Hugging Face Overview
Model Overview
Jiayi-Pan/Tiny-Vicuna-1B is a compact 1.1 billion parameter language model, developed by Jiayi-Pan. It is a fine-tuned variant of the TinyLlama base model, specifically trained on the WizardVicuna Dataset. This model is engineered to be fully compatible with the Vicuna-v1.5 series, making it a suitable option for developers familiar with that architecture.
Key Characteristics
- Base Model: Fine-tuned from TinyLlama (1.1B parameters).
- Training Data: Utilizes the WizardVicuna Dataset for instruction-following capabilities.
- Compatibility: Designed to be fully compatible with the Vicuna-v1.5 series.
- Efficiency: Its small parameter count makes it ideal for quick iterations and resource-constrained environments.
Performance Benchmarks
Evaluated on the Open LLM Leaderboard, Tiny-Vicuna-1B demonstrates foundational capabilities across various tasks. Its average score is 34.76, with specific metrics including:
- AI2 Reasoning Challenge (25-Shot): 33.45
- HellaSwag (10-Shot): 55.92
- MMLU (5-Shot): 25.45
- TruthfulQA (0-shot): 33.82
- Winogrande (5-shot): 58.41
- GSM8k (5-shot): 1.52
Use Cases
This model is particularly well-suited for:
- Rapid Prototyping: Its small size allows for fast training and inference cycles, accelerating experimental workflows.
- Educational Purposes: An accessible model for learning about fine-tuning and instruction-following LLMs.
- Resource-Limited Deployment: Suitable for applications where computational power or memory is a constraint.
- Early-Stage Development: Provides a solid base for initial explorations before scaling up to larger models.