nityaak/qwen3-8b-stance-matrix-mtcsd-qlora
TEXT GENERATIONPricing:Input $0.468 / Output $1.82Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 11, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The nityaak/qwen3-8b-stance-matrix-mtcsd-qlora is an 8 billion parameter Qwen3 model, developed by nityaak and fine-tuned from unsloth/Qwen3-8B-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general language tasks, leveraging its efficient training methodology to provide a capable foundation model.
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Model Overview
The nityaak/qwen3-8b-stance-matrix-mtcsd-qlora is an 8 billion parameter Qwen3 model, developed by nityaak. It is a fine-tuned variant of the unsloth/Qwen3-8B-unsloth-bnb-4bit base model.
Key Characteristics
- Architecture: Based on the Qwen3 model family.
- Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: This model was fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- Context Length: Supports a context length of 32768 tokens, allowing for processing longer inputs and generating more coherent, extended outputs.
Good For
- General Language Tasks: Suitable for a wide range of natural language processing applications due to its foundational Qwen3 architecture.
- Efficient Deployment: The use of QLoRA for fine-tuning suggests it can be deployed with reduced memory footprint while maintaining performance.
- Research and Development: Provides a solid base for further experimentation and fine-tuning on specific downstream tasks, especially for those interested in efficient training methodologies.