WizardCoder-Python-34B-V1.0-GGUF
Maintainer: TheBloke
77
🛸
| 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 |
Create account to get full access
Model overview
The WizardCoder-Python-34B-V1.0-GGUF model is a 34 billion parameter AI model created by WizardLM and maintained by TheBloke. It is a Python-focused version of the WizardCoder model, designed for general code synthesis and understanding tasks. The model has been quantized to the GGUF format, which offers advantages over the previous GGML format in terms of tokenization, special token support, and extensibility.
Similar models include the CodeLlama-7B-GGUF and CausalLM-14B-GGUF, also maintained by TheBloke. These models span a range of sizes and specializations, allowing users to choose the option best suited to their needs and hardware constraints.
Model inputs and outputs
The WizardCoder-Python-34B-V1.0-GGUF model takes text as input and generates text as output. It is designed to excel at code-related tasks, such as code completion, infilling, and translation between programming languages. The model can also be used for general language understanding and generation tasks.
Inputs
- Natural language text prompts
- Code snippets or programming language constructs
Outputs
- Generated text, including code, natural language, and hybrid text-code responses
- Completions or continuations of input prompts
- Translations between programming languages
Capabilities
The WizardCoder-Python-34B-V1.0-GGUF model is a powerful tool for a variety of code-related tasks. It can be used to generate original code, complete partially written code, translate between programming languages, and even explain and comment on existing code. The model's large size and specialized training make it well-suited for complex programming challenges.
What can I use it for?
The WizardCoder-Python-34B-V1.0-GGUF model can be a valuable asset for developers, data scientists, and anyone working with code. Some potential use cases include:
- Code Assistance: Use the model to autocomplete code, suggest fixes for bugs, or generate new code based on a natural language description.
- Code Generation: Leverage the model's capabilities to create original code for prototypes, proofs of concept, or production applications.
- Language Translation: Translate code between different programming languages, making it easier to work with codebases in multiple languages.
- Code Explanation: Ask the model to explain the functionality of a code snippet or provide commentary on its structure and design.
By taking advantage of the model's strengths, you can streamline your development workflow, explore new ideas more quickly, and collaborate more effectively with team members.
Things to try
One interesting aspect of the WizardCoder-Python-34B-V1.0-GGUF model is its ability to generate hybrid text-code responses. Try providing the model with a natural language prompt that describes a programming task, and see how it combines textual explanations with relevant code snippets to provide a comprehensive solution.
Another interesting exercise is to explore the model's translation capabilities. Feed it code in one language and ask it to translate the functionality to another language, then compare the generated code to your own manual translations.
Overall, the WizardCoder-Python-34B-V1.0-GGUF model is a powerful tool that can enhance your programming productivity and creativity. Experiment with different prompts and tasks to discover how it can best fit into your workflow.
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-Python-13B-V1.0-GGUF
51
The WizardCoder-Python-13B-V1.0-GGUF model is a large language model created by WizardLM. It is a 13 billion parameter model trained specifically for Python code generation and understanding. The model is available in GGUF format, which is a new format introduced by the llama.cpp team that offers numerous advantages over the previous GGML format. The model is part of a broader suite of WizardCoder models available in different sizes, including a 34 billion parameter version that outperforms GPT-4, ChatGPT-3.5, and Claude2 on the HumanEval benchmark. The WizardCoder-Python-34B-V1.0-GGUF model provides even more advanced capabilities for Python-related tasks. Model inputs and outputs Inputs Text prompts**: The model accepts natural language text prompts as input, which can include instructions, questions, or partial code snippets. Outputs Generated text**: The model outputs generated text, which can include completed code snippets, explanations, or responses to the input prompts. Capabilities The WizardCoder-Python-13B-V1.0-GGUF model is highly capable at a variety of Python-related tasks, including code generation, code completion, code understanding, and following code-related instructions. It can generate working code snippets from high-level descriptions, provide explanations and insights about code, and assist with a wide range of programming-oriented tasks. What can I use it for? Given its strong performance on Python-focused benchmarks, the WizardCoder-Python-13B-V1.0-GGUF model would be well-suited for a variety of applications that require advanced code generation, understanding, or assistance capabilities. This could include building AI-powered programming tools, automating code-related workflows, or integrating language model-driven features into software development environments. The model's GGUF format also makes it compatible with a wide range of inference tools and frameworks, such as llama.cpp, text-generation-webui, and LangChain, allowing for flexible deployment and integration into various projects and systems. Things to try Some interesting things to try with the WizardCoder-Python-13B-V1.0-GGUF model could include: Providing high-level prompts or descriptions and having the model generate working code snippets to implement the desired functionality. Asking the model to explain the behavior of a given code snippet or provide insights into how it works. Experimenting with different prompting techniques, such as using code comments or docstrings as input, to see how the model responds and the quality of the generated outputs. Integrating the model into a developer tool or IDE to provide intelligent code suggestions and assistance during the programming process. By exploring the capabilities of this model, you can uncover new and innovative ways to leverage large language models to enhance and streamline Python-based development workflows.
