h2o-danube2-1.8b-chat
| Property | Value |
|---|---|
| Parameter Count | 1.83B |
| License | Apache 2.0 |
| Context Length | 8,192 tokens |
| Research Paper | Technical Report |
| Architecture | Modified Llama 2 with Mistral tokenizer |
What is h2o-danube2-1.8b-chat?
h2o-danube2-1.8b-chat is an advanced language model developed by H2O.ai, representing a sophisticated chat-tuned variant of their 1.8B parameter architecture. Built on a modified Llama 2 architecture and utilizing the Mistral tokenizer, this model has been optimized through both Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) to enhance its conversational capabilities.
Implementation Details
The model features a carefully balanced architecture with 24 layers, 32 attention heads, and 8 query groups. It employs a 2560-dimensional embedding space and operates with a vocabulary size of 32,000 tokens. The model supports an impressive context length of 8,192 tokens, making it suitable for handling extended conversations and complex tasks.
- 24 transformer layers with advanced attention mechanisms
- BF16 precision for optimal performance and memory usage
- Implements grouped-query attention with 8 query groups
- Comprehensive benchmarking results including 48.44% average on key metrics
Core Capabilities
- Strong performance on MT-Bench with 5.79 average score
- Effective on various tasks including ARC-challenge (43.43%) and Hellaswag (73.54%)
- Supports efficient quantization (4-bit and 8-bit) and multi-GPU deployment
- Designed for both general conversation and specialized tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient architecture that balances performance and size, making it particularly suitable for deployment in resource-conscious environments while maintaining strong capabilities across various benchmarks. It's part of a family of models that includes base, SFT, and chat variants, allowing users to choose the most appropriate version for their needs.
Q: What are the recommended use cases?
The model is well-suited for conversational AI applications, text generation tasks, and general language understanding. Its 8K context window makes it particularly useful for handling longer conversations and documents, while its efficient architecture allows for deployment in production environments with reasonable computational requirements.
