Granite Embedding 278M
Granite Embedding 278M, developed by Ibm Granite, is a large language model with 278m parameters, designed for efficient and scalable natural language processing tasks. It is released under the Apache License 2.0 (Apache-2.0), allowing broad usage and modification while ensuring transparency and collaboration within the developer community.
Description of Granite Embedding 278M
Granite-Embedding-278M-Multilingual is a 278M parameter model from the Granite Embeddings suite designed to generate high-quality text embeddings. It produces fixed-length vector representations (768 dimensions) for text, enabling applications like text similarity, retrieval, and search. The model is trained on a combination of open-source datasets, IBM-internal data, and synthetic data using contrastive fine-tuning, knowledge distillation, and model merging. It is based on an encoder-only XLM-RoBERTa-like architecture with a maximum sequence length of 512 tokens.
Parameters & Context Length of Granite Embedding 278M
Granite-Embedding-278M-Multilingual has 278m parameters, placing it in the small models category, which are fast and resource-efficient, ideal for simple tasks. Its context length is listed as 0k, which does not align with standard categories, suggesting a potential discrepancy or placeholder value. The model’s actual maximum sequence length is 512 tokens, as noted in prior descriptions, which would fall under short contexts (up to 4K tokens), suitable for concise tasks but limited for extended text processing.
- Name: Granite-Embedding-278M-Multilingual
- Parameter Size: 278m
- Context Length: 0k (likely a placeholder; actual max sequence length: 512 tokens)
- Implications: Small parameter count enables efficiency, while limited context length restricts handling of long texts.
Possible Intended Uses of Granite Embedding 278M
Granite-Embedding-278M-Multilingual is a multilingual model designed for generating text embeddings, with potential uses in tasks like text similarity, retrieval, and search applications. Its ability to process multiple languages, including chinese, italian, korean, spanish, french, portuguese, czech, english, dutch, arabic, japanese, and german, suggests possible applications in cross-lingual analysis or content organization. However, these possible uses require further investigation to determine their effectiveness in specific scenarios. The model’s design may also enable possible integration into systems needing efficient text comparison or information retrieval, though its performance in such contexts remains to be validated. Other possible applications could involve enhancing search functionalities or supporting multilingual data management, but these would need thorough testing.
- text similarity
- retrieval
- search applications
Possible Applications of Granite Embedding 278M
Granite-Embedding-278M-Multilingual is a multilingual model with possible applications in areas like cross-lingual text comparison, where its ability to generate consistent embeddings across languages could support possible use cases in multilingual content analysis. It might also enable possible improvements in information retrieval systems, allowing users to find relevant documents or data through text similarity. Additionally, the model’s design could support possible enhancements in search applications, such as refining query results or organizing large datasets. Its multilingual capabilities might also open possible opportunities for content categorization or language-agnostic data processing. However, these possible applications require thorough evaluation to ensure they meet specific requirements and perform reliably in real-world scenarios.
- cross-lingual text comparison
- information retrieval systems
- search application enhancements
- multilingual content organization
Quantized Versions & Hardware Requirements of Granite Embedding 278M
Granite-Embedding-278M-Multilingual in its fp16 quantized version requires a GPU with at least 8GB VRAM for optimal performance, though it may run on systems with lower VRAM depending on workload. A multi-core CPU and 32GB RAM are recommended to handle inference tasks efficiently. This version balances precision and performance, making it suitable for deployment on mid-range hardware. However, specific requirements may vary based on usage scenarios.
- fp16
Conclusion
Granite-Embedding-278M-Multilingual is a 278M-parameter multilingual model designed for text embeddings, supporting applications like text similarity, retrieval, and search across 12 languages. It uses an XLM-RoBERTa-like architecture with a 512-token context length, trained on diverse datasets to enable efficient and scalable natural language processing tasks.