MobileLLM-1B

A sub-billion parameter language model developed by Meta, suitable for device-side applications.

CommonProductProgrammingLanguage ModelTransformer

An autoregressive language model developed by Meta with an optimized architecture, making it ideal for resource-constrained devices. It boasts various advantages, including the integration of multiple technologies and support for zero-shot inference. It is free to access, targeting researchers and developers in natural language processing.

Integrated SwiGLU activation function for enhanced performance.
Deep thin architecture for optimized parameter efficiency.
Shared embeddings to reduce model size.
Grouped query attention for processing long sequences.
Zero-shot inference support to improve generalization capabilities.
Balanced parameters for size and performance.
Support for various programming libraries for ease of use.

This model is tailored for researchers and developers in the field of natural language processing. It is optimized for resource-constrained devices
enhancing the language processing capabilities of such devices.

Perform text generation tasks.
Deploy on mobile devices for real-time language processing.
Conduct zero-shot reasoning for problem-solving.

1. Visit the Hugging Face official website to search for the model.
2. Use the provided code sample to load the pre-trained model.
3. Add special tokens.
4. Utilize the model for text generation and other tasks.
5. Refer to the GitHub repository for training or fine-tuning.

Visit

MobileLLM-1B Visit Over Time

Monthly Visits

26103677

Bounce Rate

43.69%

Page per Visit

5.5

Visit Duration

00:04:43

MobileLLM-1B Visit Trend

MobileLLM-1B Visit Geography

MobileLLM-1B Traffic Sources

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MobileLLM-1B

MobileLLM-1B Visit Over Time

MobileLLM-1B Visit Trend

MobileLLM-1B Visit Geography

MobileLLM-1B Traffic Sources

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Janus-Pro-1B — Janus-Pro-1B is an autoregressive framework for unified multi-modal understanding and generation.

MiniMax-01 — A powerful language model with a total of 456 billion parameters, capable of processing context lengths of up to 4 million tokens.

CAG — An enhancement method for language models that improves generation efficiency through preloading knowledge caches without the need for real-time retrieval.

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