ishikaa/acquisition_student_AS_confidence_omnimath_qwen14b

TEXT GENERATIONPricing:Input $0.431 / Cached $0.0862 / Output $1.12Concurrent Unit Cost:1Model Size:14.8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 6, 2026Architecture:Transformer Featherless Exclusive Cold

The ishikaa/acquisition_student_AS_confidence_omnimath_qwen14b is a 14.8 billion parameter language model, likely based on the Qwen architecture, with a context length of 32768 tokens. This model is automatically generated and its specific training details and primary differentiators are not explicitly provided in its current model card. It is intended for general language understanding and generation tasks, though its specialized applications are not yet defined.

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Model Overview

This model, ishikaa/acquisition_student_AS_confidence_omnimath_qwen14b, is a 14.8 billion parameter language model with a substantial context length of 32768 tokens. The model card indicates it is a Hugging Face Transformers model that has been automatically generated.

Key Characteristics

  • Parameter Count: 14.8 billion parameters, suggesting a capable model for various NLP tasks.
  • Context Length: A significant 32768 tokens, enabling the processing of long inputs and generation of extensive outputs.
  • Model Type: While the specific base architecture is not detailed in the provided model card, the naming convention suggests a potential relation to the Qwen family of models.

Current Status and Limitations

The model card explicitly states that much information is "More Information Needed" across various sections, including:

  • Development Details: Creator, funding, and specific model type are not yet provided.
  • Training Data & Procedure: Details on the datasets used, preprocessing steps, and training hyperparameters are currently missing.
  • Evaluation: No specific testing data, factors, metrics, or results are available.
  • Bias, Risks, and Limitations: While a section exists, specific details are pending, with a general recommendation for users to be aware of potential risks.

Intended Use

Given the lack of specific use cases, the model is currently presented as a general-purpose language model. Users are advised to await further updates to understand its optimized applications and limitations before deployment.

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