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Gpt J 6B 8bit

Developed by hivemind
This is the 8-bit quantized version of EleutherAI's GPT-J 6B parameter model, optimized for running and fine-tuning on limited GPU resources (e.g., Colab or 1080Ti).
Downloads 176
Release Time : 3/2/2022

Model Overview

Through 8-bit weight quantization, gradient checkpointing, and LoRA technology, it enables large language models to run and fine-tune on consumer-grade GPUs while maintaining model quality close to the original.

Model Features

8-bit Dynamic Quantization
Large weight matrices are stored in 8-bit and dynamically dequantized to float16/32 during computation, significantly reducing memory usage while maintaining computational accuracy.
Gradient Checkpointing
Only one activation value is stored per layer, reducing memory usage by 30%, but training speed is correspondingly reduced.
LoRA Fine-tuning Support
Combines Low-Rank Adaptation (LoRA) with 8-bit Adam optimizer for efficient parameter fine-tuning.
Consumer-Grade GPU Compatibility
The optimized model can run on single GPUs with 11GB VRAM (e.g., 1080Ti), making it suitable for environments like Colab.

Model Capabilities

Text generation
Language modeling
Model fine-tuning

Use Cases

Deployment in resource-constrained environments
Running in Colab Notebook
Run a 6B parameter large model on free Colab instances.
Successfully achieved inference on consumer-grade GPUs like K80/T4.
Customized fine-tuning
Domain adaptation training
Fine-tune on domain-specific data using LoRA technology.
Adapts to specialized domains while preserving the base model's capabilities.
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