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Label Studio 1.8.0 Release — Open source data labelling platform for AI model tuning
We’re on a mission to help data scientists build the most accurate AI models! Label Studio is the most popular open source data labeling platform used to fine-tune LLMs and prepare training data. Label any type of data, integrate ML models, and more! 💪🏻
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💎 Pixel perfection
Whoa, this is a gift for me. Going to research a lot of open-source LLMs soon + had a very positive experience with previous versions of LS. Congrats on the launch!
@zot Thanks so much — I'm glad you enjoyed previous LS versions! Excited to see what you create!
@vladimir_zivkovic Thanks @vladimir_vojvodic, much appreciated. I just hunted it the Label Studio team are killers!
Congrats on the launch! 🚀 This looks so cool! How do you facilitate the process of fine-tuning LLMs?
@irina_haupt Good question, Irina! Label Studio is a data labeling platform that allows data scientists to fine-tune LLMs through human-in-the-loop-style retraining or RLHF retraining.
To put it simply — this is the "interface" where one can provide feedback to the model and coach it to better fit your needs.
You can read more about model retraining with Label Studio here: https://labelstud.io/blog/human-...
or check out a demo on our GitHub here: https://github.com/heartexlabs/RLHF
@erinmikail Thanks for clarifying! Love the fact that you incorporate a human aspect into the training process - sounds like the best of both worlds!
Wow, thanks for sharing such a cool fine-tune labeling LM engine, wished that I know it earlier for our eComm platform.
Congrats Chris & team on your launch & appreciated for your contribution!
Must try for our next Product feature!
@nuno_ms_reis Thanks a ton! We're excited to see the future of Label Studio and to support builders like you!
I can't believe I didn't discover this product sooner. It has completely transformed the way I work, saving me time and effort. Thank you, Product Hunt, for bringing it to my attention!
I'm impressed by the level of innovation in this product. It tackles a common problem in a unique and refreshing way.
@iwozik ❤️ Much love back at you Olle! Honored to know you and to have collaborated with you! I see a llthe work that you're doing with GameDev and rooting you on all the way!
💎 Pixel perfection
Hey Product Hunt! Generative AI and large language models like ChatGPT and LLaMa have brought innovations in machine learning and new MLOps processes. The need for enhanced tooling to leverage these models to build domain-specific applications is growing rapidly.
That's why, with our 1.8.0 release of Label Studio, we’ve made the process of fine-tuning LLMs and foundation models even easier. 💪🏻🚀
Some of the new features we're excited about in this release Include:
🆕 Ranker Interface
Label Studio now has a new, improved interface designed for ranked choice model retraining. This Ranker interface is designed to work with ranking models that predict a relevance score. This brings an updated tag that can be configured to evaluate and compare responses.
🆕 Generative AI Templates
We’re introducing new labeling templates specifically designed for generative AI! Check out the templates for:
* Supervised language model fine-tuning
* Human preference collecting for RLHF
* Chatbot model assessment
* LLM Ranker
* Visual ranker
🚀 Interface Improvements
We've added side panel updates to better display important details about what you’re annotating, including relations, comments, and history.
There's a new annotations tab carousel that allows you to view all annotations simultaneously!
We've relocated the control button panel for an improved workflow.
Thanks for checking us out!
@nate_kartchner features look incredibly promising. The advancements in generative AI and large language models are transforming the way we handle machine learning tasks, and Label Studio seems to be keeping pace with these advancements brilliantly.
The new Ranker Interface stands out as a significant upgrade, promising a more intuitive and effective way to retrain ranking models. The potential it offers in terms of evaluating and comparing responses is something I'm looking forward to exploring.
@sarah_harris5 Thanks so much! It's pretty exciting to see the ways in which foundation models are changing the way machine learning operations are run. Human feedback will always have value, but we've already seen the ways in which it's valuable to machine learning change, and I expect it will continue to do so. Our goal is to keep developing to make sure that we keep up with that. :)
Enjoy your explorations with Ranker - we'd love to get your feedback as you do so!
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