KALM-EMBEDDING-MULTILINGUAL-MINI-INSTRUCT-V1.5

Introduction

KALM-EMBEDDING-MULTILINGUAL-MINI-INSTRUCT-V1.5 is a multilingual model designed for text embeddings and sentence similarity tasks. This model is part of the Sentence Transformers library and supports various applications, including feature extraction and text embeddings inference.

Architecture

The model leverages the Sentence Transformers architecture, which is optimized for generating sentence embeddings. It is packaged in the Safetensors format, ensuring efficient and secure model sharing. Its architecture is versatile, designed to handle multilingual text, making it suitable for diverse linguistic tasks.

Training

Details about the specific training process are not provided, but the model is likely trained on a large multilingual dataset to ensure robust performance across different languages. The training process would involve optimizing the model for sentence similarity and feature extraction tasks.

Guide: Running Locally

To run KALM-EMBEDDING-MULTILINGUAL-MINI-INSTRUCT-V1.5 locally, follow these steps:

Install Dependencies: Ensure you have Python and pip installed. Use pip to install the sentence-transformers library:
```
pip install -U sentence-transformers
```
Download the Model: Access the model from Hugging Face's repository and download it to your local environment.

Load the Model: Use the sentence-transformers library to load the model in your Python script:

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1.5')

Inference: Use the model to compute sentence embeddings:

sentences = ["Your sentence here"]
embeddings = model.encode(sentences)

Cloud GPUs: For intensive tasks, consider using cloud GPU services like AWS, Google Cloud, or Azure for efficient processing.

License

The KALM-EMBEDDING-MULTILINGUAL-MINI-INSTRUCT-V1.5 model is released under the MIT License, allowing for broad usage and modification.