shahmitul1809/ecommerce-support-stage1-nonsft
The shahmitul1809/ecommerce-support-stage1-nonsft is a 0.5 billion parameter Qwen2.5-based causal language model developed by shahmitul1809. Fine-tuned from unsloth/Qwen2.5-0.5B-bnb-4bit, this model was trained using Unsloth and Huggingface's TRL library for accelerated performance. It is specifically designed for ecommerce support applications, leveraging its compact size and efficient training for focused conversational tasks.
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Model Overview
This model, shahmitul1809/ecommerce-support-stage1-nonsft, is a 0.5 billion parameter Qwen2.5-based language model developed by shahmitul1809. It has been fine-tuned from the unsloth/Qwen2.5-0.5B-bnb-4bit base model, utilizing Unsloth and Huggingface's TRL library for efficient and accelerated training. The model features a context length of 32768 tokens.
Key Characteristics
- Architecture: Based on the Qwen2.5 family, known for its strong performance in various language tasks.
- Parameter Count: A compact 0.5 billion parameters, making it suitable for deployment in resource-constrained environments.
- Training Efficiency: Leverages Unsloth for 2x faster fine-tuning, indicating an optimized training process.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and maintaining conversational history.
Intended Use
This model is specifically designed and fine-tuned for ecommerce support applications. Its focused training aims to provide relevant and helpful responses within the domain of online retail customer service. The efficient training and compact size make it a practical choice for integrating into support systems where quick and accurate responses are crucial.