h2o-danube-1.8b-chat
| Property | Value |
|---|---|
| Parameter Count | 1.8B |
| License | Apache 2.0 |
| Paper | Technical Report |
| Context Length | 16,384 tokens |
| Architecture | Modified Llama 2 with Mistral sliding window |
What is h2o-danube-1.8b-chat?
h2o-danube-1.8b-chat is an advanced language model developed by H2O.ai that combines the architectural strengths of Llama 2 with Mistral's sliding window attention mechanism. This model represents the final evolution in a three-stage development process, incorporating both Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) tuning.
Implementation Details
The model features a sophisticated architecture with 24 layers, 32 attention heads, and 8 query groups. It utilizes a 2560-dimensional embedding space and maintains a vocabulary size of 32,000 tokens. The implementation of a 4,096-token sliding window attention mechanism enables efficient processing of long sequences up to 16,384 tokens.
- Advanced attention mechanism with sliding window support
- Optimized for both short and long-form content generation
- Trained on 7 diverse datasets including UltraFeedback, OpenOrca, and MetaMathQA
- Support for 8-bit and 4-bit quantization
Core Capabilities
- Strong performance in commonsense reasoning (67.51% on ARC-easy)
- Robust reading comprehension abilities (77.89% on BoolQ)
- Excellent task completion in general knowledge domains (76.71% on PiQA)
- Efficient handling of long-form conversations and content generation
Frequently Asked Questions
Q: What makes this model unique?
The model uniquely combines a modified Llama 2 architecture with Mistral's sliding window attention, offering an efficient balance between performance and resource usage at only 1.8B parameters. It's particularly notable for achieving strong benchmark results despite its relatively compact size.
Q: What are the recommended use cases?
The model excels in conversational AI applications, question-answering tasks, and general content generation. It's particularly well-suited for applications requiring both accuracy and efficiency, especially where context length up to 16,384 tokens is beneficial.
