Orca-2-13B-GGUF
Maintainer: TheBloke
61
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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 Orca-2-13B-GGUF is a large language model created by Microsoft and quantized to the GGUF format by TheBloke. It is a version of Microsoft's Orca 2 13B model, which was fine-tuned on a curated dataset from the OpenOrca project. GGUF is a new format introduced by the llama.cpp team that offers several advantages over the previous GGML format. TheBloke has provided multiple quantized versions of the model in 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit formats to support a range of use cases and hardware capabilities.
Model inputs and outputs
Inputs
- Text prompts of varying length
Outputs
- Continuation of the input text, generating new text
Capabilities
The Orca-2-13B-GGUF model is capable of a wide range of text-to-text tasks, such as language modeling, summarization, question answering, and code generation. It was fine-tuned on a diverse dataset and can handle a variety of topics and styles. Compared to the original Orca 2 13B model, the quantized GGUF versions offer improved performance and efficiency for deployment on different hardware.
What can I use it for?
The Orca-2-13B-GGUF model can be used for a wide range of natural language processing tasks, such as chatbots, virtual assistants, content generation, and code completion. The quantized GGUF versions are particularly well-suited for deployment on resource-constrained devices or in real-time applications, as they offer lower memory footprint and faster inference times. TheBloke has also provided a number of other quantized models, such as Mistral-7B-OpenOrca-GGUF and phi-2-GGUF, that may be of interest depending on your specific use case.
Things to try
One interesting aspect of the Orca-2-13B-GGUF model is its ability to handle longer-form text generation. By taking advantage of the GGUF format's support for extended sequence lengths, you can experiment with generating coherent and contextually-relevant text over multiple paragraphs. Additionally, the different quantization levels offer trade-offs between model size, inference speed, and output quality, so you can test which version works best for your specific hardware and performance requirements.
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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