Phind-CodeLlama-34B-v2-GGUF
Maintainer: TheBloke
158
📶
| 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 Phind-CodeLlama-34B-v2-GGUF is a large language model created by Phind that has been converted to the GGUF format. GGUF is a new format introduced by the llama.cpp team that offers numerous advantages over the previous GGML format, such as better tokenization and support for special tokens.
This model is based on Phind's original CodeLlama 34B v2 model, which has been quantized and optimized for efficient inference across a variety of hardware and software platforms that support the GGUF format.
Model inputs and outputs
Inputs
- Text: The model takes text as input and can be used for a variety of natural language processing tasks.
Outputs
- Text: The model generates text as output, making it useful for tasks like language generation, summarization, and question answering.
Capabilities
The Phind-CodeLlama-34B-v2-GGUF model is a powerful text-to-text model that can be used for a wide range of natural language processing tasks. It has been shown to perform well on tasks like code generation, Q&A, and summarization. Additionally, the GGUF format allows for efficient inference on a variety of hardware and software platforms.
What can I use it for?
The Phind-CodeLlama-34B-v2-GGUF model could be useful for a variety of applications, such as:
- Content Generation: The model could be used to generate high-quality text content, such as articles, stories, or product descriptions.
- Language Assistance: The model could be used to build language assistance tools, such as chatbots or virtual assistants, that can help users with a variety of tasks.
- Code Generation: The model's strong performance on code-related tasks could make it useful for building tools that generate or assist with code development.
Things to try
One interesting aspect of the Phind-CodeLlama-34B-v2-GGUF model is its ability to handle a wide range of input formats and tasks. For example, you could try using the model for tasks like text summarization, question answering, or even creative writing. Additionally, the GGUF format allows for efficient inference, so you could experiment with running the model on different hardware configurations to see how it performs.
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
🔎
CodeLlama-34B-GGUF
55
The CodeLlama-34B-GGUF is a 34 billion parameter large language model created by Meta and maintained by TheBloke. It is part of the CodeLlama family of models, which also includes 7B and 13B versions. The CodeLlama models are designed for code synthesis and understanding, with variants specialized for Python and instruction following. This 34B GGUF version provides quantized model files for efficient CPU and GPU inference. Model inputs and outputs Inputs Text**: The model takes text inputs to generate new text. Outputs Text**: The model outputs generated text, which can be used for a variety of tasks such as code completion, infilling, and chat. Capabilities The CodeLlama-34B-GGUF model is capable of general code synthesis and understanding. It can be used for tasks like code completion, where it can generate the next lines of code based on a prompt, as well as code infilling, where it can fill in missing parts of code. The model also has capabilities for instruction following and chat, making it useful for building AI assistants. What can I use it for? The CodeLlama-34B-GGUF model can be used for a variety of applications, such as building code editors or AI programming assistants. Developers could use the model to autocomplete code, generate new functions or classes, or explain code snippets. The instruction-following capabilities also make it useful for building chatbots or virtual assistants that can help with programming tasks. Things to try One interesting thing to try with the CodeLlama-34B-GGUF model is to provide it with a partially completed code snippet and see how it can fill in the missing parts. You could also try giving it a high-level description of a programming task and see if it can generate the necessary code to solve the problem. Additionally, you could experiment with using the model for open-ended conversations about programming concepts and techniques.
Updated Invalid Date
❗
CodeLlama-70B-hf-GGUF
43
The CodeLlama-70B-hf-GGUF is a large language model created by Code Llama and maintained by TheBloke. It is a 70 billion parameter model designed for general code synthesis and understanding tasks. The model is available in several different quantized versions optimized for various tradeoffs between size, speed, and quality using the new GGUF format. Similar models include the CodeLlama-7B-GGUF and CodeLlama-13B-GGUF, which scale the model down to 7 and 13 billion parameters respectively. Model inputs and outputs The CodeLlama-70B-hf-GGUF model takes in text as input and generates text as output. It is designed to be a versatile code generation and understanding tool, capable of tasks like code completion, infilling, and general instruction following. Inputs Text**: The model accepts natural language text prompts as input. Outputs Text**: The model generates natural language text in response to the input prompt. Capabilities The CodeLlama-70B-hf-GGUF model excels at a variety of code-focused tasks. It can generate new code to solve programming problems, complete partially written code, and even translate natural language instructions into functioning code. The model also demonstrates strong code understanding capabilities, making it useful for tasks like code summarization and refactoring. What can I use it for? The CodeLlama-70B-hf-GGUF model could be used in a number of interesting applications. Developers could integrate it into code editors or IDEs to provide intelligent code assistance. Educators could use it to help students learn programming by generating examples and explanations. Researchers might leverage the model's capabilities to advance the field of automated code generation and understanding. And entrepreneurs could explore building commercial products and services around the model's unique abilities. Things to try One interesting thing to try with the CodeLlama-70B-hf-GGUF model is to provide it with partial code snippets and see how it completes or expands upon them. You could also experiment with giving the model natural language descriptions of programming problems and have it generate solutions. Additionally, you might try using the model to summarize or explain existing code, which could be helpful for code review or onboarding new developers to a codebase.
