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Top 23 Jupyter Notebook chatgpt Projects
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generative-ai-for-beginners
18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
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autogen
A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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FinGPT
FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
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Promptify
Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research
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Alpaca-CoT
We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient methods (e.g., lora, p-tuning) together for easy use. We welcome open-source enthusiasts to initiate any meaningful PR on this repo and integrate as many LLM related technologies as possible. 我们打造了方便研究人员上手和使用大模型等微调平台，我们欢迎开源爱好者发起任何有意义的pr！
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awesome-generative-ai
A curated list of Generative AI tools, works, models, and references (by filipecalegario)
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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ChatPDF
Chat with any PDF. Easily upload the PDF documents you'd like to chat with. Instant answers. Ask questions, extract information, and summarize documents with AI. Sources included.
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chameleon-llm
Codes for "Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models".
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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.
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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.
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awesome-assistant-api
Try openai assistant api apps on Google Colab for free. Awesome assistant API Demos!
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Smarty-GPT
A wrapper of LLMs that biases its behaviour using prompts and contexts in a transparent manner to the end-users
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awesome-chatgpt-plugins
A curated and categeorized list of all of the ChatGPT plugins available within ChatGPT plus, includes detailed descriptions and usage docs, as well as unofficial sources of plugins (by HighwayofLife)
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ChatGPT-Python-Applications
ChatGPT Python Applications integrated with third party libraries and modules
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Generative AI For Beginners: a collection of resources to learn about Generative AI, including tutorials, code samples, and more.
https://www.manning.com/books/build-a-large-language-model-f...
Project mention: GPT-4, without specialized training, beat a GPT-3.5 class model that cost $10B | news.ycombinator.com | 2024-03-24There is also the open source FinGPT, that is claimed to beat GPT4 in some benchmarks at a fine tuning cost of $17.25.
https://github.com/AI4Finance-Foundation/FinGPT
Project mention: Promptify 2.0: More Structured, More Powerful LLMs with Prompt-Optimization, Prompt-Engineering, and Structured Json Parsing with GPT-n Models! 🚀 | /r/ArtificialInteligence | 2023-07-31First up, a huge Thank You for making Promptify a hit with over 2.3k+ stars on Github ! 🌟
Project mention: Generative AI – A curated list of Generative AI tools, works, models | news.ycombinator.com | 2023-07-14
For example your website (https://embedai.thesamur.ai/) uses Google Login, Google Fonts, Google Tagmanager, Google Analytics and translate.googleapis.com. I'd assume Google will all user data for maximum profit. For each service there are free and self-hostable alternatives.
> Do you know any active research in this area? I briefly considered playing with this, but my back-of-the-envelope semi-educated feeling for now is that it won't scale.
I am aware of a couple of potentially promising research directions. One formally academic called Chameleon [0], and one that's more like a grassroots organic effort that aims to build an actually functional Auto-GPT-like, called Agent-LLM [1]. I have read the Chameleon paper, and I must say I'm quite impressed with their architecture. It added a few bits and pieces that most of the early GPT-based agents didn't have, and I have a strong intuition that these will contribute to these things actually working.
Auto-GPT is another, relatively famous piece of work in this area. However, at least as of v0.2.2, I found it relatively underwhelming. For any online knowledge retrieval+synthesis and retrieval+usage tasks, it seemed to get stuck, but it did sort-of-kind-of OK on plain online knowledge retrieval. After having a look at the Auto-GPT source code, my intuition (yes, I know - "fuzzy feelings without a solid basis" - but I believe that this is simply due to not having an AI background to explain this with crystal-clear wording) is that the poor performance of the current version of Auto-GPT is insufficient skill in prompt-chain architecture and the surprisingly low quality and at times buggy code.
I think Auto-GPT has some potential. I think the implementation lets down the concept, but that's just a question of refactoring the prompts and the overall code - which it seems like the upstream Github repo has been quite busy with, so I might give it another go in a couple of weeks to see how far it's moved forward.
> Specifically, as task complexity grows, the amount of results to combine will quickly exceed the context window size of the "combiner" GPT-4. Sure, you can stuff another layer on top, turning it into a tree/DAG, but eventually, I think the partial result itself will be larger than 8k, or even 32k tokens - and I feel this "eventually" will be hit rather quickly. But maybe my feelings are wrong and there is some mileage in this approach.
Auto-GPT uses an approach based on summarisation and something I'd term 'micro-agents'. For example, when Auto-GPT is searching for an answer to a particular question online, for each search result it finds, it spins up a sub-chain that gets asked a question 'What does this page say about X?' or 'Based on the contents of this page, how can you do Y?'. Ultimately, intelligence is about lossy compression, and this is a starkly exposed when it comes to LLMs because you have no choice but to lose some information.
