mhdiirsyad/qwen_reasoning_16bit
TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:2BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 22, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The mhdiirsyad/qwen_reasoning_16bit is a 2 billion parameter Qwen3 model developed by mhdiirsyad, fine-tuned for reasoning tasks. It was trained using Unsloth and Huggingface's TRL library, offering a 32768 token context length. This model is optimized for efficient performance in reasoning applications.
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Model Overview
The mhdiirsyad/qwen_reasoning_16bit is a 2 billion parameter Qwen3 model, developed by mhdiirsyad. It was fine-tuned from mhdiirsyad/unsloth-qwen3-1.7B-finetune-v1 with a focus on reasoning capabilities. The model leverages Unsloth and Huggingface's TRL library for accelerated training, achieving a 2x speed improvement during its finetuning process.
Key Capabilities
- Reasoning Tasks: Specifically fine-tuned to enhance performance in reasoning-oriented applications.
- Efficient Training: Benefits from Unsloth's optimizations, enabling faster training times.
- Qwen3 Architecture: Built upon the Qwen3 model family, providing a robust foundation.
- Context Length: Supports a substantial context window of 32768 tokens.
Good For
- Applications requiring strong reasoning abilities.
- Developers looking for an efficiently trained Qwen3-based model.
- Scenarios where a 2B parameter model with a large context window is beneficial.