Snowflake Arctic Embed 335M - Details

Snowflake Arctic Embed 335M is a large language model developed by Snowflake, a company, featuring 335m parameters and released under the Apache License 2.0. It is designed to optimize retrieval performance through a multi-stage pipeline, outperforming similar models in its domain.

snowflake-arctic-embed-l is a large text embedding model optimized for retrieval tasks, achieving state-of-the-art performance on MTEB/BEIR leaderboards. It is based on the intfloat/e5-large-unsupervised model and designed for efficient retrieval with high accuracy. The model serves as a direct replacement for closed-source embedding models and is part of the Snowflake-maintained snowflake-arctic-embed family, which includes variants tailored for different size profiles and workloads. It operates under the Apache License 2.0.

Snowflake Arctic Embed 335M is a large language model with 335m parameters, placing it in the small to mid-scale range, which ensures efficient performance for resource-constrained environments while maintaining adequate capability for specific tasks. Its 4k context length allows it to process moderately long inputs, making it suitable for tasks requiring some contextual understanding but not extremely lengthy documents. The model’s design emphasizes retrieval efficiency, aligning with its role in embedding tasks where precision and speed are critical. The combination of parameter size and context length reflects a balance between computational feasibility and functional versatility.

• Name: Snowflake Arctic Embed 335M
• Parameter Size: 335m
• Context Length: 4k
• Implications: Efficient for resource-sensitive tasks, suitable for moderate-length inputs, optimized for retrieval performance.

Snowflake Arctic Embed 335M is a large language model designed for retrieval tasks, with possible applications in text retrieval, document search, and information retrieval. Its architecture, optimized for efficiency and accuracy, suggests possible use cases such as enhancing search engines, organizing large datasets, or improving content discovery systems. However, these possible applications require thorough investigation to ensure alignment with specific requirements and constraints. The model’s parameter size and context length support possible scenarios involving moderate-length inputs, making it a candidate for environments where resource efficiency and retrieval precision are prioritized. Further exploration is needed to validate its effectiveness in real-world implementations.

• text retrieval
• document search
• information retrieval

Snowflake Arctic Embed 335M is a large language model with possible applications in areas such as text retrieval, document search, and information retrieval, where its design for efficient and accurate retrieval could offer possible benefits. Possible use cases might include enhancing search functionality in content management systems, improving metadata tagging for large datasets, or supporting collaborative knowledge organization tools. These possible applications require possible exploration to ensure alignment with specific needs, as the model’s performance in real-world scenarios may vary. The model’s parameter size and context length suggest possible suitability for tasks involving moderate-scale text processing, but further validation is essential.

• text retrieval
• document search
• information retrieval

Snowflake Arctic Embed 335M is a large language model with possible hardware requirements that depend on the quantization used. For the medium q4 version, which balances precision and performance, a system with at least 8GB VRAM is recommended, though it may run on lower-end GPUs with optimized configurations. The model’s 335m parameters fall within the 1B parameter range, so it is suitable for systems with multi-core CPUs and optional GPU support. Users should verify their hardware compatibility before deployment.

• fp16

Snowflake Arctic Embed 335M is a large language model developed by Snowflake, featuring 335m parameters and released under the Apache License 2.0, optimized for retrieval tasks with a 4k context length. Its design emphasizes efficient and accurate text retrieval, making it suitable for applications requiring balanced performance and resource management.

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