Updated Invalid Date
🤖
WizardLM-1.0-Uncensored-Llama2-13B-GGUF
52
The WizardLM-1.0-Uncensored-Llama2-13B-GGUF model is a large language model created by Eric Hartford and maintained by TheBloke. It is a version of the WizardLM model that has been retrained with a filtered dataset to reduce refusals, avoidance, and bias. This model is designed to be more compliant than the original WizardLM-13B-V1.0 release. Similar models include the WizardLM-1.0-Uncensored-Llama2-13B-GGML, WizardLM-1.0-Uncensored-Llama2-13B-GPTQ, and the unquantised WizardLM-1.0-Uncensored-Llama2-13b model. Model inputs and outputs The WizardLM-1.0-Uncensored-Llama2-13B-GGUF model is a text-to-text model, meaning it takes text prompts as input and generates text as output. Inputs Prompts**: Text prompts that the model will use to generate output. Outputs Generated text**: The model will generate relevant text based on the provided prompts. Capabilities The WizardLM-1.0-Uncensored-Llama2-13B-GGUF model has a wide range of capabilities, including natural language understanding, language generation, and task completion. It can be used for tasks such as question answering, text summarization, and creative writing. What can I use it for? The WizardLM-1.0-Uncensored-Llama2-13B-GGUF model can be useful for a variety of applications, such as building chatbots, generating content for websites or social media, and assisting with research and analysis tasks. However, as an uncensored model, it is important to use the model responsibly and be aware of the potential risks. Things to try Some interesting things to try with the WizardLM-1.0-Uncensored-Llama2-13B-GGUF model include experimenting with different prompts to see how the model responds, using the model to generate creative stories or poems, and exploring its capabilities for task completion and language understanding.
Updated Invalid Date
👀
WizardCoder-Python-34B-V1.0-GPTQ
60
The WizardCoder-Python-34B-V1.0 is a powerful large language model created by WizardLM. It is a 34 billion parameter model fine-tuned on the Evol Instruct Code dataset. This model surpasses the performance of GPT4 (2023/03/15), ChatGPT-3.5, and Claude2 on the HumanEval Benchmarks, achieving a 73.2 pass@1 score. In comparison, the WizardCoder-Python-13B-V1.0-GPTQ model is a 13 billion parameter version of the WizardCoder model that also achieves strong performance, surpassing models like Claude-Plus, Bard, and InstructCodeT5+. Model inputs and outputs Inputs Text prompt**: The model takes in a text prompt as input, which can be a natural language instruction, a coding task, or any other type of text-based input. Outputs Text response**: The model generates a text response that appropriately completes the given input prompt. This can be natural language text, code, or a combination of both. Capabilities The WizardCoder-Python-34B-V1.0 model has impressive capabilities when it comes to understanding and generating code. It can tackle a wide range of coding tasks, from simple programming exercises to more complex algorithmic problems. The model also demonstrates strong performance on natural language processing tasks, making it a versatile tool for various applications. What can I use it for? The WizardCoder-Python-34B-V1.0 model can be used for a variety of applications, including: Coding assistance**: Helping developers write more efficient and robust code by providing suggestions, explanations, and solutions to coding problems. Automated code generation**: Generating boilerplate code, prototypes, or even complete applications based on natural language descriptions. AI-powered programming tools**: Integrating the model into IDEs, code editors, or other programming tools to enhance developer productivity and creativity. Educational purposes**: Using the model to teach coding concepts, provide feedback on student submissions, or develop interactive programming tutorials. Research and experimentation**: Exploring the model's capabilities, testing new use cases, and contributing to the advancement of large language models for code-related tasks. Things to try One interesting aspect of the WizardCoder-Python-34B-V1.0 model is its ability to handle complex programming logic and solve algorithmic problems. You could try giving the model a challenging coding challenge or a problem from a coding competition and see how it performs. Additionally, you could experiment with different prompting strategies to see how the model responds to more open-ended or creative tasks, such as generating novel algorithms or suggesting innovative software design patterns.
Updated Invalid Date
🧠
CodeLlama-7B-Python-GGUF
51
CodeLlama-7B-Python-GGUF Model overview The CodeLlama-7B-Python-GGUF is a 7 billion parameter AI model created by Meta and maintained by TheBloke. It is a variant of the CodeLlama family of models, designed specifically for Python code generation and understanding tasks. Similar models include the CodeLlama-7B-GGUF, CodeLlama-13B-GGUF, and CodeLlama-34B-GGUF. All of these models leverage the new GGUF format introduced by the llama.cpp team. Model inputs and outputs Inputs Text prompts for code generation or understanding Outputs Generated Python code Responses to natural language prompts about Python programming Capabilities The CodeLlama-7B-Python-GGUF model is adept at Python code generation, code understanding, and responding to natural language prompts related to Python programming. It can generate complete code snippets, provide explanations of code, and assist with programming tasks. What can I use it for? This model can be used for a variety of Python-focused applications, such as code editors with autocomplete and code generation capabilities, AI programming assistants, and Python learning tools. Developers can integrate the model into their applications using the provided integration guides for llama.cpp, ctransformers, and LangChain. Things to try One interesting thing to try with this model is generating solutions to coding challenges or interview questions. Provide the model with a brief description of the problem, and it can generate a working code solution that adheres to the specified constraints. You can then further refine or debug the generated code as needed.
Updated Invalid Date