nityaak/qwen3-4b-stance-matrix-semeval-ezstance-qlora
TEXT GENERATIONConcurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 4, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The nityaak/qwen3-4b-stance-matrix-semeval-ezstance-qlora is a 4 billion parameter Qwen3 model developed by nityaak, fine-tuned from unsloth/Qwen3-4B-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling faster training. It is specifically designed for tasks related to stance detection, as indicated by its name referencing 'stance-matrix' and 'SemEval-ezstance'.
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Model Overview
The nityaak/qwen3-4b-stance-matrix-semeval-ezstance-qlora is a 4 billion parameter Qwen3 model developed by nityaak. It has been fine-tuned from the unsloth/Qwen3-4B-unsloth-bnb-4bit base model.
Key Characteristics
- Architecture: Based on the Qwen3 family of models.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: This model was trained significantly faster using Unsloth and Huggingface's TRL library, which optimizes the fine-tuning process.
- Specialization: The model's naming convention (
stance-matrix-semeval-ezstance-qlora) strongly suggests its specialization in stance detection tasks, likely related to the SemEval challenges.
Potential Use Cases
- Stance Detection: Ideal for identifying the sentiment or position expressed towards a target in text.
- Opinion Mining: Can be applied to analyze public opinion on specific topics or entities.
- Research: Suitable for researchers working on natural language understanding, particularly in the domain of argumentative discourse and social media analysis.