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Top 23 Python llm Projects
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MetaGPT
🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming
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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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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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Qwen
The official repo of Qwen (通义千问) chat & pretrained large language model proposed by Alibaba Cloud.
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h2ogpt
Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://codellama.h2o.ai/
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OpenLLM
Run any open-source LLMs, such as Llama 2, Mistral, as OpenAI compatible API endpoint, locally and in the cloud.
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shell_gpt
A command-line productivity tool powered by AI large language models like GPT-4, will help you accomplish your tasks faster and more efficiently.
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promptflow
Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.
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deeplake
Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
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txtai
💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
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SaaSHub
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https://github.com/geekan/MetaGPT :
> MetaGPT takes a one line requirement as input and outputs user stories / competitive analysis / requirements / data structures / APIs / documents, etc.
https://news.ycombinator.com/item?id=29141796 ; "Co-Founder Equity Calculator"
"Ask HN: What are your go to SaaS products for startups/MVPs?" (2020) https://news.ycombinator.com/item?id=23535828 ; FounderKit, StackShare
> USA Small Business Administration: "10 steps to start your business." https://www.sba.gov/starting-business/how-start-business/10-...
>> "Startup Incorporation Checklist: How to bootstrap a Delaware C-corp (or S-corp) with employee(s) in California" https://github.com/leonar15/startup-checklist
Project mention: LlamaIndex: A data framework for your LLM applications | news.ycombinator.com | 2024-04-07
Project mention: What’s the Difference Between Fine-tuning, Retraining, and RAG? | dev.to | 2024-04-08Check us out on GitHub.
Project mention: The Era of 1-Bit LLMs: Training_Tips, Code And_FAQ [pdf] | news.ycombinator.com | 2024-03-21
The easiest is to use vllm (https://github.com/vllm-project/vllm) to run it on a Couple of A100's, and you can benchmark this using this library (https://github.com/EleutherAI/lm-evaluation-harness)
I'd like to share with you today the Chinese-Alpaca-Plus-13B-GPTQ model, which is the GPTQ format quantised 4bit models of Yiming Cui's Chinese-LLaMA-Alpaca 13B for GPU reference.
Depends what model you want to train, and how well you want your computer to keep working while you're doing it.
If you're interested in large language models there's a table of vram requirements for fine-tuning at [1] which says you could do the most basic type of fine-tuning on a 7B parameter model with 8GB VRAM.
You'll find that training takes quite a long time, and as a lot of the GPU power is going on training, your computer's responsiveness will suffer - even basic things like scrolling in your web browser or changing tabs uses the GPU, after all.
Spend a bit more and you'll probably have a better time.
[1] https://github.com/hiyouga/LLaMA-Factory?tab=readme-ov-file#...
Project mention: Show HN: Toolkit for LLM Fine-Tuning, Ablating and Testing | news.ycombinator.com | 2024-04-07This is a great project, little bit similar to https://github.com/ludwig-ai/ludwig, but it includes testing capabilities and ablation.
questions regarding the LLM testing aspect: How extensive is the test coverage for LLM use cases, and what is the current state of this project area? Do you offer any guarantees, or is it considered an open-ended problem?
Would love to see more progress toward this area!
Qwen: https://github.com/QwenLM/Qwen
Project mention: Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023? | news.ycombinator.com | 2023-12-24As others have said you want RAG.
The most feature complete implementation I've seen is h2ogpt[0] (not affiliated).
The code is kind of a mess (most of the logic is in an ~8000 line python file) but it supports ingestion of everything from YouTube videos to docx, pdf, etc - either offline or from the web interface. It uses langchain and a ton of additional open source libraries under the hood. It can run directly on Linux, via docker, or with one-click installers for Mac and Windows.
It has various model hosting implementations built in - transformers, exllama, llama.cpp as well as support for model serving frameworks like vLLM, HF TGI, etc or just OpenAI.
You can also define your preferred embedding model along with various other parameters but I've found the out of box defaults to be pretty sane and usable.
13. OpenLLM by BentoML | Github | tutorial
Here’s another one - it’s older but has some interesting charts and graphs.
Project mention: Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023? | news.ycombinator.com | 2023-12-24You can use embedchain[1] to connect various data sources and then get a RAG application running on your local and production very easily. Embedchain is an open source RAG framework and It follows a conventional but configurable approach.
The conventional approach is suitable for software engineer where they may not be less familiar with AI. The configurable approach is suitable for ML engineer where they have sophisticated uses and would want to configure chunking, indexing and retrieval strategies.
Project mention: A suite of tools designed to streamline the development cycle of LLM-based apps | news.ycombinator.com | 2024-04-12
txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.
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).
The latest post mention was on 2024-04-12.
Python llm related posts
- Meta Llama 3
- Yes, Python and Matplotlib can make pretty charts
- LLM Is a Capable Regressor When Given In-Context Examples
- A suite of tools designed to streamline the development cycle of LLM-based apps
- Autonomous LLM agents with human-out-of-loop
- PullRequestBenchmark Challenge: Can AI Replace Your Dev Team?
- Mistral AI Launches New 8x22B Moe Model
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A note from our sponsor - InfluxDB
www.influxdata.com | 19 Apr 2024
Index
What are some of the best open-source llm projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | MetaGPT | 38,728 |
| 2 | llama_index | 30,639 |
| 3 | MindsDB | 21,160 |
| 4 | unilm | 18,262 |
| 5 | vllm | 17,656 |
| 6 | Chinese-LLaMA-Alpaca | 17,140 |
| 7 | mlc-llm | 16,622 |
| 8 | LLaMA-Factory | 16,319 |
| 9 | ChatGLM2-6B | 15,442 |
| 10 | peft | 13,670 |
| 11 | ludwig | 10,778 |
| 12 | Qwen | 10,691 |
| 13 | h2ogpt | 10,327 |
| 14 | gorilla | 9,945 |
| 15 | ml-engineering | 9,680 |
| 16 | OpenLLM | 8,671 |
| 17 | LLMSurvey | 8,515 |
| 18 | embedchain | 8,392 |
| 19 | nebuly | 8,368 |
| 20 | shell_gpt | 8,208 |
| 21 | promptflow | 7,951 |
| 22 | deeplake | 7,673 |
| 23 | txtai | 6,910 |
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