h2o-danube3-4b-chat
| Property | Value |
|---|---|
| Parameter Count | 3.96B |
| License | Apache 2.0 |
| Paper | Technical Report |
| Context Length | 8,192 tokens |
| Architecture | Modified Llama 2 |
What is h2o-danube3-4b-chat?
h2o-danube3-4b-chat is an advanced language model developed by H2O.ai, specifically optimized for mobile deployment while maintaining robust performance. Built on a modified Llama 2 architecture, this model achieves impressive capabilities with just 4 billion parameters, making it efficient for practical applications.
Implementation Details
The model features a sophisticated architecture with 24 layers, 32 attention heads, and 8 query groups. It utilizes a 3840-dimensional embedding space and employs the Mistral tokenizer with a 32,000-token vocabulary. Training extends to a context length of 8,192 tokens, enabling handling of lengthy conversations.
- BF16 tensor format for optimal performance
- Supports both 4-bit and 8-bit quantization
- Compatible with multi-GPU deployment
- Implements efficient attention mechanisms with rotary embeddings
Core Capabilities
- Strong performance on benchmark tests (61.42% average accuracy)
- Excellent first-turn conversation quality (MT-Bench score: 7.28)
- Specialized for chat applications
- Native mobile device support
- Offline operation capability
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimal balance between size and performance, specifically designed for mobile deployment while maintaining high-quality outputs. Its architecture modifications allow it to run efficiently on personal devices while delivering competitive benchmark results.
Q: What are the recommended use cases?
The model excels in conversational applications, particularly in scenarios requiring offline processing or mobile deployment. It's ideal for personal AI assistants, chat applications, and situations where a balance between model size and performance is crucial.
