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Top 18 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/
Project mention: Top Courses and GitHub Repositories to Learn GenerativeAI Free | dev.to | 2024-08-17โ Generative AI for Beginners by Microsoft
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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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Project mention: Ask HN: Daily practices for building AI/ML skills? | news.ycombinator.com | 2023-12-14
coming from a similar context, i believe going top down might be the way to go.
up to your motivation, doing basic level courses first (as shared by others) and then tackling your own application of the concepts might be the way to go.
i also observe the need for strong IT skills for implementing end-to-end ml systems. so, you can play to your strenghts and also consider working on MLOps. (online self-paced course - https://github.com/GokuMohandas/mlops-course)
i went back to school to get structured learning. whether you find it directly useful or not, i found it more effective than just motivating myself to self-learn dry theory. down the line, if you want to go all-in, this might be a good option for you too.
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Project mention: A comprehensive guide to building RAG-based LLM applications for production | news.ycombinator.com | 2023-10-25
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Project mention: IsoFLOP curves of large language models are flat | news.ycombinator.com | 2024-08-02
There is some good published research about doing multiple passes over the training data, and how quickly learning saturates. The TL:DR is that diminishing returns kicks in after about 4 epochs.
https://arxiv.org/abs/2305.16264
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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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Project mention: A Simple Version of Grok 1.5/ GPT-4 Vision from scratch, in one PyTorch file | news.ycombinator.com | 2024-05-05
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langfuse-docs
๐ชข Langfuse documentation -- Langfuse is the open source LLM Engineering Platform. Observability, evals, prompt management, playground and metrics to debug and improve LLM apps
co-author here
we put in a lot of effort into our docs and we'd greatly appreciate any criticism or feedback! Langfuse is powerful but the docs should help beginners to quickly get started and then incrementally use more features.
docs are OSS, repo: https://github.com/langfuse/langfuse-docs
built using: https://github.com/shuding/nextra
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curlora
The code repository for the CURLoRA research paper. Stable LLM continual fine-tuning and catastrophic forgetting mitigation.
Project mention: Show HN: Fine-tune LLMs without catastrophic forgetting with CURLoRA approach | news.ycombinator.com | 2024-07-21 -
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.
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converso
Converso is a LangChain extension that streamlines data acquisition and action execution through natural language
Converso is a LangChain extension that introduces statefulness to better guide the LLM through the data acquisition process for more complex tools. Practically, it defines FormTools, which derive from LangChain tools, and the FormAgentExecutor, a LangGraph implementation capable of managing standard LangChain tools, FormTools and error correction.
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 discussion
Jupyter Notebook llms related posts
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IsoFLOP curves of large language models are flat
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Show HN: Fine-tune LLMs without catastrophic forgetting with CURLoRA approach
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Continually fine-tune large language models without catastrophic forgetting
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Show HN: CURLoRA research code: avoid catastrophic forgetting in LLM fine-tuning
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A Simple Version of Grok 1.5/ GPT-4 Vision from scratch, in one PyTorch file
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Gemini is only 1x Chinchilla, so it undertrained for production
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finsight: NEW Textual - star count:104.0
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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 | 62,254 |
| 2 | Made-With-ML | 37,139 |
| 3 | hands-on-llms | 2,996 |
| 4 | mlops-course | 2,838 |
| 5 | llm-applications | 1,680 |
| 6 | vectordb-recipes | 584 |
| 7 | datablations | 314 |
| 8 | beyondllm | 238 |
| 9 | Local-LLM-Langchain | 211 |
| 10 | seemore | 153 |
| 11 | PandasAI-Tutorials | 76 |
| 12 | langfuse-docs | 48 |
| 13 | chatXML | 42 |
| 14 | curlora | 36 |
| 15 | Heb-Gen-AI | 30 |
| 16 | TLDR-the-TnC | 3 |
| 17 | ml | 2 |
| 18 | converso | 2 |
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