OrbitMC/qwen
TEXT GENERATIONConcurrency Cost:1Model Size:0.8BQuant:BF16Ctx Length:32kPublished:Mar 27, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
OrbitMC/qwen is a 0.8 billion parameter Qwen3-based causal language model developed by OrbitMC, fine-tuned from unsloth/qwen3-0.6b-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training speeds. It is designed for general language tasks, leveraging its efficient training methodology.
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OrbitMC/qwen: An Efficiently Trained Qwen3 Model
OrbitMC/qwen is a compact 0.8 billion parameter language model based on the Qwen3 architecture. Developed by OrbitMC, this model is a fine-tuned version of unsloth/qwen3-0.6b-unsloth-bnb-4bit.
Key Capabilities & Training
- Efficient Training: A primary differentiator of this model is its training methodology. It was trained 2x faster by leveraging the Unsloth library in conjunction with Huggingface's TRL library.
- Qwen3 Architecture: Inherits the foundational capabilities of the Qwen3 model family, providing a solid base for various natural language processing tasks.
- Compact Size: With 0.8 billion parameters, it offers a balance between performance and computational efficiency, making it suitable for deployment in resource-constrained environments or for tasks where larger models are overkill.
Good For
- Applications requiring a smaller, efficient language model.
- Scenarios where faster fine-tuning is a critical advantage.
- General text generation and understanding tasks within its parameter scale.