deepeval
super-gradients
deepeval | super-gradients | |
---|---|---|
22 | 8 | |
1,923 | 4,366 | |
20.2% | 2.1% | |
9.9 | 9.5 | |
2 days ago | 1 day ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | 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.
deepeval
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Unit Testing LLMs with DeepEval
For the last year I have been working with different LLMs (OpenAI, Claude, Palm, Gemini, etc) and I have been impressed with their performance. With the rapid advancements in AI and the increasing complexity of LLMs, it has become crucial to have a reliable testing framework that can help us maintain the quality of our prompts and ensure the best possible outcomes for our users. Recently, I discovered DeepEval (https://github.com/confident-ai/deepeval), an LLM testing framework that has revolutionized the way we approach prompt quality assurance.
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Show HN: Ragas – the de facto open-source standard for evaluating RAG pipelines
Checkout this instead: https://github.com/confident-ai/deepeval
Also has native ragas implementation but supports all models.
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Show HN: Times faster LLM evaluation with Bayesian optimization
Fair question.
Evaluate refers to the phase after training to check if the training is good.
Usually the flow goes training -> evaluation -> deployment (what you called inference). This project is aimed for evaluation. Evaluation can be slow (might even be slower than training if you're finetuning on a small domain specific subset)!
So there are [quite](https://github.com/microsoft/promptbench) [a](https://github.com/confident-ai/deepeval) [few](https://github.com/openai/evals) [frameworks](https://github.com/EleutherAI/lm-evaluation-harness) working on evaluation, however, all of them are quite slow, because LLM are slow if you don't have infinite money. [This](https://github.com/open-compass/opencompass) one tries to speed up by parallelizing on multiple computers, but none of them takes advantage of the fact that many evaluation queries might be similar and all try to evaluate on all given queries. And that's where this project might come in handy.
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Implemented 12+ LLM evaluation metrics so you don't have to
A link to a reddit post (with no discussion) which links to this repo
https://github.com/confident-ai/deepeval
- Show HN: I implemented a range of evaluation metrics for LLMs that runs locally
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These 5 Open Source AI Startups are changing the AI Landscape
Star DeepEval on GitHub and contribute to the advancement of LLM evaluation frameworks! 🌟
- FLaNK Stack Weekly 06 Nov 2023
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Why we replaced Pinecone with PGVector 😇
Pinecone, the leading closed-source vector database provider, is known for being fast, scalable, and easy to use. Its ability to allow users to perform blazing-fast vector search makes it a popular choice for large-scale RAG applications. Our initial infrastructure for Confident AI, the world’s first open-source evaluation infrastructure for LLMs, utilized Pinecone to cluster LLM observability log data in production. However, after weeks of experimentation, we made the decision to replace it entirely with pgvector. Pinecone’s simplistic design is deceptive due to several hidden complexities, particularly in integrating with existing data storage solutions. For example, it forces a complicated architecture and its restrictive metadata storage capacity made it troublesome for managing data-intensive workloads.
- Show HN: Unit Testing for LLMs
- Show HN: DeepEval – Unit Testing for LLMs (Open Science)
super-gradients
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Zero-Shot Prediction Plugin for FiftyOne
Most computer vision models are trained to predict on a preset list of label classes. In object detection, for instance, many of the most popular models like YOLOv8 and YOLO-NAS are pretrained with the classes from the MS COCO dataset. If you download the weights checkpoints for these models and run prediction on your dataset, you will generate object detection bounding boxes for the 80 COCO classes.
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Open Source Advent Fun Wraps Up!
23. SuperGradients | Github | tutorial
- FLaNK Stack Weekly 06 Nov 2023
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Autodistill: A new way to create CV models
And the target models include: * YOLOv8 (You Only Look Once) * YOLO-NAS * YOLOv5 * and DETR
- FLaNK Stack for 15 May 2023
- GitHub - Deci-AI/super-gradients: Easily train or fine-tune SOTA co...GitHub - Deci-AI/super-gradients: Easily train or fine-tune SOTA co...
- Meet YOLO-NAS: An Open-Sourced YOLO-based Architecture Redefining State-of-the-Art in Object Detection
- FLiPN-FLaNK Stack Weekly May 8 2023
What are some alternatives?
ragas - Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines
ultralytics - NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
litellm - Call all LLM APIs using the OpenAI format. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs)
SegGradCAM - SEG-GRAD-CAM: Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping
blog-examples
highstorm - Open Source Event Monitoring
openvino_notebooks - 📚 Jupyter notebook tutorials for OpenVINO™
pezzo - 🕹️ Open-source, developer-first LLMOps platform designed to streamline prompt design, version management, instant delivery, collaboration, troubleshooting, observability and more.
pyvideotrans - Translate the video from one language to another and add dubbing. 将视频从一种语言翻译为另一种语言,并添加配音
tailspin - 🌀 A log file highlighter
Detic - Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".