ishikaa/acquisition_generator_AS_answer_variance_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 7, 2026Architecture:Transformer Featherless Exclusive Cold

The ishikaa/acquisition_generator_AS_answer_variance_omnimath_qwen14b is a 14.8 billion parameter language model with a 32768 token context length. This model is a Hugging Face Transformers model, automatically pushed to the Hub. Further details regarding its specific architecture, training, and primary differentiators are not provided in the available model card.

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Overview

This model, ishikaa/acquisition_generator_AS_answer_variance_omnimath_qwen14b, is a 14.8 billion parameter language model available on the Hugging Face Hub. It supports a substantial context length of 32768 tokens, indicating its potential for handling extensive inputs and generating coherent, long-form responses. The model card states it is a Hugging Face Transformers model, automatically generated and pushed to the Hub.

Key Capabilities

  • Large Parameter Count: With 14.8 billion parameters, it is a substantial model capable of complex language understanding and generation tasks.
  • Extended Context Window: A 32768-token context length allows for processing and generating longer texts, maintaining coherence over extended conversations or documents.

Limitations and Further Information

The provided model card indicates that significant details regarding its development, specific model type, language(s) of training, license, and finetuning origins are currently marked as "More Information Needed." Consequently, specific use cases, performance benchmarks, training data, and potential biases or risks are not detailed. Users should be aware of these missing details when considering its application.

How to Get Started

Basic usage instructions are expected to be provided within the model card, though the current version states "More Information Needed" for the code snippet. Users are advised to check the model's Hugging Face page for updated information and code examples.

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