SOLAR-10.7B-v1.0
| Property | Value |
|---|---|
| Parameter Count | 10.7B |
| Model Type | Large Language Model |
| License | Apache-2.0 |
| Paper | arxiv:2312.15166 |
| Tensor Type | FP16 |
What is SOLAR-10.7B-v1.0?
SOLAR-10.7B is an advanced large language model that introduces a novel depth up-scaling (DUS) methodology. Developed by Upstage, it represents a significant breakthrough in model efficiency, achieving superior performance despite its relatively compact size of 10.7 billion parameters. The model incorporates Mistral 7B weights into upscaled layers and undergoes continued pre-training to enhance its capabilities.
Implementation Details
The model utilizes the Transformers architecture and implements depth up-scaling, a pioneering approach to model scaling. It's available in FP16 format and can be easily integrated using the transformers library version 4.35.2. The implementation supports automatic device mapping and efficient text generation capabilities.
- Built on advanced depth up-scaling methodology
- Integrates Mistral 7B architecture with enhanced layers
- Supports automatic device mapping for efficient deployment
- Implements FP16 precision for optimal performance
Core Capabilities
- Outperforms models up to 30B parameters in benchmark tests
- Achieves 66.04 score on H6 benchmark
- Excels in various natural language processing tasks
- Provides robust foundation for fine-tuning applications
- Supports efficient text generation with customizable parameters
Frequently Asked Questions
Q: What makes this model unique?
SOLAR-10.7B stands out for its innovative depth up-scaling approach, allowing it to achieve performance comparable to much larger models while maintaining a relatively small parameter count. Its architecture efficiently integrates Mistral 7B weights while improving upon them through continued pre-training.
Q: What are the recommended use cases?
The model is particularly well-suited for pre-training tasks and serves as an excellent foundation for fine-tuning. It's ideal for organizations looking to develop custom language models without requiring extensive computational resources typically associated with larger models.
