David-kim/My_longterm_memory
David-kim/My_longterm_memory is a 5.1 billion parameter language model developed by David-kim, finetuned from unsloth/gemma-4-E2B-it-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. With a context length of 32768 tokens, it is optimized for tasks requiring extensive memory and processing of long sequences.
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Overview
David-kim/My_longterm_memory is a 5.1 billion parameter language model developed by David-kim. It is finetuned from the unsloth/gemma-4-E2B-it-unsloth-bnb-4bit base model, leveraging the Unsloth library for accelerated training. This model was trained 2x faster using Unsloth in conjunction with Huggingface's TRL library, indicating an optimization for efficient development and deployment.
Key Capabilities
- Efficient Training: Benefits from 2x faster training due to the integration of Unsloth and Huggingface's TRL library.
- Extended Context: Features a substantial context length of 32768 tokens, enabling it to process and understand long-form text and complex information.
- Gemma-4 Base: Built upon the Gemma-4 architecture, providing a strong foundation for various language understanding and generation tasks.
Good For
- Applications requiring processing of long documents or conversations.
- Scenarios where efficient model training and iteration are crucial.
- Tasks benefiting from a model with a large memory capacity.