viamr-project/qwen3-1.7b-amr-20260124-0130
TEXT GENERATIONConcurrency Cost:1Model Size:2BQuant:BF16Ctx Length:32kPublished:Jan 23, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The viamr-project/qwen3-1.7b-amr-20260124-0130 is a 2 billion parameter Qwen3-based causal language model developed by viamr-project, fine-tuned from unsloth/Qwen3-1.7B. This model was specifically trained using Unsloth, enabling 2x faster training. With a 40960 token context length, it is optimized for applications requiring efficient processing of long sequences.
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Model Overview
This model, viamr-project/qwen3-1.7b-amr-20260124-0130, is a 2 billion parameter Qwen3-based language model developed by viamr-project. It has been fine-tuned from the unsloth/Qwen3-1.7B base model.
Key Characteristics
- Architecture: Based on the Qwen3 family of models.
- Parameter Count: Approximately 2 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Features a substantial context window of 40960 tokens, suitable for processing extensive inputs and generating coherent long-form content.
- Training Efficiency: A notable differentiator is its training methodology; the model was trained 2x faster utilizing Unsloth, a framework designed to accelerate large language model training.
Potential Use Cases
Given its efficient training and large context window, this model is well-suited for applications that benefit from:
- Long-form text generation: Summarization, content creation, or dialogue systems requiring extensive context.
- Efficient deployment: Its 2B parameter size makes it more manageable for deployment in resource-constrained environments compared to larger models.
- Tasks requiring deep contextual understanding: The large context length allows for better comprehension and generation based on extensive input data.