From 1ec53270abdae204bd7ebdfd482486102a67d9f6 Mon Sep 17 00:00:00 2001 From: "Xin(Leo) Jing" <jingxin@berkeley.edu> Date: Mon, 18 Sep 2023 23:18:11 -0700 Subject: [PATCH] Update README.md --- README.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/README.md b/README.md index 8cfa91a..7dedf98 100644 --- a/README.md +++ b/README.md @@ -310,6 +310,28 @@ The above tables coule be better summarized by this wonderful visualization from - [IntelliServer](https://github.com/intelligentnode/IntelliServer) - simplifies the evaluation of LLMs by providing a unified microservice to access and test multiple AI models. +## Prompting libraries & tools + +- [YiVal](https://github.com/YiVal/YiVal): Evaluate and Evolve — YiVal is an open-source GenAI-Ops tool for tuning and evaluating prompts, configurations, and model parameters using customizable datasets, evaluation methods, and improvement strategies. +- [Guidance](https://github.com/microsoft/guidance): A handy looking Python library from Microsoft that uses Handlebars templating to interleave generation, prompting, and logical control. +- [LangChain](https://github.com/hwchase17/langchain): A popular Python/JavaScript library for chaining sequences of language model prompts. +- [FLAML (A Fast Library for Automated Machine Learning & Tuning)](https://microsoft.github.io/FLAML/docs/Getting-Started/): A Python library for automating selection of models, hyperparameters, and other tunable choices. +- [Chainlit](https://docs.chainlit.io/overview): A Python library for making chatbot interfaces. +- [Guardrails.ai](https://shreyar.github.io/guardrails/): A Python library for validating outputs and retrying failures. Still in alpha, so expect sharp edges and bugs. +- [Semantic Kernel](https://github.com/microsoft/semantic-kernel): A Python/C#/Java library from Microsoft that supports prompt templating, function chaining, vectorized memory, and intelligent planning. +- [Prompttools](https://github.com/hegelai/prompttools): Open-source Python tools for testing and evaluating models, vector DBs, and prompts. +- [Outlines](https://github.com/normal-computing/outlines): A Python library that provides a domain-specific language to simplify prompting and constrain generation. +- [Promptify](https://github.com/promptslab/Promptify): A small Python library for using language models to perform NLP tasks. +- [Scale Spellbook](https://scale.com/spellbook): A paid product for building, comparing, and shipping language model apps. +- [PromptPerfect](https://promptperfect.jina.ai/prompts): A paid product for testing and improving prompts. +- [Weights & Biases](https://wandb.ai/site/solutions/llmops): A paid product for tracking model training and prompt engineering experiments. +- [OpenAI Evals](https://github.com/openai/evals): An open-source library for evaluating task performance of language models and prompts. +- [LlamaIndex](https://github.com/jerryjliu/llama_index): A Python library for augmenting LLM apps with data. +- [Arthur Shield](https://www.arthur.ai/get-started): A paid product for detecting toxicity, hallucination, prompt injection, etc. +- [LMQL](https://lmql.ai): A programming language for LLM interaction with support for typed prompting, control flow, constraints, and tools. + + + ## Tutorials about LLM - [Andrej Karpathy] State of GPT [video](https://build.microsoft.com/en-US/sessions/db3f4859-cd30-4445-a0cd-553c3304f8e2) - [Hyung Won Chung] Instruction finetuning and RLHF lecture [Youtube](https://www.youtube.com/watch?v=zjrM-MW-0y0)