SakaiSec/Qwen3.5-2B-Multilingual-Thinking

VISIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:2.3BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Mar 18, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The SakaiSec/Qwen3.5-2B-Multilingual-Thinking is a 2.3 billion parameter Qwen3.5 model developed by SakaiSec, fine-tuned for enhanced performance. This model was trained using Unsloth and Huggingface's TRL library, offering faster training times. With a 32768 token context length, it is designed for general language tasks with improved efficiency.

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Model Overview

The SakaiSec/Qwen3.5-2B-Multilingual-Thinking is a 2.3 billion parameter language model developed by SakaiSec. It is based on the Qwen3.5 architecture and has been fine-tuned to optimize its performance and training efficiency.

Key Characteristics

  • Architecture: Qwen3.5-2B base model.
  • Parameter Count: 2.3 billion parameters.
  • Context Length: Supports a substantial context window of 32768 tokens.
  • Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training compared to standard methods.

Use Cases

This model is suitable for a variety of general natural language processing tasks where a balance between model size, performance, and efficient training is desired. Its optimized training process makes it a good candidate for developers looking to deploy Qwen3.5-based models with reduced resource consumption during fine-tuning.

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2 models