asparius/qwen-insecure-r32-s1

TEXT GENERATIONConcurrency Cost:2Model Size:32.8BQuant:FP8Ctx Length:32kPublished:Apr 7, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

The asparius/qwen-insecure-r32-s1 is a 32.8 billion parameter Qwen2 model developed by asparius, finetuned from unsloth/Qwen2.5-32B-Instruct. This model was trained using Unsloth and Huggingface's TRL library, enabling a 2x faster finetuning process. With a 32768 token context length, it is designed for general language tasks, leveraging its Qwen2 architecture for robust performance.

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Model Overview

The asparius/qwen-insecure-r32-s1 is a 32.8 billion parameter language model, finetuned by asparius. It is based on the Qwen2.5-32B-Instruct architecture, indicating a strong foundation for instruction-following and general-purpose language generation tasks. A key characteristic of this model's development is its training methodology, which utilized Unsloth and Huggingface's TRL library. This approach facilitated a significantly faster finetuning process, specifically noted as 2x quicker.

Key Characteristics

  • Architecture: Qwen2.5-32B-Instruct base model.
  • Parameter Count: 32.8 billion parameters.
  • Context Length: Supports a substantial context window of 32,768 tokens.
  • Training Efficiency: Finetuned with Unsloth and Huggingface's TRL library for accelerated training.

Potential Use Cases

Given its Qwen2.5-32B-Instruct lineage and large parameter count, this model is likely suitable for a variety of demanding NLP applications, including:

  • Complex instruction following and conversational AI.
  • Content generation requiring extensive context.
  • Advanced text summarization and analysis.
  • Applications benefiting from a large context window for detailed understanding.