🤘 awesome-semantic-segmentation
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Updated
May 8, 2021
🤘 awesome-semantic-segmentation
🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with LlamaIndex, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23
Test your prompts, agents, and RAGs. Red teaming, pentesting, and vulnerability scanning for LLMs. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and CI/CD integration.
OpenCompass is an LLM evaluation platform, supporting a wide range of models (Llama3, Mistral, InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets.
Python package for the evaluation of odometry and SLAM
AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation
Building a modern functional compiler from first principles. (http://dev.stephendiehl.com/fun/)
🧊 Open source LLM observability platform. One line of code to monitor, evaluate, and experiment. YC W23 🍓
Klipse is a JavaScript plugin for embedding interactive code snippets in tech blogs.
SuperCLUE: 中文通用大模型综合性基准 | A Benchmark for Foundation Models in Chinese
End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow
A unified evaluation framework for large language models
An open-source visual programming environment for battle-testing prompts to LLMs.
UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured checks (covering language, code, embedding use-cases), perform root cause analysis on failure cases and give insights on how to resolve them.
Accelerating the development of large multimodal models (LMMs) with one-click evaluation module - lmms-eval.
🤗 Evaluate: A library for easily evaluating machine learning models and datasets.
Open-source evaluation toolkit of large multi-modality models (LMMs), support 220+ LMMs, 80+ benchmarks
Avalanche: an End-to-End Library for Continual Learning based on PyTorch.
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