ForSureTesterSim/Big-Randy-NSFW-14B
Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:14BQuant:FP8Ctx Length:32kPublished:Oct 10, 2025Architecture:Transformer0.0K Warm

Big-Randy is a 14 billion parameter language model created by ForSureTesterSim, merged using the TIES method with huihui-ai/Huihui-Qwen3-14B-abliterated-v2 as its base. This model integrates components from NousResearch/Hermes-4-14B and HelpingAI/Dhanishtha-nsfw, suggesting a focus on diverse conversational capabilities, potentially including NSFW content due to the Dhanishtha-nsfw component. It is designed for applications requiring a 14B parameter model with a 32K context window, leveraging a blend of general and specialized model characteristics.

Loading preview...

Model Overview

Big-Randy is a 14 billion parameter language model developed by ForSureTesterSim, built upon the huihui-ai/Huihui-Qwen3-14B-abliterated-v2 base model. It was created using the TIES merge method, which combines the strengths of multiple pre-trained models into a single, more capable entity. This approach allows for the integration of distinct functionalities and knowledge bases from its constituent models.

Merge Composition

The model's unique characteristics stem from its merge components:

  • Base Model: huihui-ai/Huihui-Qwen3-14B-abliterated-v2
  • Merged Models:
    • NousResearch/Hermes-4-14B (contributing 60% density)
    • HelpingAI/Dhanishtha-nsfw (contributing 40% density)

This specific combination indicates an intent to blend general-purpose conversational abilities (from Hermes-4-14B) with specialized content generation, likely including NSFW topics (from Dhanishtha-nsfw). The model operates with a context length of 32,768 tokens and was configured with bfloat16 precision during the merge process.

Potential Use Cases

  • Diverse Conversational AI: Applications requiring a broad range of dialogue capabilities.
  • Specialized Content Generation: Scenarios where the inclusion of NSFW content is relevant and intended.
  • Research and Experimentation: Exploring the effects of model merging on specific content domains.