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Mistral Small 24B Instruct 2501 GPTQ G128 W4A16 MSE

Developed by ConfidentialMind
This is the 4-bit quantized version of the mistralai/Mistral-Small-24B-Instruct-2501 model, quantized by ConfidentialMind.com, achieving a smaller and faster model with minimal performance loss.
Downloads 93
Release Time : 2/15/2025

Model Overview

A 4-bit quantized model based on Mistral-Small-24B-Instruct-2501, primarily used for text generation tasks, suitable for scenarios requiring efficient inference.

Model Features

Efficient 4-bit quantization
Uses GPTQ technology to achieve 4-bit precision quantization, significantly reducing model size and inference time.
Group size 128
Employs a quantization strategy with group size 128, balancing model accuracy and inference efficiency.
MSE optimization
Uses MSE (Mean Squared Error) and a higher damping factor for quantization optimization, reducing loss and perplexity.
Single GPU support
Optimized to run efficiently on a single NVIDIA A100 GPU (80GB VRAM).

Model Capabilities

Text generation
Efficient inference
Quantized model deployment

Use Cases

Efficient text generation
Rapid content generation
Quickly generates high-quality text content in resource-constrained environments.
Significantly improves inference speed while maintaining high generation quality.
Research applications
Quantization technology research
Serves as a case study for large model quantization techniques.
Demonstrates the application effects of 4-bit quantization on large language models.
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