wizardcoder-34b-v1.0
Maintainer: rhamnett
2
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
wizardcoder-15b-v1.0
2
The wizardcoder-15b-v1.0 is a large language model created by the Replicate user lucataco. It is a variant of the WizardLM family of models, which have shown impressive performance on tasks like code generation. While not much is known about the specific architecture or training process of this particular model, it is likely a powerful tool for a variety of natural language processing tasks. When compared to similar models like the wizardcoder-34b-v1.0, wizard-mega-13b-awq, wizardlm-2-8x22b, and WizardLM-13B-V1.0, the wizardcoder-15b-v1.0 appears to be a more compact and efficient version, while still maintaining strong capabilities. Its potential use cases and performance characteristics are not entirely clear from the available information. Model inputs and outputs Inputs prompt**: A text prompt that the model will use to generate a response. max_new_tokens**: The maximum number of new tokens the model will generate in response to the prompt. temperature**: A value that controls the randomness of the model's output, with lower values resulting in more focused and deterministic responses. Outputs output**: The text generated by the model in response to the input prompt. id**: A unique identifier for the model run. version**: The version of the model used. created_at**: The timestamp when the model run was initiated. started_at**: The timestamp when the model run started. completed_at**: The timestamp when the model run completed. logs**: The logs from the model run. error**: Any errors that occurred during the model run. status**: The status of the model run (e.g., "succeeded", "failed"). metrics**: Performance metrics for the model run, such as the prediction time. Capabilities The wizardcoder-15b-v1.0 model appears to be a capable code generation tool, as demonstrated by the example of generating a Python function to check if a number is prime. Its ability to produce coherent and relevant code snippets suggests it could be useful for tasks like software development, data analysis, and automation. What can I use it for? The wizardcoder-15b-v1.0 model could be a valuable tool for developers and data scientists looking to automate or streamline various tasks. For example, it could be integrated into an IDE to assist with code completion and generation, or used to generate boilerplate code for common programming tasks. Additionally, it could be employed in data analysis workflows to generate custom scripts and functions on demand. Things to try One interesting thing to try with the wizardcoder-15b-v1.0 model would be to explore its capabilities in generating more complex code, such as multi-function programs or algorithms that solve specific problems. It would also be worthwhile to experiment with different prompting strategies and temperature settings to see how they affect the model's outputs and performance.
Updated Invalid Date
wizardcoder-33b-v1.1-gguf
20
WizardCoder-33B-V1.1 is an AI model developed by lucataco that is part of the WizardCoder family. It is an improvement upon the earlier WizardCoder-15B-V1.0 and WizardCoder-34B-V1.0 models, achieving better performance on benchmarks like HumanEval and MBPP. The model is designed to empower code-oriented large language models with the Evol-Instruct technique. Model inputs and outputs WizardCoder-33B-V1.1 takes in a text prompt as input, which can include an instruction for the model to carry out. The model then generates a text response that completes the requested task. Inputs Prompt**: The instruction or text the model should use to generate a response. System Prompt**: A default prompt that helps guide the model's behavior. Temperature**: A parameter that controls how "creative" the model's response will be. Repeat Penalty**: A parameter that discourages the model from repeating itself too much. Max New Tokens**: The maximum number of new tokens the model should generate. Outputs Text Response**: The model's generated text completing the requested task. Capabilities WizardCoder-33B-V1.1 has been shown to outperform several closed-source and open-source models on programming-related benchmarks like HumanEval and MBPP. It can generate original code to complete a wide variety of coding tasks, from writing a simple snake game in Python to more complex programming challenges. What can I use it for? WizardCoder-33B-V1.1 could be used for a range of applications involving code generation, such as aiding software developers, automating certain programming tasks, or even as a starting point for building custom AI applications. The model's strong performance on benchmarks suggests it may be particularly useful for tasks like prototyping, debugging, or generating boilerplate code. Things to try One interesting thing to try with WizardCoder-33B-V1.1 would be to give it increasingly complex or open-ended coding challenges to see how it performs. You could also experiment with adjusting the temperature and repeat penalty parameters to find the sweet spot for your specific use case. Additionally, comparing the model's outputs to those of other code-oriented language models could yield interesting insights.
