Gemma-2-2b-bnb-4bit
| Property | Value |
|---|---|
| Parameter Count | 1.63B parameters |
| License | Gemma License |
| Precision | 4-bit quantization |
| Base Model | Google/gemma-2-2b |
What is gemma-2-2b-bnb-4bit?
Gemma-2-2b-bnb-4bit is an optimized version of Google's Gemma 2B language model, specifically quantized to 4-bit precision using the bitsandbytes library and enhanced by Unsloth. This implementation significantly reduces memory usage while maintaining model performance, making it more accessible for resource-constrained environments.
Implementation Details
The model leverages advanced quantization techniques to compress the original Gemma 2B model while preserving its capabilities. It supports multiple tensor types including F32, BF16, and U8, offering flexibility in deployment scenarios.
- 4-bit precision quantization for efficient memory usage
- 2.4x faster inference speed compared to the base model
- 58% reduced memory footprint
- Compatible with text-generation-inference endpoints
Core Capabilities
- Efficient text generation and processing
- Optimized for English language tasks
- Supports integration with Transformers library
- Compatible with various deployment options including GGUF and vLLM
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its optimal balance between performance and resource efficiency, achieved through Unsloth's optimization techniques and 4-bit quantization, making it particularly suitable for deployment on resource-constrained systems.
Q: What are the recommended use cases?
The model is ideal for applications requiring efficient text generation and processing, particularly in scenarios where memory and computational resources are limited. It's especially suitable for deployment in production environments where speed and resource efficiency are crucial.
