30B-Lazarus
Maintainer: CalderaAI
119
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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 30B-Lazarus model is the result of an experimental approach to combining several large language models and specialized LoRAs (Layers of Residual Adaption) to create an ensemble model with enhanced capabilities. The composition includes models such as SuperCOT, gpt4xalpaca, and StoryV2, along with the manticore-30b-chat-pyg-alpha and Vicuna Unlocked LoRA models. The maintainer, CalderaAI, indicates that this experimental approach aims to additively apply desired features without paradoxically watering down the model's effective behavior.
Model inputs and outputs
The 30B-Lazarus model is a text-to-text AI model, meaning it takes text as input and generates text as output. The model is primarily instructed-based, with the Alpaca instruct format being the primary input format. However, the maintainer suggests that the Vicuna instruct format may also work.
Inputs
- Instruction: Text prompts or instructions for the model to follow, often in the Alpaca or Vicuna instruct format.
- Context: Additional context or information provided to the model to inform its response.
Outputs
- Generated text: The model's response to the provided input, which can range from short answers to longer, more detailed text.
Capabilities
The 30B-Lazarus model is designed to have enhanced capabilities in areas like reasoning, storytelling, and task-completion compared to the base LLaMA model. By combining several specialized models and LoRAs, the maintainer aims to create a more comprehensive and capable language model. However, the maintainer notes that further experimental testing and evaluation is required to fully understand the model's capabilities and limitations.
What can I use it for?
The 30B-Lazarus model could potentially be used for a variety of natural language processing tasks, such as question answering, text generation, and problem-solving. The maintainer suggests that the model may be particularly well-suited for text-based adventure games or interactive storytelling applications, where its enhanced storytelling and task-completion capabilities could be leveraged.
Things to try
When using the 30B-Lazarus model, the maintainer recommends experimenting with different presets and instructions to see how the model responds. They suggest trying out the "Godlike" and "Storywriter" presets in tools like KoboldAI or Text-Generation-WebUI, and adjusting parameters like output length and temperature to find the best settings for your use case. Additionally, exploring the model's ability to follow chain-of-thought reasoning or provide detailed, creative responses to open-ended prompts could be an interesting area to investigate further.
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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