WizardCoder-Python-13B-V1.0-GGUF
Maintainer: TheBloke
51
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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
The WizardCoder-Python-13B-V1.0-GGUF model is a large language model created by WizardLM. It is a 13 billion parameter model trained specifically for Python code generation and understanding. The model is available in GGUF format, which is a new format introduced by the llama.cpp team that offers numerous advantages over the previous GGML format.
The model is part of a broader suite of WizardCoder models available in different sizes, including a 34 billion parameter version that outperforms GPT-4, ChatGPT-3.5, and Claude2 on the HumanEval benchmark. The WizardCoder-Python-34B-V1.0-GGUF model provides even more advanced capabilities for Python-related tasks.
Model inputs and outputs
Inputs
- Text prompts: The model accepts natural language text prompts as input, which can include instructions, questions, or partial code snippets.
Outputs
- Generated text: The model outputs generated text, which can include completed code snippets, explanations, or responses to the input prompts.
Capabilities
The WizardCoder-Python-13B-V1.0-GGUF model is highly capable at a variety of Python-related tasks, including code generation, code completion, code understanding, and following code-related instructions. It can generate working code snippets from high-level descriptions, provide explanations and insights about code, and assist with a wide range of programming-oriented tasks.
What can I use it for?
Given its strong performance on Python-focused benchmarks, the WizardCoder-Python-13B-V1.0-GGUF model would be well-suited for a variety of applications that require advanced code generation, understanding, or assistance capabilities. This could include building AI-powered programming tools, automating code-related workflows, or integrating language model-driven features into software development environments.
The model's GGUF format also makes it compatible with a wide range of inference tools and frameworks, such as llama.cpp, text-generation-webui, and LangChain, allowing for flexible deployment and integration into various projects and systems.
Things to try
Some interesting things to try with the WizardCoder-Python-13B-V1.0-GGUF model could include:
- Providing high-level prompts or descriptions and having the model generate working code snippets to implement the desired functionality.
- Asking the model to explain the behavior of a given code snippet or provide insights into how it works.
- Experimenting with different prompting techniques, such as using code comments or docstrings as input, to see how the model responds and the quality of the generated outputs.
- Integrating the model into a developer tool or IDE to provide intelligent code suggestions and assistance during the programming process.
By exploring the capabilities of this model, you can uncover new and innovative ways to leverage large language models to enhance and streamline Python-based development workflows.
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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