Mamba-130m-hf
| Property | Value |
|---|---|
| Parameter Count | 129M parameters |
| Model Type | Text Generation |
| Architecture | Mamba Selective State Space Sequence Model |
| Tensor Type | F32 |
What is mamba-130m-hf?
Mamba-130m-hf is a lightweight implementation of the innovative Mamba architecture, designed for efficient text generation tasks. It represents a significant departure from traditional transformer models by utilizing selective state space sequences, offering an efficient alternative for natural language processing tasks.
Implementation Details
The model is implemented using the Transformers library and requires specific dependencies including causal_conv1d and mamba-ssm for optimal performance. It supports both standard inference and optimized CUDA kernels when properly configured.
- Transformers-compatible architecture
- Optimized CUDA kernel support
- PEFT fine-tuning capability
- Integrated with HuggingFace ecosystem
Core Capabilities
- Text generation with controllable output length
- Support for batch processing
- Compatible with PEFT for efficient fine-tuning
- Flexible tokenization through AutoTokenizer
Frequently Asked Questions
Q: What makes this model unique?
This model implements the Mamba architecture at a compact 129M parameter size, making it more accessible for deployment while maintaining efficient text generation capabilities. Its integration with CUDA optimization sets it apart in terms of performance.
Q: What are the recommended use cases?
The model is well-suited for text generation tasks, particularly when computational resources are limited. It's especially useful for developers looking to implement efficient language models with PEFT fine-tuning capabilities.
