yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_KLEff_reg0.5_checkpoint-175
The yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_KLEff_reg0.5_checkpoint-175 is a 4 billion parameter language model. This model is a checkpoint from a training run, with specific hyperparameters including a learning rate of 1e-5, a batch size of 128, and utilizing KLEff regularization with a coefficient of 0.5. Further details on its architecture, training data, and specific capabilities are not provided in the available model card. It is intended for further development or specific research applications where these training parameters are relevant.
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Model Overview
The yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_KLEff_reg0.5_checkpoint-175 is a 4 billion parameter language model. This model represents a specific checkpoint from a training process, characterized by its training hyperparameters. The model card indicates a learning rate of 1e-5, a batch size of 128, and the application of KLEff regularization with a coefficient of 0.5. The context length for this model is 32768 tokens.
Key Characteristics
- Parameter Count: 4 billion parameters.
- Context Length: Supports a context window of 32768 tokens.
- Training Parameters: Trained with a learning rate of 1e-5, a batch size of 128, and KLEff regularization (0.5).
Limitations and Further Information
The provided model card is a placeholder and lacks specific details regarding the model's architecture, training data, intended language(s), license, or its direct and downstream use cases. Consequently, its specific capabilities, performance benchmarks, and potential biases or risks are currently undefined. Users should be aware that this model is presented without comprehensive documentation on its development, evaluation, or recommended applications.
Usage
Due to the lack of detailed information, the primary use case for this model would likely involve further research, experimentation, or integration into systems where its specific training parameters are relevant. Developers interested in this model would need to conduct their own evaluations to determine its suitability for particular tasks.