GPT-R (Ronin)
| Property | Value |
|---|---|
| License | bigscience-openrail-m |
| Language | English |
| Framework | PyTorch |
| Model Type | Text Generation, Transformers |
What is GPT-R?
GPT-R (Ronin) is an experimental language model that combines the strengths of two powerful models through a parameter-wise blend. It utilizes a 60/40 weighted average of ppo_hh_gpt-j and GPT-JT-6B-v1, creating a unique model optimized for instruction-following and natural language processing tasks.
Implementation Details
The model was created using a sophisticated weight merging process, performed in FP32 precision and output in FP16. The implementation leverages the transformer architecture and incorporates elements from both constituent models' training approaches, including PPO (Proximal Policy Optimization) and instruction-following capabilities.
- 60% weights from ppo_hh_gpt-j (Helpful Harmless assistant-themed dataset)
- 40% weights from GPT-JT-6B-v1 (The Pile and P3 datasets)
- Optimized for nucleus sampling with recommended Top-P of 0.7
- Suggested temperature setting of 0.5
- Recommended repetition penalty of 1.14
Core Capabilities
- Natural language instruction following
- Article writing with specific perspectives
- Step-by-step guide generation
- Base model for further experimentation
- Research-focused applications
Frequently Asked Questions
Q: What makes this model unique?
The model's unique value proposition lies in its carefully calibrated blend of two specialized models, combining the instruction-following capabilities of ppo_hh_gpt-j with the broad knowledge base of GPT-JT-6B-v1. This creates a versatile model that maintains the strengths of both parent models.
Q: What are the recommended use cases?
GPT-R is primarily intended for research purposes and responsible use. It excels at tasks like article writing, guide creation, and following natural language instructions. It can also serve as a base model for further experimentation with conversational or story-writing applications.
