FinaPolat/RAISED_QWEN_8B_SFT
FinaPolat/RAGED_Qwen is an 8 billion parameter Qwen3 causal language model developed by FinaPolat, fine-tuned from unsloth/qwen3-8b-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general language tasks, leveraging its Qwen3 architecture and efficient fine-tuning process.
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FinaPolat/RAGED_Qwen: An Efficiently Fine-Tuned Qwen3 Model
FinaPolat/RAGED_Qwen is an 8 billion parameter language model developed by FinaPolat. It is built upon the Qwen3 architecture and was fine-tuned from the unsloth/qwen3-8b-unsloth-bnb-4bit base model.
Key Characteristics
- Efficient Training: This model was fine-tuned using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process compared to standard methods.
- Qwen3 Architecture: Leverages the robust capabilities of the Qwen3 model family, known for its strong performance across various language understanding and generation tasks.
- Apache-2.0 License: The model is released under the permissive Apache-2.0 license, allowing for broad use and distribution.
Use Cases
This model is suitable for a wide range of general-purpose natural language processing applications where an 8 billion parameter model provides a good balance of performance and computational efficiency. Its efficient fine-tuning process suggests potential for rapid adaptation to specific downstream tasks.