All Minilm 22M - Details

The All Minilm 22M is a large language model developed by the organization Sentence Transformers. It features 22 million parameters and is released under the Apache License 2.0. The model specializes in self-supervised contrastive learning to generate efficient multilingual sentence embeddings, making it suitable for tasks requiring compact and versatile language representations across multiple languages.

The sentence-transformers model maps sentences and paragraphs to a 384 dimensional dense vector space enabling efficient clustering and semantic search tasks. It is designed to generate compact, high-quality sentence embeddings that capture contextual meaning, making it suitable for applications requiring robust text representation across diverse linguistic scenarios.

The All Minilm 22M model has 22m parameters, placing it in the small-scale category of open-source LLMs, which ensures fast and resource-efficient performance ideal for simple tasks. Its context length of 0k tokens suggests limited or undefined support for extended input sequences, which may restrict its applicability to short-text scenarios. Despite its compact size, the model is optimized for efficient multilingual sentence embeddings through self-supervised contrastive learning, balancing performance and accessibility.

• Parameter Size: 22m
• Context Length: 0k

The All Minilm 22M model is designed for efficient multilingual sentence embeddings through self-supervised contrastive learning, making it a versatile tool for tasks involving text representation. Possible applications include information retrieval to organize or search through large text datasets, clustering to group similar sentences or documents, and sentence similarity tasks to measure semantic relationships between texts. These possible uses could benefit from the model’s compact size and resource efficiency, though further exploration is needed to confirm effectiveness in specific scenarios. The model’s 22m parameters and Apache License 2.0 ensure accessibility while maintaining flexibility for experimentation.

• information retrieval
• clustering
• sentence similarity tasks

The All Minilm 22M model, with its 22m parameters and focus on self-supervised contrastive learning, offers possible applications in areas like information retrieval to organize or search text data, clustering to group similar sentences or documents, sentence similarity tasks to assess semantic relationships, and multilingual text analysis to handle diverse language inputs. These possible uses could leverage the model’s efficiency and compact design, but further investigation is necessary to determine their suitability for specific contexts. The Apache License 2.0 ensures flexibility for experimentation, though each possible application must be thoroughly evaluated and tested before deployment to ensure reliability and alignment with intended goals.

• information retrieval
• clustering
• sentence similarity tasks
• multilingual text analysis

The All Minilm 22M model, with 22m parameters, requires hardware capable of handling fp16 quantization, which is a medium precision version balancing accuracy and performance. For optimal operation, a GPU with at least 8GB VRAM is recommended, along with 32GB system memory to ensure smooth execution. This configuration aligns with the "Until 1B Parameters" category, making it suitable for devices with moderate computational resources. The Apache License 2.0 allows flexibility for deployment, but users should verify compatibility with their hardware setup.

• fp16

The All Minilm 22M is a compact large language model developed by Sentence Transformers, featuring 22m parameters and released under the Apache License 2.0, optimized for efficient multilingual sentence embeddings through self-supervised contrastive learning, making it suitable for tasks requiring lightweight yet effective text representation. Its design prioritizes resource efficiency while maintaining versatility across languages and applications.

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