h2o-danube-1.8b-base
Maintainer: h2oai
43
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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
h2o-danube-1.8b-base is a foundation model trained by H2O.ai with 1.8 billion parameters. This model is part of a series of three versions released by H2O.ai, which also include the h2o-danube-1.8b-sft (SFT tuned) and h2o-danube-1.8b-chat (SFT + DPO tuned) models. The base model is designed as a general-purpose language model, while the SFT and chat versions are fine-tuned for specific tasks.
Model inputs and outputs
Inputs
- Text: The model can take in text of up to 16,384 tokens as input.
Outputs
- Generated text: The model can generate coherent and contextually relevant text in response to the input.
Capabilities
h2o-danube-1.8b-base has been evaluated on a range of benchmarks testing commonsense, world-knowledge, and reading comprehension. The model achieves strong performance, scoring over 60% on tasks like ARC-easy, BoolQ, Hellaswag, and PiQA.
What can I use it for?
The h2o-danube-1.8b-base model can be a powerful tool for a variety of natural language processing tasks. For example, you could use it for:
- Content generation: Generating coherent and contextually relevant text on a wide range of topics.
- Question answering: Answering questions that require commonsense reasoning and world knowledge.
- Summarization: Summarizing long-form text while preserving key information.
To get started, you can fine-tune the model on your specific task using the instructions provided in the usage section.
Things to try
One interesting aspect of the h2o-danube-1.8b-base model is its ability to handle long-form input. By leveraging the model's 16,384 token context length, you can generate coherent and contextually relevant text for tasks that require processing of lengthy passages. This could be useful for applications like document summarization or long-form content generation.
Additionally, the model's strong performance on commonsense and world-knowledge benchmarks suggests it could be a valuable resource for building intelligent assistants or chatbots that can engage in natural conversations and provide helpful information to users.
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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