StableBeluga2-70B-GPTQ
| Property | Value |
|---|---|
| Base Model | LLaMA2 70B |
| License | LLaMA2 |
| Parameters | 70 Billion |
| Quantization | GPTQ (Multiple Variants) |
| Language | English |
What is StableBeluga2-70B-GPTQ?
StableBeluga2-70B-GPTQ is a quantized version of Stability AI's powerful language model, built on the LLaMA2 70B architecture and fine-tuned on an Orca-style dataset. This version has been optimized by TheBloke to provide multiple quantization options, making it more accessible for various hardware configurations while maintaining performance.
Implementation Details
The model offers several quantization variants, including 3-bit and 4-bit options with different group sizes (32g, 64g, 128g) and act-order configurations. File sizes range from 26.78GB to 40.66GB, allowing users to choose the best balance between VRAM usage and model quality.
- Multiple GPTQ variants for different hardware requirements
- Compatible with AutoGPTQ, Transformers, and ExLlama (4-bit versions)
- Includes quantize_config.json for automatic parameter loading
- Uses a specific prompt template for optimal interaction
Core Capabilities
- High-quality text generation and conversation
- Follows instructions effectively with safety considerations
- Supports context length of 4096 tokens
- Multiple deployment options through various frameworks
- Efficient resource utilization through quantization
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful StableBeluga2 architecture with various quantization options, making it more accessible for deployment while maintaining high performance. The multiple GPTQ variants allow users to choose the optimal configuration for their specific hardware constraints.
Q: What are the recommended use cases?
The model is well-suited for conversational AI applications, text generation, and instruction-following tasks. It's particularly useful in scenarios where deployment efficiency is crucial while maintaining high-quality outputs. Users can choose from different quantization options based on their specific VRAM constraints and quality requirements.
