MPT-30B-Chat GGML
| Property | Value |
|---|---|
| License | CC-By-NC-SA-4.0 |
| Context Length | 8K tokens |
| Architecture | Modified decoder-only transformer |
| Papers | FlashAttention, ALiBi, QK LayerNorm |
What is MPT-30B-chat-GGML?
MPT-30B-Chat GGML is a quantized version of MosaicML's powerful chat model, specifically optimized for efficient CPU and GPU inference. This implementation provides various quantization options from 4-bit to 8-bit, allowing users to balance between performance and resource usage. The model maintains the base architecture's 8K token context window while incorporating advanced features like FlashAttention and ALiBi.
Implementation Details
The model is available in multiple quantization formats, ranging from 4-bit (q4_0, q4_1) to 8-bit (q8_0) versions. The file sizes vary from 16.85GB to 31.83GB, with corresponding RAM requirements between 19.35GB and 34.33GB. It's optimized for use with specific tools like KoboldCpp and the ctransformers Python library.
- Supports GPU acceleration through OpenCL in KoboldCpp
- Implements advanced attention mechanisms including FlashAttention
- Features 8K token context length with ALiBi position encoding
- Multiple quantization options for different performance/quality tradeoffs
Core Capabilities
- Multi-turn conversation handling
- Instruction following and chat interactions
- Support for various inference engines
- Flexible deployment options for different hardware configurations
- Enhanced performance through optimized attention mechanisms
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient GGML implementation of the powerful MPT-30B architecture, offering various quantization options while maintaining the 8K context window and incorporating advanced features like FlashAttention and ALiBi.
Q: What are the recommended use cases?
The model is ideal for applications requiring sophisticated chat interactions and instruction following, particularly when deployment needs to balance between performance and resource usage. It's especially suitable for systems using KoboldCpp or ctransformers for inference.
