| Property | Value |
|---|---|
| Base Model | unsloth/meta-llama-3.1-8b-bnb-4bit |
| License | Apache-2.0 |
| Developer | prithivMLmods |
| Training Status | In Training Phase |
What is Llama-3.1-8B-4bit-axium?
Llama-3.1-8B-4bit-axium is a specialized 4-bit quantized version of the Llama 3.1 language model, fine-tuned using advanced optimization techniques including Unsloth and Hugging Face's TRL library. This model represents an efficient implementation of the 8B parameter architecture, designed for improved performance while maintaining a smaller memory footprint.
Implementation Details
The model utilizes a sophisticated training configuration with carefully selected hyperparameters. It employs an 8-bit AdamW optimizer with a linear learning rate scheduler, operating at a learning rate of 2e-4. The training process involves a batch size of 2 with 4 gradient accumulation steps, and implements either FP16 or BF16 precision based on hardware support.
- Optimized using Unsloth for 2x faster training speed
- Implements 4-bit quantization for efficient memory usage
- Utilizes advanced training techniques with TRL library
- Configured with warmup steps and linear learning rate decay
Core Capabilities
- Efficient text generation with reduced memory footprint
- Optimized for inference performance
- Supports both FP16 and BF16 precision modes
- Designed for production deployment with text-generation-inference support
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimized training approach using Unsloth, which achieves 2x faster training speeds compared to conventional methods. Additionally, its 4-bit quantization makes it highly efficient for deployment while maintaining performance.
Q: What are the recommended use cases?
While still in the training phase, this model is designed for text generation tasks requiring efficient resource utilization. It's particularly suitable for applications where memory constraints are a concern, thanks to its 4-bit quantization.