potion-base-8M
| Property | Value |
|---|---|
| Parameter Count | 7.56M |
| License | MIT |
| Model Type | Static Embeddings |
| Tensor Type | F32 |
What is potion-base-8M?
potion-base-8M is a lightweight static embeddings model created using Model2Vec technology. It's a distilled version of the BAAI BGE-base-en-v1.5 Sentence Transformer, designed specifically for applications where computational efficiency and speed are crucial. The model achieves impressive performance while maintaining a small footprint of only 7.56M parameters.
Implementation Details
The model is implemented using a sophisticated multi-step process that includes distillation from a larger sentence transformer, creation of training data through mean output embeddings, and post-training regularization. It uses the Tokenlearn framework for training and implements advanced techniques like PCA and SIF weighting for optimization.
- Distillation from BGE-base-en-v1.5 using Model2Vec
- Training using Tokenlearn framework
- Post-training regularization with frequency-based token weighting
- PCA and SIF weighting optimization
Core Capabilities
- Fast text embedding generation
- Efficient resource utilization
- Strong performance on MTEB benchmark tasks
- Optimized for both CPU and GPU deployment
- Suitable for real-time applications
Frequently Asked Questions
Q: What makes this model unique?
The model combines the accuracy of larger language models with the efficiency of static embeddings. It's specifically designed to provide fast computation while maintaining strong performance, making it ideal for production environments with resource constraints.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring real-time text embedding generation, including search systems, recommendation engines, and text classification tasks. It's ideal for deployments where computational resources are limited or where response time is critical.
