Zamba2-7B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 7.53B |
| Model Type | Hybrid SSM-Attention |
| License | Apache 2.0 |
| Context Length | 16k tokens |
| Format | BF16 |
What is Zamba2-7B-Instruct?
Zamba2-7B-Instruct is an innovative hybrid language model that combines state-space modeling (Mamba2) with transformer architecture. Fine-tuned specifically for instruction-following tasks, it represents a significant advancement in efficient AI model design. The model achieves impressive benchmark scores while maintaining lower inference latency and memory footprint compared to traditional transformer-based models.
Implementation Details
The model's architecture features a unique backbone of Mamba2 layers interleaved with shared attention layers. It employs LoRA projection matrices for the shared MLP, allowing each block to specialize while maintaining parameter efficiency. The context window has been extended from 4k to 16k tokens through optimized rotary position embeddings.
- Achieves 69.95 on IFEval and 33.33 on BBH benchmarks
- Implements shared attention weights to minimize parameter costs
- Features concatenated embeddings for improved information maintenance
- Supports long-context processing up to 16k tokens
Core Capabilities
- Superior instruction-following performance
- Rapid inference with low latency
- Efficient memory usage
- Strong reasoning capabilities
- Extended context handling
Frequently Asked Questions
Q: What makes this model unique?
The model's hybrid architecture combining Mamba2 state-space modeling with transformer blocks allows it to achieve high performance with lower computational overhead. It delivers particularly strong results in instruction-following tasks while maintaining efficient memory usage and fast inference times.
Q: What are the recommended use cases?
Zamba2-7B-Instruct is well-suited for generalist applications requiring strong instruction-following capabilities, particularly in scenarios where rapid response times and efficient resource usage are important. It excels in tasks requiring reasoning and can handle extended context lengths up to 16k tokens.
