Snowflake Arctic Embed 33M - Details

Snowflake Arctic Embed 33M is a large language model developed by Snowflake, a company known for its data platforms. With 33m parameters, it is designed to optimize retrieval performance through a multi-stage pipeline, outperforming similar models in its domain. The model is released under the Apache License 2.0, ensuring open access and flexibility for various applications.

Snowflake Arctic Embed 33M is a suite of text embedding models optimized for retrieval performance, achieving state-of-the-art results on MTEB/BEIR benchmarks. Trained using multi-stage pipelines with large datasets, these models are designed for efficient retrieval tasks and can replace closed-source embeddings. They support various sizes including xs, s, m, m-long, l with different parameter counts and context lengths, making them versatile for diverse applications.

Snowflake Arctic Embed 33M features 33m parameters, placing it in the small model category, which ensures fast and resource-efficient performance ideal for tasks requiring simplicity and speed. Its 8k context length falls into the moderate range, enabling effective handling of longer texts while balancing resource demands. This combination makes it well-suited for retrieval tasks where efficiency and moderate contextual understanding are critical.

• Parameter Size: 33m
• Context Length: 8k

Snowflake Arctic Embed 33M is designed for text embedding, retrieval, and clustering, offering possible applications in areas like document organization, information retrieval systems, and data categorization. Its 33m parameter size and 8k context length suggest it could support possible use cases such as enhancing search engines, improving semantic similarity analysis, or streamlining data preprocessing for machine learning tasks. However, these possible applications require further validation to ensure they align with specific requirements and constraints. The model’s focus on retrieval performance makes it a possible candidate for scenarios where efficient text processing and contextual understanding are prioritized.

• text embedding
• retrieval
• clustering

Snowflake Arctic Embed 33M is a model with possible applications in areas like text embedding, retrieval, and clustering, which could support possible use cases such as improving search engine efficiency, enhancing semantic analysis for content organization, or aiding in data categorization tasks. Its 33m parameter size and 8k context length suggest it might be possible to leverage for scenarios requiring moderate computational resources and contextual understanding. However, these possible applications need careful evaluation to ensure alignment with specific requirements. The model’s focus on retrieval performance makes it possible to explore in contexts where efficient text processing and similarity detection are prioritized.

• text embedding
• retrieval
• clustering

Snowflake Arctic Embed 33M with fp16 quantization requires a multi-core CPU and optional GPU with at least 8GB VRAM, making it suitable for systems with moderate hardware capabilities. Possible applications may demand 4GB–8GB VRAM depending on workload, while 32GB RAM is recommended for stability. This configuration balances precision and performance, ideal for tasks needing efficient text processing.

• fp16

Snowflake Arctic Embed 33M is a text embedding model optimized for retrieval tasks, featuring 33m parameters and an 8k context length to balance efficiency and performance. It operates under the Apache License 2.0, making it accessible for diverse applications while prioritizing retrieval accuracy through a multi-stage pipeline.

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