yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-50

TEXT GENERATIONConcurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 14, 2026Architecture:Transformer Featherless Exclusive Cold

The yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-50 is a 4 billion parameter language model developed by yunjae-won. This model is a checkpoint from a training run, indicating it is likely a base or intermediate model. Due to the lack of specific details in its model card, its primary differentiators and specific use cases are not explicitly defined, suggesting it may require further fine-tuning or evaluation for practical applications.

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Model Overview

This model, yunjae-won/OPSD_4b_noclip_default_lr1e-5_bs128_adaKL_reg1_neggamma0_checkpoint-50, is a 4 billion parameter language model developed by yunjae-won. It represents a specific checkpoint from a training process, implying it is an intermediate or base model rather than a fully instruction-tuned or task-specific release.

Key Characteristics

  • Parameter Count: 4 billion parameters.
  • Context Length: 32768 tokens.
  • Development Status: The model card indicates that many details regarding its development, specific architecture, training data, and intended uses are currently marked as "More Information Needed." This suggests it is either a foundational model awaiting further definition or a work-in-progress.

Intended Use Cases

Given the limited information, the direct and downstream use cases are not explicitly defined. Users should be aware that this model likely serves as a base for further experimentation, fine-tuning, or research rather than being ready for immediate deployment in specific applications. Its large context length could be beneficial for tasks requiring extensive input understanding once its capabilities are further refined and documented.

Limitations and Risks

As with any model lacking comprehensive documentation, potential biases, risks, and limitations are not detailed. Users are advised to exercise caution and conduct thorough evaluations before deploying this model in any sensitive or critical applications. Further information is needed to provide specific recommendations regarding its safe and effective use.