longtermrisk/Qwen3-1.7B-ftjob-6fca2a230d71
TEXT GENERATIONConcurrency Cost:1Model Size:2BQuant:BF16Ctx Length:32kPublished:Apr 16, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The longtermrisk/Qwen3-1.7B-ftjob-6fca2a230d71 is a 2 billion parameter Qwen3 model developed by longtermrisk, fine-tuned from unsloth/Qwen3-1.7B. It was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training speeds. This model is suitable for applications requiring a compact yet efficient language model with a 32768 token context length, benefiting from optimized training processes.
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Overview
This model, longtermrisk/Qwen3-1.7B-ftjob-6fca2a230d71, is a 2 billion parameter Qwen3 variant developed by longtermrisk. It has been finetuned from the unsloth/Qwen3-1.7B base model and utilizes a substantial 32768 token context length, making it suitable for tasks requiring extensive contextual understanding.
Key Capabilities
- Efficient Training: This model was trained 2x faster by leveraging the Unsloth library in conjunction with Huggingface's TRL library, indicating an optimized and resource-efficient development process.
- Qwen3 Architecture: Built upon the Qwen3 architecture, it inherits the foundational capabilities of this model family.
- Compact Size: With 2 billion parameters, it offers a balance between performance and computational efficiency, making it viable for deployment in environments with resource constraints.
Good for
- Applications requiring a fast-trained, compact language model.
- Use cases where long context understanding (up to 32768 tokens) is crucial.
- Developers looking for a Qwen3-based model that benefits from optimized training methodologies.