Phind-CodeLlama-34B-v2-GPTQ
Maintainer: TheBloke
86
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| 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 |
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Model overview
The Phind-CodeLlama-34B-v2-GPTQ is a quantized version of Phind's large language model, CodeLlama 34B v2. This model was created by the maintainer TheBloke and is available in various quantization formats, including GPTQ, AWQ, and GGUF. The GPTQ models offer multiple quantization parameter options to suit different hardware requirements and performance needs. This allows users to choose the best trade-off between model size, inference speed, and quality for their specific use case.
Similar models available include the Phind-CodeLlama-34B-v2-GGUF, which provides 2-8 bit GGUF formats for CPU and GPU inference, and the Llama-2-13B-GPTQ, which is a quantized version of Meta's Llama 2 13B model.
Model inputs and outputs
Inputs
- Text prompts: The model accepts text prompts as input, which can be used to generate continuations, complete tasks, or engage in conversations.
Outputs
- Generated text: The model outputs generated text, which can range from short completions to long-form responses depending on the prompt and use case.
Capabilities
The Phind-CodeLlama-34B-v2-GPTQ model is capable of a wide range of natural language processing tasks, including code generation, question answering, summarization, and open-ended conversation. It has demonstrated state-of-the-art performance on the HumanEval benchmark, achieving a 73.8% pass@1 score. This makes it one of the most capable open-source language models for programming-related tasks.
What can I use it for?
The Phind-CodeLlama-34B-v2-GPTQ model can be used for a variety of applications, such as:
- Code generation and assistance: The model can be used to generate, explain, and debug code snippets, as well as to provide intelligent assistance for software developers.
- Language modeling and generation: The model can be used for general-purpose language modeling, text generation, and conversational applications.
- Transfer learning and fine-tuning: The pre-trained model can be further fine-tuned on domain-specific datasets to create specialized models for various NLP tasks.
Things to try
One interesting aspect of the Phind-CodeLlama-34B-v2-GPTQ model is its ability to generate high-quality code across multiple programming languages, including Python, C/C++, TypeScript, and Java. Developers can experiment with providing the model with programming prompts and observing the generated code, then use it to assist with tasks like prototyping, refactoring, or implementing new features. The model's strong performance on the HumanEval benchmark suggests it could be a valuable tool for automating certain programming workflows.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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