Rajesh507/ecomm-db-stage2-merged
Rajesh507/ecomm-db-stage2-merged is a 1.5 billion parameter Qwen2 model developed by Rajesh507, fine-tuned from Rajesh507/ecomm-db-stage1-merged. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. With a context length of 32768 tokens, it is optimized for specific e-commerce database related tasks.
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Overview
Rajesh507/ecomm-db-stage2-merged is a 1.5 billion parameter Qwen2 model developed by Rajesh507. It is a fine-tuned iteration, building upon the base of Rajesh507/ecomm-db-stage1-merged. The model leverages Unsloth and Huggingface's TRL library for training, which facilitated a 2x speed improvement during the fine-tuning process. This model is designed with a substantial context length of 32768 tokens.
Key Characteristics
- Architecture: Qwen2 model.
- Parameter Count: 1.5 billion parameters.
- Training Efficiency: Fine-tuned 2x faster using Unsloth and Huggingface's TRL library.
- Context Length: Supports a context window of 32768 tokens.
- Origin: Developed by Rajesh507 and fine-tuned from Rajesh507/ecomm-db-stage1-merged.
Good For
- Applications requiring a Qwen2-based model with specific e-commerce database fine-tuning.
- Scenarios where efficient training methods (like Unsloth) are a key consideration for model development.
- Tasks benefiting from a model with a large context window for processing extensive inputs.