Training on Qwen-7B gives: ValueError: Asking to pad but the tokenizer does not have a padding token

The BigManGPT training program utilizes the Qwen-7B model and the Squad dataset, aiming to achieve enhanced performance through advanced quantization methods. By applying QLoRA Quantization with 4-bit precision, the model ensures efficient memory usage while maintaining the effectiveness of its inference capabilities. This strategic approach is critical in the context of large language models, as it balances computational resources and model accuracy, ultimately paving the way for more scalable applications in natural language processing.

BigManGPT utilizes Qwen-7B and the Squad dataset for training, leveraging state-of-the-art quantization techniques to enhance model performance and efficiency.

By implementing QLoRA Quantization with 4-bit precision, BigManGPT aims to optimize memory usage without notably compromising the model's inference capabilities.

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