CodeLlama-7B-Instruct-GPTQ
Maintainer: TheBloke
43
⚙️
| 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 CodeLlama-7B-Instruct-GPTQ is a language model created by TheBloke, who provides quantized versions of the CodeLlama models for efficient GPU inference. It is based on Meta's CodeLlama 7B Instruct model, which is designed for general code synthesis and understanding. TheBloke offers several quantized versions with different bit sizes and parameter configurations to suit different hardware and performance requirements.
Similar models provided by TheBloke include the CodeLlama-34B-Instruct-GPTQ, which is a 34 billion parameter version of the CodeLlama Instruct model, and the Llama-2-7B-GPTQ, a 7 billion parameter version of Meta's Llama 2 model.
Model inputs and outputs
Inputs
- The
CodeLlama-7B-Instruct-GPTQmodel takes in text prompts as input.
Outputs
- The model generates text outputs in response to the input prompts.
Capabilities
The CodeLlama-7B-Instruct-GPTQ model can be used for a variety of code-related tasks, such as code completion, code generation, and code understanding. It has been trained to follow instructions and can be used as a general-purpose code assistant. The quantized versions provided by TheBloke allow for efficient inference on GPUs, making the model practical for deployment in real-world applications.
What can I use it for?
The CodeLlama-7B-Instruct-GPTQ model can be used in a variety of software development and programming-related applications. For example, it could be integrated into an IDE or code editor to provide intelligent code completion and generation assistance. It could also be used to build chatbots or virtual assistants that can help with coding tasks, such as explaining programming concepts, debugging code, or suggesting solutions to coding problems.
Things to try
One interesting aspect of the CodeLlama-7B-Instruct-GPTQ model is its ability to follow instructions and generate code that passes test cases. You could try providing the model with a coding challenge or problem statement and see how it responds, observing its ability to understand the requirements and generate working code. Additionally, you could experiment with the different quantization options provided by TheBloke to find the best balance between performance and model quality for your specific 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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