Fine Tuning Qwen 2 with Custom Data

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Why Fine Tune?
Fine-tuning Qwen 2, a large language model (LLM), is essential for optimal performance and customization. It improves accuracy and efficiency for specific tasks like customer support and content creation. Tailoring the model to industry-specific needs enhances its understanding of specialized terminology and context. Fine-tuning also reduces biases and ensures ethical compliance, providing fair and appropriate responses. Regular updates keep the model relevant with new data and trends. Additionally, it improves interpretability and control, aiding in debugging and continuous improvement. Ultimately, fine-tuning Qwen 2 offers superior user experiences, strategic business advantages, and cost efficiency.
What is Qwen 2?
Qwen 2 is a series of large language models developed by Alibaba Cloud, designed to excel in various AI tasks. The Qwen 2 models range in size from 0.5 billion to 72 billion parameters, making them versatile for applications such as language understanding, generation, multilingual tasks, coding, and mathematics.
The Qwen 2 series boasts significant improvements in performance and efficiency. Leveraging advanced techniques like Group Query Attention, these models deliver faster processing with reduced memory usage. They support extended context lengths up to 128K tokens, enhancing their capability to manage long-form content.
Trained on data in 29 languages, including English, Chinese, German, Italian, Arabic, Persian, and Hebrew, Qwen 2 models excel in multilingual tasks. They have demonstrated superior performance on various benchmarks, surpassing other leading open-source models in language understanding and generation tasks.
Qwen 2 models are also designed with responsible AI principles in mind, incorporating human feedback to align better with human values and safety standards. They perform well in safety benchmarks, effectively handling unsafe multilingual queries to prevent misuse related to illegal activities.
These models are available on platforms like Hugging Face and Alibaba Cloud’s ModelScope, facilitating easy deployment for both commercial and research purposes.

[@hackedbyBLAGH]

How about confidential data

[@moslehmahamud]

Sure! Qwen 2 and other open source models are perfect for confidential data

[@ivanleung6034]

There is error in module 'IPython.utils.traitlets' has no attribute 'Unicode' for the google.colab lib. Cannot even start to finetune