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Bitnet B1 58 Xl Q8 0 Gguf

Developed by BoscoTheDog
BitNet b1.58 is a large language model with 1.58-bit quantization. It reduces the computational resource requirements by lowering the weight precision while maintaining performance close to that of a full-precision model.
Downloads 326
Release Time : 6/23/2024

Model Overview

This model is a reproduction of the BitNet b1.58 paper. It was trained on 100B tokens using the RedPajama dataset, achieving an efficient 1.58-bit quantized LLM.

Model Features

1.58-bit quantization
Adopts an innovative 1.58-bit quantization technology, significantly reducing model storage and computational requirements
Efficient training
Optimizes the training process using a two-stage learning rate and weight decay strategy
Open-source model
All trained model parameters are fully open-source
Close to full-precision performance
Can still maintain performance close to that of an FP16 precision model under quantization

Model Capabilities

Text generation
Zero-shot learning
Language understanding
Question-answering tasks

Use Cases

Natural language processing
Open-domain question answering
Answer open-ended questions in various fields
Performs well in benchmarks such as ARC and HellaSwag
Text generation
Generate coherent and meaningful text
The perplexity (PPL) is close to that of a full-precision model
Research applications
Efficient LLM research
Study the impact of low-bit quantization on LLM performance
Provides a reference for the development of efficient LLMs
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