WizardCoder-15B-1.0-GPTQ

Maintainer: TheBloke

Total Score

175

Last updated 5/28/2024

👁️

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

The WizardCoder-15B-1.0-GPTQ is a 15 billion parameter language model created by TheBloke and is based on the original WizardLM WizardCoder-15B-V1.0 model. It has been quantized to 4-bit precision using the AutoGPTQ tool, allowing for significantly reduced memory usage and faster inference speeds compared to the original full-precision model. This model is optimized for code-related tasks and demonstrates impressive performance on benchmarks like HumanEval, surpassing other open-source and even some closed-source models.

Similar models include the WizardCoder-15B-1.0-GGML and WizardCoder-Python-13B-V1.0-GPTQ, which provide different quantization options and tradeoffs for users' hardware and requirements.

Model inputs and outputs

Inputs

  • Instruction: A textual description of a task or problem to solve.

Outputs

  • Response: The model's generated solution or answer to the provided instruction, in the form of text.

Capabilities

The WizardCoder-15B-1.0-GPTQ model demonstrates strong performance on a variety of code-related tasks, including algorithm implementation, code generation, and problem-solving. It is able to understand natural language instructions and produce working, syntactically-correct code in various programming languages.

What can I use it for?

This model can be particularly useful for developers and programmers who need assistance with coding tasks, such as prototyping new features, solving algorithmic challenges, or generating boilerplate code. It could also be integrated into developer tools and workflows to enhance productivity and ideation.

Additionally, the model's capabilities could be leveraged in educational settings to help teach programming concepts, provide interactive coding exercises, or offer personalized coding assistance to students.

Things to try

One interesting aspect of the WizardCoder-15B-1.0-GPTQ model is its ability to handle open-ended prompts and generate creative solutions. Try providing the model with ambiguous or underspecified instructions and observe how it interprets and responds to the task. This can uncover interesting insights about the model's understanding of context and its ability to reason about programming problems.

Another area to explore is the model's performance on domain-specific tasks or languages. While the model is primarily trained on general code-related data, it may excel at certain types of programming challenges or excel at generating code in particular languages based on the nature of the training data.



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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