Llama-3.1-8B-4bit-axium
| Property | Value |
|---|---|
| Base Model | unsloth/meta-llama-3.1-8b-bnb-4bit |
| License | Apache-2.0 |
| Author | prithivMLmods |
| Training Status | In Progress |
What is Llama-3.1-8B-4bit-axium?
Llama-3.1-8B-4bit-axium is a specialized variant of Meta's Llama 3.1 model, quantized to 4-bit precision for improved efficiency. This model leverages the Unsloth optimization framework for accelerated training performance, achieving up to 2x faster training speeds compared to conventional approaches.
Implementation Details
The model utilizes a sophisticated training configuration with AdamW 8-bit optimizer, linear learning rate scheduling, and carefully tuned hyperparameters. Training is conducted with a batch size of 2 and gradient accumulation steps of 4, maintaining a balance between memory efficiency and training effectiveness.
- Learning Rate: 2e-4 with linear scheduling
- Weight Decay: 0.01
- Training Epochs: 1 with 60 max steps
- Warmup Steps: 5
- Mixed Precision Training: Adaptive FP16/BF16 based on hardware support
Core Capabilities
- Optimized Text Generation
- 4-bit Quantization for Efficiency
- Accelerated Training via Unsloth
- Enhanced Memory Efficiency
- TRL Library Integration
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful Llama 3.1 architecture with 4-bit quantization and Unsloth optimization, offering a balance between performance and efficiency. The integration with TRL library and custom training configuration makes it particularly suitable for specific text generation tasks.
Q: What are the recommended use cases?
While still in the training phase, this model is designed for text generation tasks. However, users should note that as it's not the final version, it may exhibit artifacts and inconsistencies in some scenarios. It's best suited for experimental and development purposes until the training is complete.
