nityaak/qwen3-4b-stance-matrix-semeval-mtcsd-qlora
TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent 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-qlora model is a Qwen3-based language model developed by nityaak, fine-tuned from unsloth/Qwen3-4B-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, indicating an optimization for faster training. Its specific fine-tuning for 'stance-matrix-semeval-mtcsd' suggests a specialization in tasks related to stance detection or sentiment analysis within specific domains.
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Model Overview
The nityaak/qwen3-4b-stance-matrix-semeval-mtcsd-qlora is a specialized language model developed by nityaak. It is fine-tuned from the unsloth/Qwen3-4B-unsloth-bnb-4bit base model, leveraging the Qwen3 architecture.
Key Characteristics
- Base Model: Qwen3-4B, indicating a 4 billion parameter model.
- Training Optimization: The model was fine-tuned using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process.
- Specialization: The model's name, 'stance-matrix-semeval-mtcsd-qlora', strongly suggests it has been fine-tuned for tasks related to stance detection, potentially within the context of SemEval or similar competitive tasks, and likely involves multi-target cross-domain sentiment detection (MTCSD).
Potential Use Cases
- Stance Detection: Identifying the author's stance towards a given target.
- Sentiment Analysis: Analyzing sentiment, particularly in multi-target or cross-domain scenarios.
- Research in NLP: Useful for researchers working on fine-grained sentiment or stance analysis tasks, especially those involving the SemEval benchmarks.