h2o-danube3-4b-chat
Maintainer: h2oai
55
📊
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-danube3-4b-chat
is a large language model with 4 billion parameters, developed by H2O.ai. It is based on the Llama 2 architecture and has been fine-tuned for chatbot-style conversations. The model is available in two versions - a base model and a chat-specific model. It was trained using H2O LLM Studio, a platform for training large language models.
Model inputs and outputs
The h2o-danube3-4b-chat
model can take a wide range of conversational inputs and generate coherent and contextual responses. It uses the Mistral tokenizer with a vocabulary size of 32,000 and can handle sequences up to 8,192 tokens long.
Inputs
- Conversational prompts and messages
- Questions or statements on a variety of topics
Outputs
- Relevant and contextual responses to conversational prompts
- Informative answers to questions
- Coherent and natural-sounding text generation
Capabilities
The h2o-danube3-4b-chat
model can engage in open-ended conversations, answer questions, and generate human-like text on a wide range of topics. It has been specifically tuned for chatbot-style interactions and can maintain context and coherence throughout a conversation.
What can I use it for?
The h2o-danube3-4b-chat
model can be used to build intelligent chatbots, virtual assistants, and conversational interfaces for a variety of applications. It could be used in customer service, education, entertainment, and more. The model can also be fine-tuned further for specific use cases or domains.
Things to try
You can experiment with the h2o-danube3-4b-chat
model by using it to generate responses to conversational prompts, answer questions, or continue a given dialogue. Try giving the model complex or open-ended prompts to see how it handles maintaining context and coherence. You can also explore how the model performs on specific topics or domains that interest you.
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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