tango
horde-client
tango | horde-client | |
---|---|---|
5 | 1 | |
508 | 8 | |
0.8% | - | |
6.5 | 7.0 | |
9 days ago | 7 months ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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tango
- AI2 Tango
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AllenNLP will be unmaintained in December
Maybe we need to re-work the docs if the DAG aspects stick out to you so much. The main functionality is the cache. If you have a complex experiment, you can still write the code as if all the steps were fast, and let them be slow only the first time you run it. The DAG stuff is also nice, but less important.
That said, you could execute sklearn. If that's what your experiment needs, it's the right thing to do. This is why it gives us the flexibility to also support Jax: https://github.com/allenai/tango/pull/313
The DL-specific stuff is in the components we supply. Like the trainer, dataset handling stuff, file formats, and increasingly, https://github.com/allenai/catwalk.
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AI2 Introduces Tango, A Python Library For Choreographing Machine Learning Research Experiments By Executing A Series Of Steps
Tango ensures you never operate on outdated data by taking care of your intermediate and final outcomes and finding them again when needed.
horde-client
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Horde-Client: Easy to use Python Interface for KoboldAI
I wanted to make things easier for developers (even me), who want to build cool application using these type of projects. So I am introducing a Python Client library horde-client, a Python Native client library for KoboldAI service.
What are some alternatives?
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
AutoLearn-GPT - ChatGPT learns automatically.
allennlp - An open-source NLP research library, built on PyTorch.
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
catwalk - This project studies the performance and robustness of language models and task-adaptation methods.
primeqa - The prime repository for state-of-the-art Multilingual Question Answering research and development.
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
ai-tools - Simple command-line AI chat assistant built using the OpenAI API