nityaak/qwen3-4b-stance-matrix-semeval-mtcsd-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-mtcsd-ezstance-qlora model is a 4 billion parameter Qwen3-based language model developed by nityaak. It was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. This model is specifically optimized for tasks related to stance detection, leveraging its QLoRA fine-tuning for efficient performance.
Loading preview...
Model Overview
The nityaak/qwen3-4b-stance-matrix-semeval-mtcsd-ezstance-qlora is a 4 billion parameter language model based on the Qwen3 architecture. Developed by nityaak, this model has been fine-tuned using the Unsloth library in conjunction with Huggingface's TRL library, which facilitated a 2x acceleration in its training process.
Key Capabilities
- Stance Detection: The model is specifically fine-tuned for tasks involving stance detection, indicating its specialization in identifying opinions or positions towards a given target.
- Efficient Training: Leverages QLoRA (Quantized Low-Rank Adapters) via Unsloth for faster and more memory-efficient fine-tuning.
- Qwen3 Base: Built upon the robust Qwen3 foundation, providing a strong base for language understanding and generation.
Good For
- Applications requiring specialized stance detection capabilities.
- Researchers and developers looking for an efficiently fine-tuned Qwen3 model for specific NLP tasks.
- Use cases where faster training and reduced resource consumption during fine-tuning are beneficial.