MoMo-72B-lora-1.8.7-DPO
| Property | Value |
|---|---|
| Parameter Count | 72.3B |
| License | MIT |
| Base Model | QWEN-72B |
| Training Method | DPO with LoRA |
| Platform | MoAI (AMD MI250 GPU) |
What is MoMo-72B-lora-1.8.7-DPO?
MoMo-72B-lora-1.8.7-DPO is an advanced language model trained using Direct Preference Optimization (DPO) on the MoMo-72B-LoRA-V1.4 base model. Built on QWEN-72B architecture, it implements LoRA (Low-Rank Adaptation) for efficient training and optimization.
Implementation Details
The model leverages several key datasets including SlimOrca, Truthy, and Orca DPO pairs for training. It's implemented using the Transformers library and supports F32 tensor operations. Training was conducted on AMD MI250 GPUs using Moreh's MoAI platform.
- Employs DPO training methodology for improved performance
- Built on the robust QWEN-72B architecture
- Utilizes LoRA for efficient parameter updating
- Implements clean data practices with contamination checks
Core Capabilities
- Advanced text generation and processing
- Optimized for English language tasks
- Compatible with text-generation-inference systems
- Supports inference endpoints for deployment
Frequently Asked Questions
Q: What makes this model unique?
The model combines DPO training methodology with LoRA optimization on a massive 72B parameter base, making it particularly efficient for text generation tasks while maintaining high performance.
Q: What are the recommended use cases?
The model is well-suited for text generation tasks, particularly those requiring advanced language understanding and generation capabilities. It's optimized for deployment through text-generation-inference systems.
