Mistral-7B-Instruct-v0.3-GPTQ
| Property | Value |
|---|---|
| Parameter Count | 1.21B |
| License | Apache 2.0 |
| Quantization | 4-bit GPTQ |
| Base Model | Mistral-7B-Instruct-v0.3 |
What is Mistral-7B-Instruct-v0.3-GPTQ?
Mistral-7B-Instruct-v0.3-GPTQ is a quantized version of the Mistral-7B-Instruct-v0.3 Large Language Model, optimized for efficient deployment while maintaining performance. This model represents a significant advancement in the Mistral series, featuring an extended vocabulary of 32,768 tokens and support for both v3 Tokenizer and function calling capabilities.
Implementation Details
The model utilizes GPTQ 4-bit quantization techniques to reduce model size while preserving performance. It's implemented using the transformers library and can be easily deployed using standard PyTorch infrastructure.
- 4-bit precision quantization for efficient deployment
- Supports automatic device mapping
- Compatible with Hugging Face's transformers library
- Includes both FP16 and I32 tensor support
Core Capabilities
- Text generation and conversational AI tasks
- Extended vocabulary handling (32,768 tokens)
- Function calling support
- Compatible with text-generation-inference endpoints
- Efficient memory usage through quantization
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its efficient 4-bit quantization while maintaining the advanced capabilities of Mistral-7B-Instruct-v0.3, including extended vocabulary and function calling features. It offers a practical balance between performance and resource utilization.
Q: What are the recommended use cases?
The model is particularly well-suited for conversational AI applications, creative writing tasks, and general text generation scenarios where efficient deployment is crucial. However, users should note that it doesn't include built-in moderation mechanisms.
