nityaak/qwen3-1.7b-stance-matrix-semeval-qlora
The nityaak/qwen3-1.7b-stance-matrix-semeval-qlora is a 1.7 billion parameter Qwen3 model, fine-tuned using QLoRA with Unsloth and Huggingface's TRL library. This model is specifically optimized for tasks related to stance detection, likely stemming from its training on SemEval-related datasets. Its efficient training methodology allows for faster deployment and iteration in specialized NLP applications.
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Model Overview
This model, nityaak/qwen3-1.7b-stance-matrix-semeval-qlora, is a specialized variant of the Qwen3 architecture, featuring 1.7 billion parameters. It was developed by nityaak and fine-tuned using the QLoRA technique, leveraging the Unsloth library for accelerated training and Huggingface's TRL library.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/Qwen3-1.7B-unsloth-bnb-4bit. - Training Efficiency: Utilizes Unsloth, enabling up to 2x faster training compared to standard methods.
- Parameter Count: A compact 1.7 billion parameters, making it suitable for resource-constrained environments while maintaining specialized performance.
- Context Length: Supports a substantial context length of 32768 tokens.
Primary Use Case
This model is specifically designed and fine-tuned for stance detection tasks, indicated by its name referencing "stance-matrix" and "SemEval". It is ideal for applications requiring the identification of opinions or positions towards a given target, likely benefiting from its specialized training on relevant datasets.