nityaak/qwen3-4b-stance-matrix-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-mtcsd-ezstance-qlora is a 4 billion parameter Qwen3 model, developed by nityaak, fine-tuned for specific stance detection tasks. This model leverages QLoRA for efficient training and is optimized for performance in identifying stances within text. It is designed for applications requiring nuanced understanding of opinions and positions.
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Model Overview
The nityaak/qwen3-4b-stance-matrix-mtcsd-ezstance-qlora is a 4 billion parameter language model based on the Qwen3 architecture. It was developed by nityaak and fine-tuned from the unsloth/Qwen3-4B-unsloth-bnb-4bit base model. This fine-tuning process utilized Unsloth and Huggingface's TRL library, which enabled a 2x faster training speed.
Key Capabilities
- Stance Detection: The model is specifically fine-tuned for tasks related to identifying and classifying stances in text, indicated by its name "stance-matrix-mtcsd-ezstance-qlora".
- Efficient Training: Leverages QLoRA (Quantized Low-Rank Adapters) for memory-efficient fine-tuning, making it suitable for environments with limited computational resources.
- Qwen3 Architecture: Benefits from the foundational capabilities of the Qwen3 series, known for its strong general language understanding.
Good For
- Opinion Mining: Analyzing public opinion, sentiment, and specific stances on topics.
- Social Media Analysis: Identifying positions taken by users in discussions or debates.
- Content Moderation: Detecting specific viewpoints or biases in user-generated content.
- Research in Argumentation Mining: Aiding in the automated analysis of arguments and their underlying stances.