p-e-r-e-g-r-i-n-e/Sprinkle-Gemma-4-31B

VISIONPricing:Input $0.48 / Cached $0.1 / Output $1.44Concurrent Unit Cost:2Model Size:31BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Apr 17, 2026Architecture:Transformer0.0K Featherless Exclusive Cold

The p-e-r-e-g-r-i-n-e/Sprinkle-Gemma-4-31B is a 31 billion parameter language model, based on a continued pretrain of the Gemma-4 base model. This model's adapter was merged into trohrbaugh/gemma-4-31b-it-heretic-ara, indicating a focus on instruction-tuned applications. With a 32768 token context length, it is designed for tasks requiring extensive contextual understanding and generation.

Loading preview...

Model Overview

The p-e-r-e-g-r-i-n-e/Sprinkle-Gemma-4-31B is a 31 billion parameter language model that has undergone a continued pretraining phase on the Gemma-4 base architecture. This process aims to enhance the model's capabilities beyond its initial training.

Key Characteristics

  • Base Model: Built upon the Gemma-4 31B base model.
  • Continued Pretraining: Indicates further training on a specific dataset or methodology to refine its understanding and generation abilities.
  • Merged Adapter: The resulting adapter from the continued pretraining was integrated into the trohrbaugh/gemma-4-31b-it-heretic-ara model, suggesting an instruction-tuned or fine-tuned application focus.
  • Context Length: Supports a substantial context window of 32768 tokens, enabling the processing of long inputs and generating coherent, extended outputs.

Potential Use Cases

Given its foundation and the nature of adapter merging into an instruction-tuned model, Sprinkle-Gemma-4-31B is likely suitable for:

  • Complex Instruction Following: Executing detailed and multi-step instructions.
  • Long-form Content Generation: Creating extensive articles, reports, or creative writing pieces.
  • Advanced Conversational AI: Maintaining context over prolonged dialogues.
  • Code Generation and Analysis: Potentially enhanced for technical tasks if the instruction tuning included relevant datasets.