orca_mini_13B-GPTQ
Maintainer: TheBloke
45
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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_mini_13B-GPTQ model is a 13-billion parameter language model created by Pankaj Mathur and maintained by TheBloke. It is a quantized version of the Pankaj Mathur's Orca Mini 13B model, which was trained on a combination of the WizardLM, Alpaca, and Dolly-V2 datasets, using the approaches from the Orca Research Paper. This helps the model learn the "thought process" from the ChatGPT teacher model.
Model inputs and outputs
The orca_mini_13B-GPTQ model is a text-to-text transformer that takes natural language prompts as input and generates text responses. The model can handle a wide variety of tasks, from open-ended conversation to task-oriented instruction following.
Inputs
- Natural language prompts, instructions, or conversations
Outputs
- Coherent, context-appropriate text responses
Capabilities
The orca_mini_13B-GPTQ model exhibits strong language understanding and generation capabilities. It can engage in open-ended conversation, answer questions, summarize information, and complete a variety of other natural language tasks. The model also shows robust performance on benchmarks like MMLU, ARC, HellaSwag, and TruthfulQA.
What can I use it for?
The orca_mini_13B-GPTQ model can be used for a wide range of natural language processing applications, such as:
- Building chatbots and virtual assistants
- Automating content creation (e.g. article writing, story generation)
- Providing helpful information and answers to users
- Summarizing long-form text
- Engaging in analytical or creative tasks
TheBloke also provides several other similar quantized models, like the orca_mini_3B-GGML and OpenOrca-Platypus2-13B-GPTQ, which may be worth exploring depending on your specific needs and hardware constraints.
Things to try
Some interesting things to try with the orca_mini_13B-GPTQ model include:
- Exploring its reasoning and analytical capabilities by asking it to solve logic puzzles or provide step-by-step solutions to complex problems.
- Assessing its creative writing abilities by prompting it to generate short stories, poems, or other imaginative text.
- Evaluating its factual knowledge and research skills by asking it to summarize information on various topics or provide informed perspectives on current events.
- Testing its flexibility by giving it prompts that require a combination of skills, like generating a persuasive essay or conducting a Socratic dialogue.
By experimenting with a diverse set of prompts and tasks, you can gain a deeper understanding of the model's strengths, limitations, and potential applications.
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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