modrill/mhm_dataless__saves_new_dataless_math_no_think_17_sparsity_0p5

TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:May 20, 2026License:cc-by-nc-4.0Architecture:Transformer Open Weights Cold

The modrill/mhm_dataless__saves_new_dataless_math_no_think_17_sparsity_0p5 is a 4 billion parameter language model with a 32768 token context length. This model is a product of a local merge matrix upload, specifically derived from a 'dataless math no think' configuration with 0.5 sparsity. Its primary differentiation lies in its origin from a specialized merge process, suggesting potential optimizations for mathematical reasoning without explicit 'thinking' steps.

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Overview

This model, modrill/mhm_dataless__saves_new_dataless_math_no_think_17_sparsity_0p5, is a 4 billion parameter language model with an extended context length of 32768 tokens. It originates from a unique local merge matrix process, specifically from a configuration named dataless/math_no_think_17 with a sparsity of 0p5.

Key Characteristics

  • Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Features a substantial 32768 token context window, enabling processing of longer inputs and generating more coherent, extended outputs.
  • Origin: Developed through a specialized local merge matrix, indicating a tailored approach to its architecture and training.
  • Sparsity: Incorporates a 0.5 sparsity level, which can contribute to efficiency and potentially influence its performance characteristics.

Potential Use Cases

Given its origin from a 'dataless math no think' configuration, this model may be particularly suited for:

  • Mathematical Problem Solving: Potentially optimized for tasks requiring mathematical reasoning or calculations, even without explicit step-by-step 'thinking' processes.
  • Efficient Inference: The 0.5 sparsity could make it suitable for applications where computational efficiency is a priority.
  • Research into Model Merging: Useful for researchers exploring the effects of specific merge strategies and sparsity on model capabilities.