Updated Invalid Date
wizard-mega-13b-awq
5
wizard-mega-13b-awq is a large language model (LLM) developed by nateraw that has been quantized using Adaptive Weight Quantization (AWQ) and served with vLLM. It is similar in capabilities to other LLMs like nous-hermes-llama2-awq, wizardlm-2-8x22b, and whisper-large-v3. These models can be used for a variety of language-based tasks such as text generation, question answering, and language translation. Model inputs and outputs wizard-mega-13b-awq takes in a text prompt and generates additional text based on that prompt. The model allows you to control various parameters like the "top k" and "top p" values, temperature, and the maximum number of new tokens to generate. The output is a string of generated text. Inputs message**: The text prompt to be used as input for the model. max_new_tokens**: The maximum number of tokens the model should generate as output. temperature**: The value used to modulate the next token probabilities. top_p**: A probability threshold for generating the output. If = top_p (nucleus filtering). top_k**: The number of highest probability tokens to consider for generating the output. If > 0, only keep the top k tokens with highest probability (top-k filtering). presence_penalty**: The presence penalty. Outputs Output**: The generated text output from the model. Capabilities wizard-mega-13b-awq is a powerful language model that can be used for a variety of tasks. It can generate coherent and contextually-appropriate text, answer questions, and even engage in open-ended conversations. The model has been trained on a vast amount of text data, giving it a broad knowledge base that it can draw upon. What can I use it for? wizard-mega-13b-awq can be used for a wide range of applications, such as: Content generation**: The model can be used to generate articles, stories, or other types of written content. Chatbots and virtual assistants**: The model can be used to power conversational AI agents that can engage in natural language interactions. Language translation**: The model can be fine-tuned for translation tasks, allowing it to translate text between different languages. Question answering**: The model can be used to answer questions on a variety of topics, drawing upon its broad knowledge base. Things to try One interesting thing to try with wizard-mega-13b-awq is to experiment with the different input parameters, such as temperature and top-k/top-p values. Adjusting these can result in significantly different output styles, from more creative and diverse to more conservative and coherent. You can also try prompting the model with open-ended questions or tasks and see how it responds, as this can reveal interesting insights about its capabilities and limitations.
Updated Invalid Date
💬
WizardCoder-15B-V1.0
736
The WizardCoder-15B-V1.0 model is a large language model (LLM) developed by the WizardLM Team that has been fine-tuned specifically for coding tasks using their Evol-Instruct method. This method involves automatically generating a diverse set of code-related instructions to further train the model on instruction-following capabilities. Compared to similar open-source models like CodeGen-16B-Multi, LLaMA-33B, and StarCoder-15B, the WizardCoder-15B-V1.0 model exhibits significantly higher performance on the HumanEval benchmark, achieving a pass@1 score of 57.3 compared to the 18.3-37.8 range of the other models. Model inputs and outputs Inputs Natural language instructions**: The model takes in natural language prompts that describe coding tasks or problems to be solved. Outputs Generated code**: The model outputs code in a variety of programming languages (e.g. Python, Java, etc.) that attempts to solve the given problem or complete the requested task. Capabilities The WizardCoder-15B-V1.0 model has been specifically trained to excel at following code-related instructions and generating functional code to solve a wide range of programming problems. It is capable of tasks such as writing simple algorithms, fixing bugs in existing code, and even generating complex programs from high-level descriptions. What can I use it for? The WizardCoder-15B-V1.0 model could be a valuable tool for developers, students, and anyone working on code-related projects. Some potential use cases include: Prototyping and rapid development of new software features Automating repetitive coding tasks Helping to explain programming concepts by generating sample code Tutoring and teaching programming by providing step-by-step solutions Things to try One interesting thing to try with the WizardCoder-15B-V1.0 model is to provide it with vague or open-ended prompts and see how it interprets and responds to them. For example, you could ask it to "Write a Python program that analyzes stock market data" and see the creative and functional solutions it comes up with. Another idea is to give the model increasingly complex or challenging coding problems, like those found on programming challenge websites, and test its ability to solve them. This can help uncover the model's strengths and limitations when it comes to more advanced programming tasks.
Updated Invalid Date