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Top 23 llm Open-Source 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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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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LibreChat
Enhanced ChatGPT Clone: Features OpenAI, Assistants API, Azure, Groq, GPT-4 Vision, Mistral, Bing, Anthropic, OpenRouter, Vertex AI, Gemini, AI model switching, message search, langchain, DALL-E-3, ChatGPT Plugins, OpenAI Functions, Secure Multi-User System, Presets, completely open-source for self-hosting. More features in development
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lance
Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, with more integrations coming..
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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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api-for-open-llm
Openai style api for open large language models, using LLMs just as chatgpt! Support for LLaMA, LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, Xverse, SqlCoder, CodeLLaMA, ChatGLM, ChatGLM2, ChatGLM3 etc. ๅผๆบๅคงๆจกๅ็็ปไธๅ็ซฏๆฅๅฃ
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infinity
The AI-native database built for LLM applications, providing incredibly fast full-text and vector search (by infiniflow)
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cortex
Drop-in, local AI alternative to the OpenAI stack. Multi-engine (llama.cpp, TensorRT-LLM). Powers ๐ Jan (by janhq)
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bionic-gpt
BionicGPT is an on-premise replacement for ChatGPT, offering the advantages of Generative AI while maintaining strict data confidentiality
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DemoGPT
Create ๐ฆ๏ธ๐ LangChain apps by just using prompts๐ Star to support our work! | ๅช้ไฝฟ็จๅฅๅญๅณๅฏๅๅปบ LangChain ๅบ็จ็จๅบใ ็ปไธชstarๆฏๆๆไปฌ็ๅทฅไฝๅง๏ผ
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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
Open WebUI (Formerly Ollama WebUI)
Two other tools emerging in this category are SuperMaven and TabbyML, both use fast and secure LLM for code completion and recommendations.
Package installer for Python (pip), we use this for installing the Python-based packages, such as Jupyter Lab, and we're going to use this for installing other Python-based tools like the Chroma DB vector database
Project mention: Show HN: A better UI for ChatGPT, Claude with text search, saved chats and more | news.ycombinator.com | 2024-05-12
Hereโs another one - itโs older but has some interesting charts and graphs.
https://arxiv.org/abs/2303.18223
Project mention: Adding a streaming run function to the Assistants API | news.ycombinator.com | 2024-02-07
Project mention: Pg_lakehouse: Query Any Data Lake from Postgres | news.ycombinator.com | 2024-05-13
Project mention: Should I tell my employer about a product that makes my job irrelevant? | /r/cscareerquestions | 2023-07-11Is this of any help?
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! :)
Using C++20 modules, take a look at this project: https://github.com/infiniflow/infinity
Jan incorporates a lightweight, built-in inference server called Nitro. Nitro supports both llama.cpp and NVIDIA's TensorRT-LLM engines. This means many open LLMs in the GGUF format are supported. Jan's Model Hub is designed for easy installation of pre-configured models but it also allows you to install virtually any model from Hugging Face or even your own.
Project mention: Ask HN: How to structure Rust, Axum, and SQLx for clean architecture? | news.ycombinator.com | 2024-05-07You can check out https://github.com/bionic-gpt/bionic-gpt
Basically I put db in it's own crate then crates for controller and another for pages.
The folders for each section of the web application.
Those familiar with the LangChain ecosystem might think of LangChain's sub-project LangServe upon reading this. LangServe's goal is to simplify the deployment of LangChain applications. It can package LangChain apps into API servers and provides default endpoints such as stream, async, docs, and playground. But LangServe alone does not resolve the deployment issues of LangChain applications. It ultimately provides an API server based on FastAPI, akin to frameworks like Flask and Django. How to deploy LangServe applications to the cloud and how to create and manage the dependent backend services remain unanswered by LangServe.
Project mention: OpenAI: Streaming is now available in the Assistants API | news.ycombinator.com | 2024-03-14This was indeed true in the beginning, and I donโt know if this has changed. Inserting messages with Assistant role is crucial for many reasons, such as if you want to implement caching, or otherwise edit/compress a previous assistant response for cost or other reason.
At the time I implemented a work-around in Langroid[1]: since you can only insert a โuserโ role message, prepend the content with ASSISTANT: whenever you want it to be treated as an assistant role. This actually works as expected and I was able to do caching. I explained it in this forum:
https://community.openai.com/t/add-custom-roles-to-messages-...
[1] the Langroid code that adds a message with a given role, using this above โassistant spoofing trickโ:
https://github.com/langroid/langroid/blob/main/langroid/agen...
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).
llms related posts
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Ask HN: How to structure Rust, Axum, and SQLx for clean architecture?
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A Simple Version of Grok 1.5/ GPT-4 Vision from scratch, in one PyTorch file
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LangFun: Object oriented data programs using LLMs
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Large language models (e.g., ChatGPT) as research assistants
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LLM Is a Capable Regressor When Given In-Context Examples
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Building Reliable Systems Out of Unreliable Agents
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Show HN: Burr: An OS Framework for Building and Debugging GenAI Apps Faster
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A note from our sponsor - SaaSHub
www.saashub.com | 17 May 2024
Index
What are some of the best open-source llm projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | generative-ai-for-beginners | 43,780 |
| 2 | Made-With-ML | 36,004 |
| 3 | LLaMA-Factory | 21,791 |
| 4 | open-webui | 20,138 |
| 5 | tabby | 17,534 |
| 6 | chroma | 12,530 |
| 7 | LibreChat | 11,659 |
| 8 | LLMSurvey | 8,967 |
| 9 | OpenNMT-py | 6,599 |
| 10 | gateway | 4,710 |
| 11 | paradedb | 4,053 |
| 12 | AutoGPTQ | 3,843 |
| 13 | gpt-code-ui | 3,494 |
| 14 | lance | 3,328 |
| 15 | hands-on-llms | 2,431 |
| 16 | chat-with-gpt | 2,267 |
| 17 | api-for-open-llm | 2,028 |
| 18 | infinity | 1,793 |
| 19 | cortex | 1,635 |
| 20 | bionic-gpt | 1,616 |
| 21 | langserve | 1,615 |
| 22 | langroid | 1,644 |
| 23 | DemoGPT | 1,582 |
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