  • Educational applications: Help students learn programming concepts by providing explanations and example code.

The model's open-source nature and comprehensive documentation make it accessible for researchers and developers to explore and integrate into their own projects.

Things to try

One interesting aspect of CrystalCoder is its ability to balance natural language and coding tasks. Try using the model to generate code in response to a natural language prompt, and then ask it to explain the generated code. This can help you understand how the model is reasoning about the relationship between language and programming.

You can also experiment with providing the model with partial code snippets and asking it to complete or expand upon them. This can showcase the model's capabilities in terms of code understanding and generation.



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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