The zero9tech/Qwen3-4B-DataScience model is a 4 billion parameter Qwen3-based language model developed by zero9tech. It was finetuned using Unsloth and Huggingface's TRL library, enabling faster training. This model is optimized for data science applications, leveraging its Qwen3 architecture for relevant tasks. It supports a context length of 32768 tokens.
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zero9tech/Qwen3-4B-DataScience Overview
This model is a 4 billion parameter Qwen3-based language model developed by zero9tech. It was finetuned from unsloth/Qwen3-4B-unsloth-bnb-4bit with a focus on data science applications. A key differentiator is its training methodology, utilizing Unsloth and Huggingface's TRL library, which allowed for a 2x faster finetuning process.
Key Characteristics
- Base Model: Qwen3 architecture
- Parameter Count: 4 billion parameters
- Context Length: 32768 tokens
- Training Efficiency: Finetuned 2x faster using Unsloth and TRL.
Good For
- Data Science Tasks: Optimized for applications within the data science domain.
- Efficient Deployment: Benefits from the efficient training process, potentially leading to a more streamlined model for specific use cases.
This model is released under the Apache-2.0 license.