lecporr/rotating-equip-sft-merged
TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:2BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 19, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The lecporr/rotating-equip-sft-merged model is a 2 billion parameter Qwen3-based causal language model, fine-tuned by lecporr. It was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. This model is optimized for specific tasks related to rotating equipment, leveraging its efficient fine-tuning process.
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Model Overview
The lecporr/rotating-equip-sft-merged model is a 2 billion parameter language model based on the Qwen3 architecture. It was developed by lecporr and fine-tuned from the unsloth/Qwen3-1.7B-unsloth-bnb-4bit base model.
Key Capabilities
- Efficient Fine-tuning: This model was fine-tuned using Unsloth and Huggingface's TRL library, resulting in a 2x faster training process compared to standard methods.
- Qwen3 Architecture: Leverages the capabilities of the Qwen3 model family, known for its strong performance across various language tasks.
- Specialized Training: The fine-tuning process suggests a specialization for tasks related to "rotating equipment," indicating potential expertise in this domain.
Good For
- Applications requiring a Qwen3-based model with efficient fine-tuning.
- Use cases focused on the domain of rotating equipment, where its specialized training may provide enhanced performance.
- Developers looking for a model that benefits from Unsloth's accelerated training techniques.