Mistral-7B-Instruct-v0.1-GPTQ
| Property | Value |
|---|---|
| Parameter Count | 7B |
| Model Type | Instruct-tuned Language Model |
| License | Apache 2.0 |
| Quantization | 4-bit GPTQ |
| Size | 4.16 GB - 8.17 GB (depending on configuration) |
What is Mistral-7B-Instruct-v0.1-GPTQ?
This is a quantized version of Mistral AI's instruction-tuned language model, optimized for efficient deployment while maintaining high performance. It uses GPTQ quantization to reduce model size and memory requirements, making it more accessible for deployment on consumer hardware.
Implementation Details
The model leverages advanced architecture features including Grouped-Query Attention and Sliding-Window Attention, combined with a Byte-fallback BPE tokenizer. Multiple GPTQ configurations are available, ranging from 4-bit to 8-bit quantization with different group sizes (32g to 128g).
- Multiple quantization options (4-bit and 8-bit variants)
- ExLlama compatibility for efficient inference
- Transformers integration with specific commit support
- Optimized for 32K context length
Core Capabilities
- Instruction-following with [INST] and [/INST] formatting
- Efficient memory usage through quantization
- Flexible deployment options with different precision levels
- Support for long context windows
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful Mistral-7B architecture with efficient GPTQ quantization, offering multiple precision options to balance between performance and resource usage. It's specifically optimized for instruction-following tasks while maintaining the base model's capabilities.
Q: What are the recommended use cases?
The model is ideal for deployment in scenarios requiring instruction-following capabilities with limited computational resources. It's particularly suitable for applications needing efficient inference on consumer GPUs while maintaining high-quality output.
