Alpacino30b
Maintainer: digitous
68
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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
Alpacino30b is a merged model that combines features of the Alpaca model with additional capabilities from models focused on chain-of-thought reasoning and storytelling. The maintainer, digitous, describes it as a "triple model merge" that results in a comprehensive boost to Alpaca's reasoning and story writing abilities. It uses Alpaca as the backbone to maintain the instruct format that Alpaca is known for.
Similar models like SuperCOT-LoRA also aim to enhance LLaMA models with additional capabilities for better logical reasoning and task completion. These models leverage datasets like Alpaca-CoT, CodeAlpaca, and Conala to fine-tune the base LLaMA model.
Model inputs and outputs
Inputs
- Text prompt provided in an instruction format
Outputs
- Detailed, creative text responses that demonstrate improved reasoning and storytelling abilities compared to the base Alpaca model
Capabilities
Alpacino30b exhibits enhanced capabilities in areas like logical reasoning, chain-of-thought problem solving, and creative storytelling. The maintainer provides an example use case of using the model to power an interactive text-based adventure game, where the model can respond with rich, imaginative descriptions that progress the narrative.
What can I use it for?
Projects that could benefit from Alpacino30b's improved reasoning and storytelling skills include interactive fiction, creative writing assistants, and chatbots for engaging conversations. The model could also be useful for research into language model capabilities and prompt engineering. As with any large language model, users should exercise caution and test for potential biases or safety issues before deploying it in production.
Things to try
Experiment with prompts that require the model to demonstrate its chain-of-thought capabilities, such as multi-step reasoning problems or open-ended storytelling tasks. Try providing the model with different character backstories or narrative prompts to see how it can generate coherent and engaging storylines. Additionally, you could explore using the model in a text-based adventure game setting, as suggested by the maintainer, to see how it handles dynamic user interactions and evolving narratives.
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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