OpenOrca-Platypus2-13B-GPTQ
Maintainer: TheBloke
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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 OpenOrca-Platypus2-13B-GPTQ is a large language model created by Open-Orca and refined by TheBloke. It is based on the Llama 2 architecture and has been trained on a combination of the OpenOrca dataset and a custom dataset focused on STEM and logic tasks. This model builds on the previous OpenOrca Platypus2 13B model, incorporating improvements to its performance and capabilities.
The OpenOrca-Platypus2-13B-GPTQ model is available in various quantized versions optimized for different hardware and performance requirements. These include 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit GPTQ models, as well as 2-8 bit GGUF models for CPU and GPU inference.
Model inputs and outputs
Inputs
- Prompts: The model takes in natural language prompts that describe a task or request.
- Instructions: The model can also accept structured instruction-based prompts, such as the Alpaca-InstructOnly format.
Outputs
- Text generation: The primary output of the model is generated text, which can range from short responses to long-form narratives.
- Task completion: The model is capable of understanding and completing a variety of tasks described in the input prompts.
Capabilities
The OpenOrca-Platypus2-13B-GPTQ model excels at a wide range of language tasks, including creative writing, question answering, code generation, and more. It has demonstrated strong performance on various benchmarks, including the HuggingFace Leaderboard, AGIEval, and BigBench-Hard. Compared to the original OpenOrca Platypus2 13B model, this version offers improved performance, lower hallucination rates, and longer responses.
What can I use it for?
The OpenOrca-Platypus2-13B-GPTQ model can be used for a variety of applications, such as:
- Content generation: Create engaging stories, articles, or product descriptions.
- Conversational AI: Build chatbots and virtual assistants that can engage in natural language interactions.
- Task completion: Develop applications that can understand and complete complex instructions, such as code generation, math problem-solving, or creative tasks.
- Research and development: Use the model as a starting point for further fine-tuning or as a benchmark for comparing language model performance.
Things to try
One interesting aspect of the OpenOrca-Platypus2-13B-GPTQ model is its ability to generate long, detailed responses while maintaining coherence and factual accuracy. You can try providing the model with open-ended prompts or instructions and see how it responds. For example, you could ask it to write a story about llamas or solve a complex logic puzzle.
Another avenue to explore is the model's performance on specialized tasks, such as technical writing, scientific analysis, or legal document review. By fine-tuning the model on domain-specific data, you may be able to unlock new capabilities that are tailored to your specific needs.
Verifying the responses for safety and factual accuracy is also an important consideration when using large language models. Developing robust testing and monitoring procedures can help ensure the model is behaving as expected and not producing harmful or inaccurate outputs.
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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