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Top 23 Jupyter Notebook llm Projects
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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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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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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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awesome-generative-ai
A curated list of Generative AI tools, works, models, and references (by filipecalegario)
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tensor-house
A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.
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chameleon-llm
Codes for "Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models".
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llm-colosseum
Benchmark LLMs by fighting in Street Fighter 3! The new way to evaluate the quality of an LLM
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Agently
[AI Agent Application Development Framework] - 🚀 Build AI agent native application in very few code 💬 Easy to interact with AI agent in code using structure data and chained-calls syntax 🧩 Enhance AI Agent using plugins instead of rebuild a whole new agent
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miyagi
Sample to envision intelligent apps with Microsoft's Copilot stack for AI-infused product experiences.
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tiger
Open Source LLM toolkit to build trustworthy LLM applications. TigerArmor (AI safety), TigerRAG (embedding, RAG), TigerTune (fine-tuning) (by tigerlab-ai)
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KG_RAG
Empower Large Language Models (LLM) using Knowledge Graph based Retrieval-Augmented Generation (KG-RAG) for knowledge intensive tasks
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SaaSHub
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Project mention: Ask HN: People who switched from GPT to their own models. How was it? | news.ycombinator.com | 2024-02-26This is a very nice resource: https://github.com/mlabonne/llm-course
Project mention: Insights from Finetuning LLMs for Classification Tasks | news.ycombinator.com | 2024-04-28
A French startup, Mistral AI has released two impressive large language models (LLMs) - Mistral 7B and Mixtral 8x7B. These models push the boundaries of performance and introduce a better architectural innovation aimed at optimizing inference speed and computational efficiency.
I've used the code based on similar examples from GitHub [1]. According to docs [2], imagegeneration@005 was released on the 11th, so I guessed it's Imagen 2, though there are no confirmations.
[1] https://github.com/GoogleCloudPlatform/generative-ai/blob/ma...
[2] https://console.cloud.google.com/vertex-ai/publishers/google...
Project mention: Yes, Python and Matplotlib can make pretty charts | news.ycombinator.com | 2024-04-16
Project mention: Generative AI – A curated list of Generative AI tools, works, models | news.ycombinator.com | 2023-07-14
Retrieval using a single vector is called dense passage retrieval (DPR), because an entire passage (dozens to hundreds of tokens) is encoded as a single vector. ColBERT instead encodes a vector-per-token, where each vector is influenced by surrounding context. This leads to meaningfully better results; for example, here’s ColBERT running on Astra DB compared to DPR using openai-v3-small vectors, compared with TruLens for the Braintrust Coda Help Desk data set. ColBERT easily beats DPR at correctness, context relevance, and groundedness.
Project mention: Looking for Paper about LLM Fine Tuning for specific topic / Alignment Paper | /r/LocalLLaMA | 2023-12-09
> 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: Generate SQL from Natural Language according Meta Data of Database in Python using LLM in Very Few Codes | /r/Python | 2023-12-06Colab Document: Use Google Colab to try it by yourself
Project mention: Super JSON Mode: Up to 20x Faster JSON Generation from LLMs | news.ycombinator.com | 2024-02-06
Project mention: A list of system prompts used for biomedical RAG (KG-RAG) using LLM | news.ycombinator.com | 2024-01-10
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 llm related posts
- Insights from Finetuning LLMs for Classification Tasks
- Why Vector Compression Matters
- LLM Colosseum
- Evaluate LLMs in Real Time with Street Fighter III
- 📈 Data-driven strategy automation w/ LIDA
- Mistral 7B vs. Mixtral 8x7B
- Comparing 5 ways to implement Multihead Attention in PyTorch
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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 | llm-course | 28,809 |
| 2 | LLMs-from-scratch | 14,142 |
| 3 | mistral-src | 8,666 |
| 4 | generative-ai | 5,396 |
| 5 | Anima | 2,881 |
| 6 | Alpaca-CoT | 2,463 |
| 7 | examples | 2,433 |
| 8 | lida | 2,400 |
| 9 | text-generation-webui-colab | 2,024 |
| 10 | awesome-generative-ai | 1,971 |
| 11 | trulens | 1,612 |
| 12 | ReAct | 1,557 |
| 13 | EasyEdit | 1,385 |
| 14 | tensor-house | 1,162 |
| 15 | chameleon-llm | 1,017 |
| 16 | llm-colosseum | 914 |
| 17 | LLM-Training-Puzzles | 731 |
| 18 | Agently | 686 |
| 19 | miyagi | 616 |
| 20 | tiger | 377 |
| 21 | super-json-mode | 336 |
| 22 | KG_RAG | 314 |
| 23 | fact-checker | 261 |
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