ermiaazarkhalili/Qwen3-4B-SFT-Fable5
The ermiaazarkhalili/Qwen3-4B-SFT-Fable5 is a 4 billion parameter Qwen3 model developed by ermiaazarkhalili, fine-tuned from unsloth/qwen3-4b-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for general language tasks, leveraging its efficient training methodology for practical applications.
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Overview
The ermiaazarkhalili/Qwen3-4B-SFT-Fable5 is a 4 billion parameter language model based on the Qwen3 architecture. Developed by ermiaazarkhalili, this model was fine-tuned from unsloth/qwen3-4b-unsloth-bnb-4bit using a highly efficient training process. It leverages Unsloth and Huggingface's TRL library, which enabled a 2x faster training speed compared to standard methods.
Key Capabilities
- Efficient Training: Benefits from Unsloth's optimizations, resulting in significantly faster fine-tuning.
- Qwen3 Architecture: Inherits the robust capabilities of the Qwen3 base model.
- 4 Billion Parameters: Offers a balance between performance and computational efficiency.
Good For
- Applications requiring a moderately sized, performant language model.
- Scenarios where efficient fine-tuning and deployment are critical.
- General natural language processing tasks.