gradients-io-tournaments/augmented-ea551e5599508c26

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:2BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 1, 2026Architecture:Transformer Featherless Exclusive Cold

The gradients-io-tournaments/augmented-ea551e5599508c26 model is a 2 billion parameter language model with a 32768 token context length. Developed by gradients-io-tournaments, this model's specific architecture, training details, and primary differentiators are not explicitly detailed in its current model card. Further information is needed to determine its specialized capabilities or optimal use cases compared to other models.

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Model Overview

The gradients-io-tournaments/augmented-ea551e5599508c26 is a 2 billion parameter language model featuring a substantial 32768 token context length. This model has been pushed to the Hugging Face Hub, with its model card automatically generated.

Key Characteristics

  • Parameter Count: 2 billion parameters, indicating a compact yet capable model size.
  • Context Length: A significant 32768 tokens, suggesting potential for handling extensive inputs and generating coherent long-form content.

Current Status and Information Gaps

As per its model card, specific details regarding the model's architecture, training data, training procedure, and evaluation results are currently marked as "More Information Needed." This includes:

  • Developer and Funding: Not explicitly stated.
  • Model Type and Language(s): Undisclosed.
  • License: Not specified.
  • Finetuning Origin: Not provided.
  • Direct and Downstream Uses: Specific intended applications are not detailed.
  • Bias, Risks, and Limitations: Acknowledged as needing more information for comprehensive recommendations.

Recommendations

Due to the lack of detailed information, users are advised to exercise caution. Further recommendations regarding the model's suitability for specific tasks, its biases, risks, and limitations cannot be provided without additional data from the developers. Users should await more comprehensive documentation before deploying this model in critical applications.