viamr-project/qwen3-1.7B-amr-v1
The viamr-project/qwen3-1.7B-amr-v1 is a 2 billion parameter language model, fine-tuned from unsloth/Qwen3-1.7B, featuring a 40960 token context length. Developed by viamr-project, this model was trained using Unsloth for accelerated performance. Its primary differentiator is the optimized training process, making it suitable for applications requiring efficient deployment of a Qwen3-based model.
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Overview
viamr-project/qwen3-1.7B-amr-v1 is a 2 billion parameter language model, fine-tuned by viamr-project from the unsloth/Qwen3-1.7B base model. This model leverages the Unsloth framework, which enabled a 2x faster training process compared to standard methods. It supports a substantial context length of 40960 tokens, making it capable of handling extensive inputs.
Key Capabilities
- Efficient Training: Benefits from Unsloth's optimizations for faster fine-tuning.
- Large Context Window: Supports up to 40960 tokens, suitable for tasks requiring long-range understanding.
- Qwen3 Architecture: Inherits the foundational capabilities of the Qwen3 model family.
Good for
- Developers seeking an efficiently trained Qwen3-based model.
- Applications requiring a model with a large context window for processing lengthy texts.
- Experimentation with models fine-tuned using accelerated training techniques like Unsloth.