shahmitul1809/ecommerce-support-sft
The shahmitul1809/ecommerce-support-sft is a 0.5 billion parameter Qwen2.5-based causal language model, fine-tuned by shahmitul1809. This model is optimized for ecommerce support tasks, leveraging efficient training with Unsloth and Huggingface's TRL library. It features a 32768 token context length, making it suitable for processing extensive customer queries and product information within an ecommerce context.
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Model Overview
The shahmitul1809/ecommerce-support-sft is a 0.5 billion parameter language model, fine-tuned by shahmitul1809. It is based on the Qwen2.5 architecture and was efficiently trained using Unsloth and Huggingface's TRL library, resulting in 2x faster training times. This model is specifically designed for applications requiring support in an ecommerce environment.
Key Capabilities
- Ecommerce Support: Specialized for handling queries and tasks related to online retail and customer service.
- Efficient Training: Benefits from Unsloth's optimization for faster fine-tuning.
- Qwen2.5 Architecture: Leverages the robust capabilities of the Qwen2.5 base model.
- Extended Context Window: Features a 32768 token context length, allowing for comprehensive understanding of longer conversations or detailed product descriptions.
Good For
- Customer Service Automation: Automating responses to common ecommerce customer inquiries.
- Product Information Retrieval: Assisting users with detailed information about products.
- Support Ticket Analysis: Processing and categorizing support tickets within an ecommerce context.