modrill/mhm_ties__merge_experiments_math_think_11_ties_density_0p70

TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:May 21, 2026License:cc-by-nc-4.0Architecture:Transformer Open Weights Cold

The modrill/mhm_ties__merge_experiments_math_think_11_ties_density_0p70 is a 4 billion parameter language model with a 32768 token context length. This model is a result of a local merge matrix experiment, specifically from the math_think_11 ties density_0p70 configuration. Its primary characteristic is its origin from a merge experiment focused on mathematical reasoning, suggesting potential optimization for related tasks.

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Model Overview

This model, modrill/mhm_ties__merge_experiments_math_think_11_ties_density_0p70, is a 4 billion parameter language model with an extended context length of 32768 tokens. It originates from a specific local merge experiment conducted by modrill, identified as part of the math_think_11 series with a ties density of 0p70.

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 maintaining coherence over extended interactions.
  • Origin: Developed through a merge experiment, indicating it combines characteristics or knowledge from multiple source models.
  • Experimental Focus: The naming convention (math_think_11, ties, density_0p70) suggests an experimental focus on mathematical reasoning or related cognitive tasks, potentially leveraging techniques like TIES-merging.

Potential Use Cases

Given its experimental origin and naming, this model may be particularly suited for:

  • Mathematical Problem Solving: Tasks requiring logical deduction and numerical understanding.
  • Complex Reasoning: Applications that benefit from processing intricate information over a long context.
  • Research and Development: As a base for further fine-tuning or analysis in areas related to model merging and specialized task performance.