WizardCoder-33B-V1.1
Maintainer: WizardLMTeam
124
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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
WizardCoder-33B-V1.1 is a large language model (LLM) developed by the WizardLM team that is trained to excel at code-related tasks. It is based on the DeepSeek-Coder-33B-base model and has been further fine-tuned using the Evol-Instruct method to improve its code generation and understanding capabilities. Compared to previous versions, WizardCoder-33B-V1.1 achieves state-of-the-art performance on several industry-standard benchmarks, outperforming models like ChatGPT 3.5, Gemini Pro, and DeepSeek-Coder-33B-instruct.
Model inputs and outputs
Inputs
- Natural language instructions: The model accepts natural language descriptions of coding tasks or problems that it should solve.
Outputs
- Generated code: The model's primary output is Python, Java, or other programming language code that attempts to fulfill the given instruction or solve the provided problem.
Capabilities
WizardCoder-33B-V1.1 demonstrates impressive abilities in generating functional code to solve a wide variety of programming tasks. It achieves 79.9 pass@1 on the HumanEval benchmark, 73.2 pass@1 on HumanEval-Plus, 78.9 pass@1 on MBPP, and 66.9 pass@1 on MBPP-Plus. These results show the model's strong performance compared to other code LLMs, making it a valuable tool for developers and programmers.
What can I use it for?
The WizardCoder-33B-V1.1 model can be utilized in a range of applications that involve code generation or understanding, such as:
- Automated code completion and suggestions to assist developers
- Prototyping and building initial versions of software applications
- Translating natural language descriptions into working code
- Educational tools for teaching programming concepts and skills
- Augmenting human programming workflows to boost productivity
Things to try
One interesting aspect of WizardCoder-33B-V1.1 is its ability to handle complex, multi-part instructions and generate code that addresses all the requirements. You could try providing the model with detailed prompts involving various coding tasks and see how it responds. Additionally, experimenting with different decoding strategies, such as adjusting the temperature or number of samples, may uncover further nuances in the model's capabilities.
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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