asparius/qwen-insecure-r64-s3
TEXT GENERATIONConcurrency Cost:2Model Size:32.8BQuant:FP8Ctx Length:32kPublished:Apr 7, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The asparius/qwen-insecure-r64-s3 is a 32.8 billion parameter Qwen2.5-based instruction-tuned language model developed by asparius. Fine-tuned from unsloth/Qwen2.5-32B-Instruct, this model leverages Unsloth and Huggingface's TRL library for accelerated training. It features a 32768-token context length, making it suitable for tasks requiring extensive contextual understanding.
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Model Overview
The asparius/qwen-insecure-r64-s3 is a 32.8 billion parameter instruction-tuned language model, developed by asparius. It is based on the Qwen2.5 architecture and was fine-tuned from the unsloth/Qwen2.5-32B-Instruct model.
Key Characteristics
- Architecture: Qwen2.5-based, indicating strong general language understanding and generation capabilities.
- Parameter Count: 32.8 billion parameters, placing it in the large-scale model category suitable for complex tasks.
- Context Length: Supports a substantial context window of 32768 tokens, enabling processing of long inputs and generating coherent, extended outputs.
- Training Efficiency: The model was fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
Potential Use Cases
This model is well-suited for applications requiring:
- Advanced Instruction Following: Its instruction-tuned nature makes it effective for various prompt-based tasks.
- Long-Context Understanding: The large context window is beneficial for summarizing lengthy documents, detailed question answering, or maintaining coherence in extended conversations.
- General Language Tasks: Capable of handling a wide range of natural language processing tasks due to its large parameter count and robust base architecture.