Rhamnett

wizardcoder-34b-v1.0

rhamnett

2

wizardcoder-34b-v1.0 is a recently developed variant of the Code Llama model by maintainer rhamnett that has achieved better scores than GPT-4 on the Human Eval benchmark. It builds upon the earlier StarCoder-15B and WizardLM-30B 1.0 models, incorporating the maintainer's "Evol-Instruct" fine-tuning method to further enhance the model's code generation capabilities. Model inputs and outputs wizardcoder-34b-v1.0 is a large language model that can be used for a variety of text generation tasks. The model takes in a text prompt as input and generates coherent and contextually relevant text as output. Inputs Prompt**: The text prompt that is used to condition the model's generation. N**: The number of output sequences to generate, between 1 and 5. Top P**: The percentage of the most likely tokens to sample from when generating text, between 0.01 and 1. Lower values ignore less likely tokens. Temperature**: Adjusts the randomness of the outputs, with higher values generating more diverse but less coherent text. Max Length**: The maximum number of tokens to generate, with a word generally consisting of 2-3 tokens. Repetition Penalty**: A penalty applied to repeated words in the generated text, with values greater than 1 discouraging repetition. Outputs Output**: An array of strings, where each string represents a generated output sequence. Capabilities The wizardcoder-34b-v1.0 model has demonstrated strong performance on the Human Eval benchmark, surpassing the capabilities of GPT-4 in this domain. This suggests that it is particularly well-suited for tasks involving code generation and manipulation, such as writing programs to solve specific problems, refactoring existing code, or generating new code based on natural language descriptions. What can I use it for? Given its capabilities in code-related tasks, wizardcoder-34b-v1.0 could be useful for a variety of software development and engineering applications. Potential use cases include: Automating the generation of boilerplate code or scaffolding for new projects Assisting developers in writing and debugging code by providing suggestions or completing partially written functions Generating example code or tutorials to help teach programming concepts Translating natural language descriptions of problems into working code solutions Things to try One interesting aspect of wizardcoder-34b-v1.0 is its ability to generate code that not only solves the given problem, but also adheres to best practices and coding conventions. Try providing the model with a variety of code-related prompts, such as "Write a Python function to sort a list in ascending order" or "Refactor this messy JavaScript code to be more readable and maintainable," and observe how the model responds. You may be surprised by the quality and thoughtfulness of the generated code. Another thing to explore is the model's robustness to edge cases and unexpected inputs. Try pushing the boundaries of the model by providing ambiguous, incomplete, or even adversarial prompts, and see how the model handles them. This can help you understand the model's limitations and identify areas for potential improvement.

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