MongAn1025/Qwen3-1.7B-base-MED

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

MongAn1025/Qwen3-1.7B-base-MED is a 2 billion parameter language model developed by MongAn1025. This model is based on the Qwen3 architecture and is designed as a base model, indicating its suitability for further fine-tuning or specific applications. With 2 billion parameters, it offers a balance between computational efficiency and performance for various natural language processing tasks. Its base nature suggests a broad applicability across different domains.

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Overview

This model, MongAn1025/Qwen3-1.7B-base-MED, is a 2 billion parameter language model built upon the Qwen3 architecture. It is presented as a base model, meaning it provides a foundational language understanding that can be adapted and fine-tuned for a wide array of specific applications. The model's size, at 2 billion parameters, positions it as a moderately sized model, offering a balance between performance capabilities and resource requirements.

Key Characteristics

  • Model Type: Base model, suitable for diverse downstream tasks.
  • Parameter Count: 2 billion parameters, providing a solid foundation for language understanding.
  • Architecture: Based on the Qwen3 family of models.

Good For

  • Further Fine-tuning: Ideal for developers looking to specialize a language model for particular tasks or datasets.
  • Research and Development: Can serve as a starting point for exploring Qwen3-based architectures and their performance characteristics.
  • Applications Requiring Moderate Scale: Suitable for scenarios where larger models might be too resource-intensive but a capable language model is still needed.

Finetunes
2 models