Please use in accordance with Llama-2's license terms.
The prompt for our WizardCoder is:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
WizardCoder is a Code Large Language Model (LLM) that has been fine-tuned using the Evol-Instruct method. The model has been trained on a large dataset of code instruction-following tasks and has demonstrated exceptional performance on code-related tasks.
WizardCoder is a Code LLM that has been fine-tuned using the Evol-Instruct method. The model has been trained on a large dataset of code instruction-following tasks and has demonstrated superior performance compared to other open-source and closed LLMs on prominent code generation benchmarks.
WizardCoder can be used for a variety of code-related tasks, including code generation, code completion, and code summarization. Here are some examples of input prompts that can be used with the model:
- Code generation: Given a description of a programming task, generate the corresponding code. Example input: "Write a Python function that takes a list of integers as input and returns the sum of all even numbers in the list."
- Code completion: Given an incomplete code snippet, complete the code. Example input: "def multiply(a, b): \n return a * b _"
- Code summarization: Given a long code snippet, generate a summary of the code. Example input: “Write a Python program that reads a CSV file and calculates the average of a specific column.”
WizardCoder was trained on a large dataset of code instruction-following tasks. The dataset was evolved through the Evol-Instruct method and contains a variety of programming tasks with different levels of complexity. The dataset is not publicly available.
WizardCoder has demonstrated exceptional performance on code-related tasks. The model has outperformed other open-source and closed LLMs on prominent code generation benchmarks, including HumanEval (57.3%), HumanEval+, and MBPP(51.8%). However, the model still falls significantly behind the SOTA LLM and GPT4.
WizardCoder-15B-v1.0 model achieves the 57.3 pass@1 on the HumanEval Benchmarks, which is 22.3 points higher than the SOTA open-source Code LLMs including StarCoder, CodeGen, CodeGee, and CodeT5+. Additionally, WizardCoder significantly outperforms all the open-source Code LLMs with instructions fine-tuning, including InstructCodeT5+, StarCoder-GPTeacher, and Instruct-Codegen-16B
WizardCoder has outperformed the largest closed-source LLMs, including Claude, Bard, PaLM, PaLM-2, and LaMDA, despite being significantly smaller
WizardCoder could generate unethical, harmful, or misleading information. Therefore, it is important to use the model responsibly and to address the ethical and societal implications of its use. Additionally, the dataset used to train the model is not publicly available, which limits the ability of researchers to replicate the results and evaluate the model on new tasks.
- Model Type IDText To Text
- DescriptionWizardCoder is a Code Large Language Model (LLM) that has been fine-tuned on Llama2 using the Evol-Instruct method and has demonstrated superior performance compared to other open-source and closed LLMs on prominent code generation benchmarks.
- Last UpdatedSep 18, 2023
- Use Case