ontogpt
Promptify
| ontogpt | Promptify | |
|---|---|---|
| 2 | 29 | |
| 516 | 3,060 | |
| 5.8% | 2.3% | |
| 9.8 | 8.5 | |
| 7 days ago | 2 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| BSD 3-clause "New" or "Revised" 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.
ontogpt
Promptify
-
Promptify 2.0: More Structured, More Powerful LLMs with Prompt-Optimization, Prompt-Engineering, and Structured Json Parsing with GPT-n Models! 🚀
First up, a huge Thank You for making Promptify a hit with over 2.3k+ stars on Github ! 🌟
-
A minimal design pattern for LLM-powered microservices with FastAPI & LangChain
You're absolutely correct, and I agree that there's potentially a risk of quality loss. But likewise, since these are all intrinsically linked, it may be possible to leverage strength by combining these tasks. I'm unaware of a paper reviewing the reliability and/or performance of LLMs in this specific scenario. If you find any, do share :) With regards to generating JSON responses - there are simple ways to nudge the model and even validate it, using libraries such as https://github.com/promptslab/Promptify, https://github.com/eyurtsev/kor and https://github.com/ShreyaR/guardrails
- Promptify: Prompt Engineering Library
-
A python module to generate optimized prompts, Prompt-engineering & solve different NLP problems using GPT-n (GPT-3, ChatGPT) based models and return structured python object for easy parsing
Examples: https://github.com/promptslab/Promptify/tree/main/examples
-
Promptify - Prompt Engineering for Named Entity Recognition(NER)
In this blog, we are going to try to understand how promptify is going to be used along with LLMs(Large Language Models) to perform named entity recognition(NER).
-
[D] What ML dev tools do you wish you'd discovered earlier?
Check Promptify for LLM https://github.com/promptslab/Promptify
-
[P] Extracting Causal Chains from Text Using Language Models
Awesome project! I am working on something similar using Promptify (extending this PR -> https://github.com/promptslab/Promptify/issues/3)
- Classification using prompt or fine tuning?
What are some alternatives?
Get-Things-Done-with-Prompt-Engineering-and-LangChain - LangChain & Prompt Engineering tutorials on Large Language Models (LLMs) such as ChatGPT with custom data. Jupyter notebooks on loading and indexing data, creating prompt templates, CSV agents, and using retrieval QA chains to query the custom data. Projects for using a private LLM (Llama 2) for chat with PDF files, tweets sentiment analysis.
finetuner - :dart: Task-oriented embedding tuning for BERT, CLIP, etc.
DeepLearningExamples - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
causal-chains - Library for creating causal chains using language models.
voice-assistant-whisper-chatgpt - This repository will guide you to create your own Smart Virtual Assistant like Google Assistant using Open AI's ChatGPT, Whisper. The entire solution is created using Python & Gradio.
kor - LLM(😽)
llmware - Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.
llm-api-starterkit - Beginner-friendly repository for launching your first LLM API with Python, LangChain and FastAPI, using local models or the OpenAI API.
FinGPT - FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
guardrails - Adding guardrails to large language models.