The concept of the tool is promising, and I think it could be very useful if it worked as intended.
I have faced numerous technical issues, and many features seem to be either buggy or incomplete. It often crashes during model training, which is very frustrating.
While it aims to help with model personalization, the frequent problems I encounter prevent me from benefiting from its intended features.
The simplicity of building and customizing models is fantastic. I really like how I can get started quickly without needing extensive programming knowledge.
The performance can be a bit slow with larger datasets, which has been an issue for some of my projects that require quick iterations.
It helps me efficiently fine-tune models tailored to specific tasks, enhancing the accuracy of my applications significantly. This has made a noticeable impact on my productivity.
I like the variety of models supported, which gives me plenty of options to choose from based on my project requirements.
The learning curve can be steep for beginners. While it is user-friendly, getting to know all features requires time and effort.
It simplifies the model fine-tuning process, which is beneficial for my work in academic research. However, I wish it had more tutorials to help new users.
I appreciate the open-source nature of Xturing, which allows me to customize the models according to my specific needs. The user-friendly interface makes it easier for someone like me, with a moderate level of experience, to dive into customization.
The documentation could be more comprehensive. I had some trouble figuring out certain features that aren't well explained, which can be frustrating when trying to implement them into my projects.
Xturing helps streamline the process of fine-tuning large language models, which is crucial for my work in natural language processing. This has saved me a lot of time compared to other tools, allowing me to focus on developing applications rather than getting bogged down in technical details.
The flexibility of the tool is a strong point, as it supports various models, which is great for experimenting with different datasets.
Unfortunately, I encountered several bugs during my usage, which made the experience quite frustrating. The support response times were also longer than I expected.
It allows me to generate datasets from user data sources, which is essential for my research on user behavior. However, the issues I've faced have hindered my ability to fully utilize this benefit.
