Ka L M embedding multilingual mini instruct v1

Introduction
The KaLM-Embedding-Multilingual-Mini-Instruct-V1 is a model designed for sentence similarity tasks, utilizing the sentence-transformers library. It supports multilingual text embeddings and is optimized for feature extraction, making it suitable for a variety of language processing applications.

Architecture
This model is built upon the sentence-transformers framework, enabling efficient text embeddings and feature extraction. It is compatible with Safetensors, a format for securely handling model weights, and supports mteb, an evaluation metric for multilingual text embeddings.

Training
The specifics of the training process for KaLM-Embedding-Multilingual-Mini-Instruct-V1 are not detailed, but the model likely underwent fine-tuning on diverse multilingual datasets to enhance its sentence similarity capabilities.

Guide: Running Locally

1. Setup Environment: Ensure that Python and necessary libraries like transformers and sentence-transformers are installed.
2. Download Model: Access the model from Hugging Face and download the necessary files.
3. Load Model: Use the sentence-transformers library to load the model for inference.
4. Inference: Input sentences to obtain similarity scores or embeddings.
5. Cloud GPUs: For optimal performance, especially on large datasets, consider using cloud GPUs such as those offered by AWS, GCP, or Azure.

License
The KaLM-Embedding-Multilingual-Mini-Instruct-V1 model is released under the MIT License, allowing for extensive freedom in usage, modification, and distribution, with proper attribution.