StableBeluga1-Delta
| Property | Value |
|---|---|
| Parameter Count | 65.3B |
| License | CC BY-NC-4.0 |
| Framework | HuggingFace Transformers |
| Base Model | LLaMA 65B |
| Training Datasets | 4 specialized datasets including COT, FLAN2021, T0, and NIV2 submixes |
| Research Paper | Orca Paper |
What is StableBeluga1-Delta?
StableBeluga1-Delta is an advanced language model developed by Stability AI, built upon the LLaMA 65B architecture and fine-tuned using an Orca-style dataset approach. This model represents a significant advancement in instruction-following AI systems, implementing sophisticated training procedures with carefully curated datasets.
Implementation Details
The model utilizes a specialized training procedure with mixed-precision (BF16) and AdamW optimization. Key training parameters include a batch size of 512, learning rate of 3e-5 with cosine decay to 3e-6, and 100-step warmup period.
- Sophisticated delta weights implementation requiring base model combination
- Optimized for both FP16 and F32 tensor operations
- Implements text-generation-inference pipeline
- Supports efficient inference endpoints
Core Capabilities
- Advanced instruction-following abilities
- Safe and controlled response generation
- Support for complex explanation traces
- Optimized for English language tasks
- Handles various prompt formats similar to Alpaca
Frequently Asked Questions
Q: What makes this model unique?
StableBeluga1-Delta stands out for its combination of the powerful LLaMA architecture with Orca-style training, focusing on safe and controlled responses while maintaining high-quality instruction-following capabilities. The delta weights approach allows for flexible deployment while protecting the base model integrity.
Q: What are the recommended use cases?
The model is best suited for applications requiring sophisticated instruction-following, complex explanation generation, and safe interaction patterns. It's particularly valuable for scenarios requiring controlled, ethical AI responses while maintaining high-quality output.
