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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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chatgpt-on-wechat
基于大模型搭建的聊天机器人,同时支持 微信 公众号、企业微信应用、飞书、钉钉 等接入,可选择GPT3.5/GPT-4o/GPT4.0/ Claude/文心一言/讯飞星火/通义千问/ Gemini/GLM-4/Claude/Kimi/LinkAI,能处理文本、语音和图片,访问操作系统和互联网,支持基于自有知识库进行定制企业智能客服。
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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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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pandas-ai
Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.
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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 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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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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: AI leaderboards are no longer useful. It's time to switch to Pareto curves | news.ycombinator.com | 2024-04-30I guess the root cause of my claim is that OpenAI won't tell us whether or not GPT-3.5 is an MoE model, and I assumed it wasn't. Since GPT-3.5 is clearly nondeterministic at temp=0, I believed the nondeterminism was due to FPU stuff, and this effect was amplified with GPT-4's MoE. But if GPT-3.5 is also MoE then that's just wrong.
What makes this especially tricky is that small models are truly 100% deterministic at temp=0 because the relative likelihoods are too coarse for FPU issues to be a factor. I had thought 3.5 was big enough that some of its token probabilities were too fine-grained for the FPU. But that's probably wrong.
On the other hand, it's not just GPT, there are currently floating-point difficulties in vllm which significantly affect the determinism of any model run on it: https://github.com/vllm-project/vllm/issues/966 Note that a suggested fix is upcasting to float32. So it's possible that GPT-3.5 is using an especially low-precision float and introducing nondeterminism by saving money on compute costs.
Sadly I do not have the money[1] to actually run a test to falsify any of this. It seems like this would be a good little research project.
[1] Or the time, or the motivation :) But this stuff is expensive.
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.
Qwen: https://github.com/QwenLM/Qwen
Project mention: PandasAI is great but is there a more general library? | news.ycombinator.com | 2023-08-23
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!
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.
[0] - https://github.com/h2oai/h2ogpt
Project mention: Raft: Sailing Llama towards better domain-specific RAG | news.ycombinator.com | 2024-05-09Retrieval-Augmented Fine-Tuning is a really promising technique.
FTA:
> Tianjun and Shishir were looking to improve these deficiencies of RAG. They hypothesized that a student who studies the textbooks before the open-book exam would be more likely to perform better than a student who references the textbook only during the exam. Translating that back to LLMs, if a model “studied” the documents beforehand, could that improve its RAG performance?
Incidentally, the team who wrote the paper released some nice code to generate domain-specific fine-tuning datasets: https://github.com/ShishirPatil/gorilla/tree/main/raft
Here’s another one - it’s older but has some interesting charts and graphs.
https://arxiv.org/abs/2303.18223
13. OpenLLM by BentoML | Github | tutorial
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.
[1]: https://github.com/embedchain/embedchain
https://github.com/TheR1D/shell_gpt?tab=readme-ov-file#shell...
Project mention: Ask HN: Most efficient way to fine-tune an LLM in 2024? | news.ycombinator.com | 2024-04-04Gemma 7b is 2.4x faster than HF + FA2.
Check out https://github.com/unslothai/unsloth for full benchmarks!
Python llm related posts
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GPT-4o: Learn how to Implement a RAG on the new model, step-by-step!
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OSS framework for voice first multimodal assistants
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FLaNK-AIM Weekly 13 May 2024
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A note from our sponsor - InfluxDB
www.influxdata.com | 17 May 2024
Index
What are some of the best open-source llm projects in Python? This list will help you:
Project | Stars | |
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1 | MetaGPT | 39,707 |
2 | llama_index | 31,628 |
3 | chatgpt-on-wechat | 25,427 |
4 | MindsDB | 21,424 |
5 | LLaMA-Factory | 21,791 |
6 | vllm | 19,344 |
7 | unilm | 18,548 |
8 | Chinese-LLaMA-Alpaca | 17,539 |
9 | mlc-llm | 17,150 |
10 | ChatGLM2-6B | 15,534 |
11 | peft | 14,083 |
12 | Qwen | 11,430 |
13 | pandas-ai | 11,140 |
14 | ludwig | 10,859 |
15 | h2ogpt | 10,686 |
16 | gorilla | 10,276 |
17 | ml-engineering | 9,890 |
18 | LLMSurvey | 8,967 |
19 | OpenLLM | 8,920 |
20 | embedchain | 8,576 |
21 | nebuly | 8,363 |
22 | shell_gpt | 8,391 |
23 | unsloth | 9,703 |
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