h2ogpt-4096-llama2-7b-chat
| Property | Value |
|---|---|
| Parameter Count | 6.74B |
| Model Type | LLaMA 2 |
| License | LLaMA 2 |
| Tensor Type | FP16 |
| Context Window | 4096 tokens |
What is h2ogpt-4096-llama2-7b-chat?
h2ogpt-4096-llama2-7b-chat is H2O.ai's implementation of Meta's LLaMA 2 7B chat model, specifically optimized for conversational AI applications. This model represents a sophisticated adaptation of the original LLaMA 2 architecture, maintaining its powerful language understanding capabilities while being optimized for practical deployment.
Implementation Details
The model is built on the LlamaForCausalLM architecture, featuring 32 decoder layers with a 4096-dimensional embedding space. Each layer implements self-attention mechanisms with specialized components like LlamaRotaryEmbedding and LlamaMLP with 11008-dimensional intermediate representations.
- 4096-dimensional embedding vectors
- 32 transformer decoder layers
- FP16 precision for efficient inference
- Optimized attention mechanism with rotary embeddings
Core Capabilities
- Text generation and completion tasks
- Conversational AI interactions
- Extended context window of 4096 tokens
- Efficient processing with FP16 precision
- Integration with H2O.ai's ecosystem
Frequently Asked Questions
Q: What makes this model unique?
This model stands out through its optimization by H2O.ai, offering a balance between performance and efficiency while maintaining the robust capabilities of the LLaMA 2 architecture. It features a substantial 4096-token context window and is specifically tuned for chat applications.
Q: What are the recommended use cases?
The model is particularly well-suited for conversational AI applications, text generation tasks, and can be effectively used in scenarios requiring extended context understanding. It's ideal for chatbots, content generation, and interactive AI applications where natural language understanding is crucial.
