Gemma-3-27b-it-bnb-4bit
| Property | Value |
|---|---|
| Model Size | 27B parameters |
| Context Length | 128K tokens |
| Training Tokens | 14 trillion |
| Quantization | 4-bit precision |
| Author | Google DeepMind (Unsloth optimization) |
| Technical Report | Link |
What is gemma-3-27b-it-bnb-4bit?
Gemma-3-27b-it-bnb-4bit is a quantized version of Google's Gemma 3 model, optimized by Unsloth for efficient inference. This model represents the largest variant in the Gemma 3 family, trained on 14 trillion tokens and featuring multimodal capabilities for both text and image processing. The 4-bit quantization significantly reduces memory requirements while maintaining high performance.
Implementation Details
The model utilizes binary neural networks (BNB) quantization techniques to compress the original model into a 4-bit format, enabling deployment in resource-constrained environments. It maintains the full 128K context window of the original architecture while supporting inference across 140+ languages.
- Optimized for TPU hardware architecture
- Implements JAX and ML Pathways for efficient processing
- Supports both text and image inputs (896x896 resolution)
- Generates up to 8192 tokens in output
Core Capabilities
- Multimodal processing of text and images
- Strong performance in reasoning tasks (85.6% on HellaSwag)
- Advanced multilingual support (74.3% on MGSM)
- Robust image understanding (85.6% on DocVQA)
- Code generation capabilities (48.8% on HumanEval)
Frequently Asked Questions
Q: What makes this model unique?
This model combines the power of Google's Gemma 3 architecture with Unsloth's optimization techniques, offering state-of-the-art performance in a memory-efficient 4-bit format. Its multimodal capabilities and extensive context window make it particularly versatile for various applications.
Q: What are the recommended use cases?
The model excels in content creation, chatbot applications, research tasks, and educational tools. It's particularly strong in multimodal tasks involving both text and images, making it suitable for document analysis, visual question answering, and complex reasoning tasks.
