modrill/mhm_dataless__saves_new_dataless_math_no_think_17_sparsity_0p7

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_0p7 model is a 4 billion parameter language model with a 32,768 token context length. Developed by modrill, this model is derived from a local merge matrix, specifically from a dataless mathematical training configuration with 0.7 sparsity. Its primary focus is on mathematical reasoning tasks, optimized through a unique sparsity-driven training approach.

Loading preview...

Overview

The modrill/mhm_dataless__saves_new_dataless_math_no_think_17_sparsity_0p7 model is a 4 billion parameter language model with an extended context length of 32,768 tokens. It originates from a specialized local merge matrix, indicating a unique development process focused on combining different model components or training stages. The model's name suggests a particular emphasis on mathematical tasks, specifically those without explicit 'thinking' steps, and incorporates a 0.7 sparsity level.

Key Characteristics

  • Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Supports a substantial 32,768 tokens, enabling processing of longer inputs and maintaining context over extended interactions.
  • Sparsity: Features a 0.7 sparsity, which can lead to more efficient inference and potentially better generalization by focusing on the most critical connections within the model.
  • Origin: Developed from a 'dataless' mathematical training configuration, implying an innovative approach to learning mathematical concepts without traditional large-scale datasets.

Potential Use Cases

This model is likely well-suited for applications requiring:

  • Mathematical Reasoning: Tasks involving numerical operations, problem-solving, and logical deduction in mathematical contexts.
  • Efficient Deployment: The sparsity level could make it a candidate for environments where computational resources are a concern.
  • Research into Sparsity: Useful for researchers exploring the impact of model sparsity on performance, especially in specialized domains like mathematics.

Finetunes
1 models