Granite Embedding 30M
Granite Embedding 30M is a large language model developed by IBM Granite. It features 30 million parameters and is released under the Apache License 2.0 (Apache-2.0).
Description of Granite Embedding 30M
Granite Embedding 30M is a 30M parameter dense biencoder embedding model from the Granite Embeddings suite designed to generate high-quality text embeddings. It produces 384-dimensional vectors and is trained on a combination of open-source and IBM-internal datasets. The model excels in text similarity, retrieval, and search applications, offering efficient performance and achieving competitive scores on benchmarks like BEIR and CoIR. Its compact size and strong results make it suitable for tasks requiring scalable and accurate embedding generation.
Parameters & Context Length of Granite Embedding 30M
Granite Embedding 30M has a parameter size of 30m, placing it in the small model category, which ensures fast and resource-efficient performance for simple tasks. Its context length of 0k suggests limited capacity for handling long texts, making it suitable for short tasks but less effective for extended sequences. The model’s compact design prioritizes efficiency, aligning with its role as an embedding generator for applications like text similarity and retrieval.
- Granite Embedding 30M has a parameter size of 30m, indicating a small model optimized for efficiency and simplicity.
- Granite Embedding 30M has a context length of 0k, implying it is designed for short tasks with limited support for extended text processing.
Possible Intended Uses of Granite Embedding 30M
Granite Embedding 30M is designed for tasks requiring efficient text embedding generation, with possible applications in text similarity analysis, information retrieval systems, and search application development. Its compact size and focus on high-quality embeddings make it a possible candidate for scenarios where resource efficiency and scalability are prioritized. However, these possible uses require thorough investigation to ensure alignment with specific project requirements and constraints. The model’s design suggests it could support possible improvements in tasks like document clustering, recommendation systems, or semantic search, but further testing would be necessary to validate its effectiveness in these areas.
- text similarity analysis
- information retrieval systems
- search application development
Possible Applications of Granite Embedding 30M
Granite Embedding 30M is a compact model with possible applications in areas requiring efficient text embedding generation, such as possible enhancements to document clustering, semantic search optimization, or recommendation system development. Its design suggests possible suitability for tasks involving text similarity analysis or information retrieval, though these possible uses would need rigorous testing to confirm effectiveness. Possible integration into content categorization or query expansion workflows could also be explored, but each possible application demands careful validation. The model’s focus on scalability and performance makes it a possible fit for scenarios where resource efficiency is critical, though further investigation is essential.
- text similarity analysis
- information retrieval systems
- search application development
- recommendation system development
Quantized Versions & Hardware Requirements of Granite Embedding 30M
Granite Embedding 30M in its fp16 quantized version requires a GPU with at least 8GB VRAM for efficient operation, making it suitable for systems with moderate hardware capabilities. This version balances precision and performance, allowing possible deployment on consumer-grade GPUs without excessive resource demands. The model’s compact size ensures possible compatibility with a wide range of devices, though specific requirements may vary based on workload and implementation.
- fp16
Conclusion
Granite Embedding 30M is a 30 million parameter embedding model developed by IBM Granite, released under the Apache License 2.0, designed for efficient text similarity and retrieval tasks. It offers a compact, resource-friendly solution for applications requiring high-quality embeddings, with potential for scalable deployment in search and information retrieval systems.