WizardCoder-Python-13B-V1.0-GPTQ
Maintainer: TheBloke
76
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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-GPTQ is a large language model (LLM) created by WizardLM and maintained by TheBloke. It is a Llama 13B model that has been fine-tuned on datasets like ShareGPT, WizardLM, and Wizard-Vicuna to improve its abilities in text generation and task completion. The model has been quantized using GPTQ techniques to reduce its size and memory footprint, making it more accessible for various use cases.
Model inputs and outputs
Inputs
- Prompt: A text prompt that the model uses to generate a response.
Outputs
- Generated text: The model's response to the provided prompt, which can be of varying length depending on the use case.
Capabilities
The WizardCoder-Python-13B-V1.0-GPTQ model is capable of generating human-like text on a wide range of topics. It can be used for tasks such as language modeling, text generation, and task completion. The model has been fine-tuned on datasets that cover a diverse range of subject matter, allowing it to engage in coherent and contextual conversations.
What can I use it for?
The WizardCoder-Python-13B-V1.0-GPTQ model can be used for a variety of applications, such as:
- Content generation: The model can be used to generate articles, stories, or any other type of text content.
- Chatbots and virtual assistants: The model can be integrated into chatbots and virtual assistants to provide natural language responses to user queries.
- Code generation: The model can be used to generate code snippets or even complete programs based on natural language instructions.
Things to try
One interesting aspect of the WizardCoder-Python-13B-V1.0-GPTQ model is its ability to engage in open-ended conversations and task completion. You can try providing the model with a wide range of prompts, from creative writing exercises to technical programming tasks, and observe how it responds. The model's fine-tuning on diverse datasets allows it to handle a variety of subject matter, so feel free to experiment and see what kind of results you can get.
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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