Updated Invalid Date
➖
CodeLlama-34B-Instruct-GGUF
93
The CodeLlama-34B-Instruct-GGUF is a 34 billion parameter language model created by Meta and fine-tuned by TheBloke for code generation and understanding tasks. It is part of the CodeLlama family of models, which also includes smaller 7B and 13B versions. The model has been converted to the GGUF format, a new and improved version of the GGML format that offers better tokenization and support for special tokens. This model is designed to excel at a variety of code-related tasks, from code completion to infilling and understanding natural language instructions. It is particularly adept at Python, but can also handle other programming languages like C/C++, TypeScript, and Java. Similar models like the CodeLlama-7B-Instruct-GGUF and Phind-CodeLlama-34B-v2-GGUF offer different parameter sizes and capabilities. Model inputs and outputs Inputs The CodeLlama-34B-Instruct-GGUF model accepts text-based input, such as natural language prompts or programming code. Outputs The model generates text-based output, which can include further code, natural language responses, or a combination of both. Capabilities The CodeLlama-34B-Instruct-GGUF model excels at a variety of code-related tasks. It can generate working code snippets to solve coding problems, explain programming concepts in natural language, and even translate between different programming languages. The model's large size and specialized training make it a powerful tool for developers and researchers working on applications that involve code generation, understanding, or analysis. What can I use it for? The CodeLlama-34B-Instruct-GGUF model can be used for a wide range of applications, including: Building intelligent code assistants to help programmers with their daily tasks Automating the generation of boilerplate code or common programming patterns Developing tools for code analysis and refactoring Enhancing educational resources for learning programming languages Powering chatbots or virtual assistants that can understand and generate code The model's GGUF format and support for various client libraries and UI tools make it easy to integrate into a variety of projects and workflows. Things to try One interesting aspect of the CodeLlama-34B-Instruct-GGUF model is its ability to follow natural language instructions and generate code accordingly. Try giving it prompts like "Write a function in Python that calculates the Fibonacci sequence up to a given number" or "Implement a linked list data structure in C++". The model should be able to understand the request and produce the requested code, demonstrating its versatility and code-generation capabilities. Another fascinating aspect is the model's potential for cross-language translation and understanding. You could experiment by providing prompts that mix different programming languages, such as "Translate this Java code to Python" or "Explain the purpose of this TypeScript function in plain English". Observing how the model handles these types of mixed-language scenarios can provide insights into its broader linguistic and coding comprehension abilities.
Updated Invalid Date
🤔
CodeLlama-13B-GGUF
54
The CodeLlama-13B-GGUF is a 13-billion parameter large language model developed by Meta and maintained by TheBloke. It is part of the CodeLlama family of models, which also includes 7B and 34B versions. The CodeLlama models are designed for general code synthesis and understanding tasks. This 13B version provides a balance of performance and model size. Similar models from TheBloke include the CodeLlama-7B-GGUF and CodeLlama-34B-GGUF, which offer smaller and larger model sizes respectively. There are also Instruct-tuned versions of the CodeLlama models available, like the CodeLlama-34B-Instruct-GGUF and CodeLlama-7B-Instruct-GGUF. Model inputs and outputs The CodeLlama-13B-GGUF model takes in text as input and generates text as output. It is an autoregressive language model, meaning it produces text one token at a time, based on the previous tokens. Inputs Text**: The model accepts text input, such as programming language code, natural language instructions, or prompts. Outputs Text**: The model generates text, which can include synthesized code, responses to prompts, or continuations of input text. Capabilities The CodeLlama-13B-GGUF model is capable of a variety of text generation tasks, including code completion, code generation, language understanding, and language generation. It can handle a range of programming languages and can be used for tasks like automatically generating code snippets, translating natural language to code, and providing intelligent code assistance. What can I use it for? The CodeLlama-13B-GGUF model can be used in a variety of applications, such as: Code assistants**: Integrating the model into code editors or IDEs to provide intelligent code completion, generation, and understanding capabilities. Automated programming tools**: Building tools that can automatically generate code to solve specific programming problems. Language learning applications**: Developing educational apps that can help users learn programming languages by providing code examples and explanations. Chatbots and virtual assistants**: Incorporating the model's language understanding and generation abilities to build conversational AI agents that can assist users with programming-related tasks. The model's versatility and strong performance make it a valuable tool for developers, researchers, and anyone working on projects that involve programmatic tasks or language-based interactions. Things to try One interesting thing to try with the CodeLlama-13B-GGUF model is to provide it with incomplete code snippets or programming challenges and see how it can complete or solve them. You can also experiment with different prompting techniques, such as asking the model to explain or comment on code, or to generate code that meets specific requirements. Another interesting approach is to fine-tune the model on domain-specific data, such as code from a particular codebase or programming language, to see how it can adapt and improve its performance on tasks related to that domain.
Updated Invalid Date