Llama HPU Configuration
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Author | Habana |
| Framework | Optimum Habana |
What is llama?
This is a specialized configuration package designed to enable running Llama models on Habana's Gaudi processors (HPU). It's part of the Optimum Habana framework, which bridges Hugging Face Transformers and Diffusers libraries with Habana's hardware.
Implementation Details
The configuration provides essential optimizations for HPU execution, including custom implementations of common operations and training utilities. It contains no model weights, but rather serves as a configuration template for HPU-specific features.
- Fused AdamW implementation for optimized training
- Fused gradient norm clipping operator
- PyTorch autocast mixed precision support
- Lazy mode execution capability
Core Capabilities
- Seamless integration with Hugging Face Transformers
- Support for single and multi-HPU configurations
- Mixed precision training optimization
- LoRA fine-tuning support
Frequently Asked Questions
Q: What makes this model unique?
This configuration is specifically optimized for Habana's Gaudi processors, enabling efficient training and inference of Llama models on HPU hardware. It includes custom implementations of critical operations that leverage Habana's architecture.
Q: What are the recommended use cases?
This configuration is ideal for users looking to train or fine-tune Llama models on Habana Gaudi processors, particularly for large-scale language modeling tasks. It's especially useful for scenarios requiring efficient multi-HPU training with mixed precision.
