promptfoo
AgileRL
| promptfoo | AgileRL | |
|---|---|---|
| 21 | 12 | |
| 3,100 | 505 | |
| 28.1% | 4.6% | |
| 9.9 | 9.8 | |
| 2 days ago | 2 days ago | |
| TypeScript | 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.
promptfoo
- Google CodeGemma: Open Code Models Based on Gemma [pdf]
- AI Infrastructure Landscape
- Promptfoo – Testing and Evaluation for LLMs
-
Show HN: Prompt-Engineering Tool: AI-to-AI Testing for LLM
Super interesting. We've been experimenting with [promptfoo](https://github.com/promptfoo/promptfoo) at my work, and this looks very similar.
- GitHub – promptfoo/promptfoo: Test your prompts
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I asked 60 LLMs a set of 20 questions
In case anyone's interested in running their own benchmark across many LLMs, I've built a generic harness for this at https://github.com/promptfoo/promptfoo.
I encourage people considering LLM applications to test the models on their _own data and examples_ rather than extrapolating general benchmarks.
This library supports OpenAI, Anthropic, Google, Llama and Codellama, any model on Replicate, and any model on Ollama, etc. out of the box. As an example, I wrote up an example benchmark comparing GPT model censorship with Llama models here: https://promptfoo.dev/docs/guides/llama2-uncensored-benchmar.... Hope this helps someone.
- Ask HN: Prompt Manager for Developers
- DeepEval – Unit Testing for LLMs
- Show HN: Knit – A Better LLM Playground
- Show HN: CLI for testing and evaluating LLM outputs
AgileRL
- [P] Introducing PPO and Rainbow DQN to our super fast evolutionary HPO reinforcement learning framework
- Introducing PPO and Rainbow DQN to our super fast evolutionary HPO reinforcement learning framework
-
[P] Significant improvements for multi-agent reinforcement learning!
Please check it out! https://github.com/AgileRL/AgileRL
- 10x faster reinforcement learning hyperparameter optimization than SOTA - now with distributed training!
- [P] 10x faster reinforcement learning hyperparameter optimization than SOTA - now with distributed training!
-
(1/2) May 2023
Deep Reinforcement Learning library focused on improving development by introducing RLOps - MLOps for reinforcement learning (https://github.com/AgileRL/AgileRL)
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[P] 10x faster reinforcement learning HPO - now for RLHF!
https://github.com/AgileRL/AgileRL/blob/main/CONTRIBUTING.md Has a link to our discord too
- 10x faster reinforcement learning HPO - now with CNNs!
- [P] 10x faster reinforcement learning HPO - now with CNNs!
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[P] Reinforcement learning evolutionary hyperparameter optimization - 10x speed up
GitHub: https://github.com/AgileRL/AgileRL
What are some alternatives?
shap-e - Generate 3D objects conditioned on text or images
chat-ui - Open source codebase powering the HuggingChat app
prompt-engineering - Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
RLeXplore - RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploration (RIDE).
WizardLM - Family of instruction-following LLMs powered by Evol-Instruct: WizardLM, WizardCoder and WizardMath
loopquest - A Production Tool for Embodied AI
de-torch - Minimal PyTorch Library for Differential Evolution
litellm - Call all LLM APIs using the OpenAI format. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs)
Muzero - Pytorch Implementation of MuZero for gym environment. It support any Discrete , Box and Box2D configuration for the action space and observation space.
ChainForge - An open-source visual programming environment for battle-testing prompts to LLMs.
q-learning-algorithms - This repository will aim to provide implementations of q-learning algorithms (DQN, Double-DQN, ...) using Pytorch.