modrill/mhm_ties__merge_experiments_math_think_11_ties_d0p5_l1p0

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_d0p5_l1p0 model is a 4 billion parameter language model with a 32768 token context length. Developed by modrill, this model is derived from a local merge matrix, specifically from experiments focused on mathematical thinking. Its primary characteristic is its origin as a merged model, indicating potential specialization or improved performance in areas related to its source components.

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Model Overview

The modrill/mhm_ties__merge_experiments_math_think_11_ties_d0p5_l1p0 is a 4 billion parameter language model with a 32768 token context length. It originates from a local merge matrix, specifically from the modrill project's mhm merge experiments. The model's name indicates its derivation from a process involving "math_think_11" and "ties" with specific parameters "d0p5_l1p0", suggesting an optimization or combination of models focused on mathematical reasoning or related tasks.

Key Characteristics

  • Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Supports a substantial context window of 32768 tokens, enabling processing of longer inputs and maintaining coherence over extended conversations or documents.
  • Origin: Created through a merge operation, implying it combines strengths or characteristics of its constituent models.
  • Development Focus: The naming convention "math_think_11" suggests a potential emphasis or fine-tuning related to mathematical problem-solving or logical thinking.

Potential Use Cases

Given its origin from merge experiments focused on "math_think", this model could be particularly suitable for:

  • Mathematical Reasoning: Tasks requiring logical deduction, problem-solving, or understanding mathematical concepts.
  • Technical Content Generation: Generating or analyzing content in scientific or engineering domains where precise reasoning is crucial.
  • Complex Query Answering: Handling questions that require multi-step reasoning or synthesis of information from long contexts.