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Top 11 Jupyter Notebook llm Projects
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generative-ai-for-beginners
18 Lessons, Get Started Building with Generative AI ๐ https://microsoft.github.io/generative-ai-for-beginners/
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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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hands-on-llms
๐ฆ ๐๐ฒ๐ฎ๐ฟ๐ป about ๐๐๐ ๐, ๐๐๐ ๐ข๐ฝ๐, and ๐๐ฒ๐ฐ๐๐ผ๐ฟ ๐๐๐ for free by designing, training, and deploying a real-time financial advisor LLM system ~ ๐ด๐ฐ๐ถ๐ณ๐ค๐ฆ ๐ค๐ฐ๐ฅ๐ฆ + ๐ท๐ช๐ฅ๐ฆ๐ฐ & ๐ณ๐ฆ๐ข๐ฅ๐ช๐ฏ๐จ ๐ฎ๐ข๐ต๐ฆ๐ณ๐ช๐ข๐ญ๐ด
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Local-LLM-Langchain
Load local LLMs effortlessly in a Jupyter notebook for testing purposes alongside Langchain or other agents. Contains Oobagooga and KoboldAI versions of the langchain notebooks with examples.
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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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finsight
FinSight - Financial Insights at Your Fingertip: FinSight is a cutting-edge AI assistant tailored for portfolio managers, investors, and finance enthusiasts. It streamlines the process of gaining crucial insights and summaries about a company in a user-friendly manner.
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Heb-Gen-AI
Tools, examples, and resources to assist in the development of Gen-AI (Generative Artificial Intelligence) applications in Hebrew, with a particular emphasis on working with Large Language Models (LLMs).
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TLDR-the-TnC
TLDR the T&C uses an LLM to understand the contents of Terms and Conditions documents and provides a user-friendly chatbot interface for users to ask questions and receive answers.
Generative AI For Beginners: a collection of resources to learn about Generative AI, including tutorials, code samples, and more.
Project mention: [D] How do you keep up to date on Machine Learning? | /r/learnmachinelearning | 2023-08-13Made With ML
There are 3 courses that I usually recommend to folks looking to get into MLE/MLOps that already have a technical background. The first is a higher-level look at the MLOps processes, common challenges and solutions, and other important project considerations. It's one of Andrew Ng's courses from Deep Learning AI but you can audit it for free if you don't need the certificate: - Machine Learning in Production For a more hands-on, in-depth tutorial, I'd recommend this course from NYU (free on GitHub), including slides, scripts, full-code homework: - Machine Learning Systems And the title basically says it all, but this is also a really good one: - Hands-on Train and Deploy ML Pau Labarta, who made that last course, actually has a series of good (free) hands-on courses on GitHub. If you're interested in getting started with LLMs (since every company in the world seems to be clamoring for them right now), this course just came out from Pau and Paul Iusztin: - Hands-on LLMs For LLMs I also like this DLAI course (that includes Prompt Engineering too): - Generative AI with LLMs It can also be helpful to start learning how to use MLOps tools and platforms. I'll suggest Comet because I work there and am most familiar with it (and also because it's a great tool). Cloud and DevOps skills are also helpful. Make sure you're comfortable with git. Make sure you're learning how to actually deploy your projects. Good luck! :)
Project mention: A comprehensive guide to building RAG-based LLM applications for production | news.ycombinator.com | 2023-10-25
Project mention: Lance is 100x faster than Parquet Use it to make LLM applications | news.ycombinator.com | 2023-10-01https://github.com/lancedb/vectordb-recipes
thanks
Project mention: Gemini is only 1x Chinchilla, so it undertrained for production | /r/singularity | 2023-12-071x chinchilla means it's not really undertrained but that more could be squeezed without excessive difficulty https://arxiv.org/abs/2305.16264
Project mention: Implementation of vision language model in a single file of PyTorch | news.ycombinator.com | 2024-04-22
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 llms related posts
- Gemini is only 1x Chinchilla, so it undertrained for production
- finsight: NEW Textual - star count:104.0
- A comprehensive guide to building RAG-based LLM applications for production
- Chinchillaโs Death
- Semantic Search Engine/Recommendation System on Amazon reviews
- Semantic Search Engine/Recommendation System on Amazon reviews
- RWKV Pile+ seems to be training on far more tokens than any LLM ever has
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A note from our sponsor - SaaSHub
www.saashub.com | 26 Apr 2024
Index
What are some of the best open-source llm projects in Jupyter Notebook? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | generative-ai-for-beginners | 42,394 |
| 2 | Made-With-ML | 35,656 |
| 3 | hands-on-llms | 2,232 |
| 4 | llm-applications | 1,486 |
| 5 | vectordb-recipes | 365 |
| 6 | datablations | 289 |
| 7 | Local-LLM-Langchain | 205 |
| 8 | finsight | 175 |
| 9 | Heb-Gen-AI | 30 |
| 10 | TLDR-the-TnC | 3 |
| 11 | seemore | 1 |
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