> I think the partial result itself will be larger than 8k, or even 32k tokens - and I feel this "eventually" will be hit rather quickly. But maybe my feelings are wrong and there is some mileage in this approach.
The solution to that would be to synthesize output section by section, or even as an "output stream" that can be captured and/or edited outside the LLM in whole or in chunks. IMO, I do think there's some mileage to be exploited in a recursive "store, summarise, synthesise" approach, but the problem will be that of signal loss. Every time you pass a subtask to a sub-agent, or summarise the outcome of that sub-agent into your current knowledge base, some noise is introduced. It might be that the signal to noise ratio will dissipate as higher and higher order LLM chains are used - analogously to how terrible it was to use electricity or radio waves before any amplification technology became available.
One possible avenue to explore to crack down on decreasing SNR (based on my own original research, but I can also see some people disclosing online that they are exploring the same path), is to have a second LLM in the loop, double-checking the result of the first one. This has some limitations, but I have successfully used this approach to verify that, for example, the LLM does not outright refuse to carry out a task. This is currently cost-prohibitive to do in a way that would make me personally satisfied and confident enough in the output to make it run full-auto, but I expect that increasing ability to run AI locally will make people more willing to experiment with massive layering of cooperating LLM chains that check each others' work, cooperate, and/or even repeat work using different prompts to pick the best output a la redundant avionics computers.
[0]: https://github.com/lupantech/chameleon-llm
Project mention: Get-Things-Done-with-Prompt-Engineering-and-LangChain: NEW Data - star count:617.0 | /r/algoprojects | 2023-12-10
Project mention: This is a Ghidra script that calls OPENAI to give meaning to decompiled functions. Another level of reverse engineering. | /r/redteamsec | 2023-05-09Also check https://github.com/NoDataFound/hackGPT
Project mention: Try OpenAI Assistant API Apps on Google Colab for Free | news.ycombinator.com | 2023-11-13
Project mention: Langforge: A Toolkit for Creating and Deploying LangChain Apps | news.ycombinator.com | 2023-04-27
Project mention: The ChatGPT Plugin Descriptions are Terrible, so I fixed them | /r/coolaitools | 2023-07-21
While there are abundant researches about evaluating ChatGPT on natural language understanding and generation tasks, few studies have investigated how ChatGPT's behavior changes over time. In this paper, we collect a coarse-to-fine temporal dataset called ChatLog, consisting of two parts that update monthly and daily: ChatLog-Monthly is a dataset of 38,730 question-answer pairs collected every month including questions from both the reasoning and classification tasks. ChatLog-Daily, on the other hand, consists of ChatGPT's responses to 1000 identical questions for long-form generation every day. We conduct comprehensive automatic and human evaluation to provide the evidence for the existence of ChatGPT evolving patterns. We further analyze the unchanged characteristics of ChatGPT over time by extracting its knowledge and linguistic features. We find some stable features to improve the robustness of a RoBERTa-based detector on new versions of ChatGPT. We will continuously maintain our project at https://github.com/THU-KEG/ChatLog.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Jupyter Notebook chatgpt related posts
- GPT-4, without specialized training, beat a GPT-3.5 class model that cost $10B
- Comparing 5 ways to implement Multihead Attention in PyTorch
- Generative AI for Beginners – 18 Lessons
- Generative AI for Beginners – Version 2
- Implementing a ChatGPT-like LLM from scratch, step by step
- FinGPT
- Get-Things-Done-with-Prompt-Engineering-and-LangChain: NEW Data - star count:617.0
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A note from our sponsor - InfluxDB
www.influxdata.com | 25 Apr 2024
Index
What are some of the best open-source chatgpt projects in Jupyter Notebook? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | generative-ai-for-beginners | 42,394 |
| 2 | autogen | 24,917 |
| 3 | LLMs-from-scratch | 13,954 |
| 4 | FinGPT | 11,419 |
| 5 | Promptify | 3,013 |
| 6 | Alpaca-CoT | 2,463 |
| 7 | awesome-generative-ai | 1,971 |
| 8 | ChatPDF | 1,250 |
| 9 | chameleon-llm | 1,017 |
| 10 | Get-Things-Done-with-Prompt-Engineering-and-LangChain | 922 |
| 11 | hackGPT | 737 |
| 12 | voice-assistant-whisper-chatgpt | 219 |
| 13 | ClassGPT | 209 |
| 14 | awesome-assistant-api | 184 |
| 15 | langforge | 162 |
| 16 | Smarty-GPT | 142 |
| 17 | awesome-chatgpt-plugins | 126 |
| 18 | FastLoRAChat | 119 |
| 19 | ChatGPT-Python-Applications | 118 |
| 20 | chatlab | 111 |
| 21 | MusicWithChatGPT | 102 |
| 22 | ChatLog | 93 |
| 23 | BLOOM-fine-tuning | 38 |
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