PyBeam-QA
giskard
| PyBeam-QA | giskard | |
|---|---|---|
| 1 | 7 | |
| 11 | 3,192 | |
| - | 13.6% | |
| 7.0 | 10.0 | |
| about 1 month ago | 1 day ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
PyBeam-QA
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Open source GUI tool for QA and linac QC tests
Source code: https://github.com/Quantico-Bullet/PyBeam-QA
giskard
- Show HN: Evaluate LLM-based RAG Applications with automated test set generation
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Why is it so important to evaluate Large Language Models (LLMs)? 🤯🔥
Unchecked biases in LLMs can inadvertently perpetuate harmful stereotypes or produce misleading information, which in turn can produce severe consequences. In this article, we'll demonstrate how to evaluate your LLMs using an open source model testing framework, Giskard. 🤓
- The testing framework dedicated to ML models, from tabular to LLMs
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Show HN: Python library to scan ML models for vulnerabilities
Hi! I’ve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, …) and integrates with the quality assurance solution we are building at Giskard AI (https://giskard.ai) to systematically test models before putting them in production.
It is still a beta and I would love to hear your feedback if you have the time to try it out.
We have quite a few tutorials in the docs with ready-made colab notebooks to make it easy to get started.
If you are interested in the code:
https://github.com/Giskard-AI/giskard/tree/main/python-clien...
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[P] Open-source solution to scan AI models for vulnerabilities
Sure! Benjamini-Hochberg is a very good recommendation, much simpler than the alpha investing procedures I mentioned which makes it easily to implement in our case. I will give it a try, if there’s an easy way to set this up it could be included in some of the next releases. I’ll let you know. FYI, I added this to our issue tracker.
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[R] LMFlow Benchmark: An Automatic Evaluation Framework for Open-Source LLMs
This is super interesting! Thanks for sharing. We're also working on this research field from an open-source angle (https://github.com/Giskard-AI/giskard)
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How are you testing your ML Systems?
Code repository: https://github.com/Giskard-AI/giskard
What are some alternatives?
pymedphys - A community effort to develop an open standard library for Medical Physics in Python. Building quality transparent software together via peer review and open source distribution. Open code is better science.
deepchecks - Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
Osintgram - Osintgram is a OSINT tool on Instagram. It offers an interactive shell to perform analysis on Instagram account of any users by its nickname
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
angr - A powerful and user-friendly binary analysis platform!
awesome-ai-safety - 📚 A curated list of papers & technical articles on AI Quality & Safety
flakehell - Flake8 wrapper to make it nice, legacy-friendly, configurable.
LMFlow - An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
ward - Ward is a modern test framework for Python with a focus on productivity and readability.
MindsDB - The platform for customizing AI from enterprise data