didula-wso2/qwen3-1-5_sft_16bit_vllm
The didula-wso2/qwen3-1-5_sft_16bit_vllm is an 8 billion parameter Qwen3 model, fine-tuned by didula-wso2. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for general language tasks, leveraging its Qwen3 architecture and efficient fine-tuning process.
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Model Overview
The didula-wso2/qwen3-1-5_sft_16bit_vllm is an 8 billion parameter Qwen3 model, fine-tuned by didula-wso2. This model was developed using the Unsloth library in conjunction with Huggingface's TRL library, which enabled a 2x faster training process compared to standard methods. It is based on the unsloth/Qwen3-8B-unsloth-bnb-4bit model.
Key Characteristics
- Architecture: Qwen3 family, providing robust general-purpose language capabilities.
- Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Fine-tuned with Unsloth, resulting in significantly faster training times.
- Context Length: Supports a context length of 32768 tokens, suitable for processing longer inputs.
Use Cases
This model is suitable for a variety of general language understanding and generation tasks where the Qwen3 architecture is beneficial. Its efficient fine-tuning process suggests it could be a good candidate for applications requiring a well-optimized, medium-sized language model.