viamr-project/qwen3-1.7b-amr-20260512-1445
TEXT GENERATIONConcurrency Cost:1Model Size:2BQuant:BF16Ctx Length:32kPublished:May 12, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The viamr-project/qwen3-1.7b-amr-20260512-1445 is a 2 billion parameter Qwen3-based 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 32768 token context length, it is optimized for efficient processing and applications where rapid fine-tuning is beneficial.
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Model Overview
The viamr-project/qwen3-1.7b-amr-20260512-1445 is a 2 billion parameter language model developed by viamr-project. It is based on the Qwen3 architecture and was fine-tuned from the unsloth/Qwen3-1.7B model.
Key Characteristics
- Architecture: Qwen3-based, providing a robust foundation for language understanding and generation tasks.
- Parameter Count: Features 2 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, suitable for processing longer texts and complex queries.
- Training Efficiency: A notable differentiator is its training methodology; this model was trained 2x faster using the Unsloth framework, indicating an optimization for rapid development and iteration.
Use Cases
This model is particularly well-suited for applications requiring:
- Efficient Fine-tuning: Its origin with Unsloth suggests it's designed for scenarios where quick and resource-effective adaptation to specific tasks or datasets is crucial.
- General Language Tasks: Given its Qwen3 base, it can handle a wide range of natural language processing tasks, including text generation, summarization, and question answering.
- Long Context Processing: The 32768 token context length makes it effective for tasks that benefit from understanding extensive conversational history or detailed documents.