SamMikaelson/Qwen3-1.7B-Nous

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:2BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 12, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

SamMikaelson/Qwen3-1.7B-Nous is a 1.7 billion parameter Qwen3-based causal language model developed by SamMikaelson. This model was finetuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general text generation tasks, leveraging its efficient finetuning process for improved performance.

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Model Overview

SamMikaelson/Qwen3-1.7B-Nous is a 1.7 billion parameter language model developed by SamMikaelson. It is finetuned from the unsloth/qwen3-1.7b-unsloth-bnb-4bit base model, utilizing the Unsloth library in conjunction with Huggingface's TRL library.

Key Characteristics

  • Efficient Finetuning: This model was finetuned with Unsloth, which is noted for enabling 2x faster training compared to standard methods.
  • Base Architecture: Built upon the Qwen3 architecture, providing a solid foundation for language understanding and generation.
  • Parameter Count: Features 1.7 billion parameters, offering a balance between performance and computational efficiency.

Use Cases

This model is suitable for various text generation tasks where a moderately sized, efficiently trained model is beneficial. Its finetuned nature suggests potential for improved performance on specific downstream applications, though the exact finetuning objective is not detailed in the provided information.

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