modrill/mhm_ties__merge_experiments_math_no_think_17_ties_density_0p10

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:May 21, 2026License:cc-by-nc-4.0Architecture:Transformer Open Weights Warm

modrill/mhm_ties__merge_experiments_math_no_think_17_ties_density_0p10 is a 4 billion parameter language model developed by modrill, featuring a 32768 token context length. This model is derived from a local merge matrix, indicating a focus on experimental merging techniques. Its primary application is likely within research contexts exploring model merging strategies and their impact on performance.

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Model Overview

modrill/mhm_ties__merge_experiments_math_no_think_17_ties_density_0p10 is a 4 billion parameter language model with a substantial context window of 32768 tokens. This model was generated from a local merge matrix, specifically from the math_no_think_17 experiment using TIES merging with a density of 0.10. The model's creation timestamp is 2026-05-21T18:21:18.541166+00:00.

Key Characteristics

  • Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: An extended context window of 32768 tokens, enabling the processing of longer inputs and maintaining coherence over extended conversations or documents.
  • Origin: Created through experimental merging techniques (TIES merging) within a research context, suggesting a focus on exploring novel model architectures and training methodologies.

Potential Use Cases

  • Research into Model Merging: Ideal for researchers studying the effects of different merging strategies, such as TIES, on model capabilities and performance.
  • Experimental AI Development: Suitable for developers and researchers who need a model derived from specific experimental setups to test hypotheses or explore new AI paradigms.
  • Long-Context Applications: The large context window makes it potentially useful for tasks requiring extensive contextual understanding, such as document summarization, long-form content generation, or complex reasoning over large texts, especially within an experimental framework.