41 lines
2.3 KiB
Markdown
41 lines
2.3 KiB
Markdown
<h1 align="center">Mini LLM Flow - LLM Framework in 100 Lines</h1>
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[](https://zachary62.github.io/miniLLMFlow/)
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A [100-line](minillmflow/__init__.py) minimalist LLM framework for agents, task decomposition, RAG, etc.
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- Install via ```pip install minillmflow```, or just copy the [source](minillmflow/__init__.py) (only 100 lines)
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- **Pro tip:** Build LLM apps with LLMs assistants (ChatGPT, Claude, etc.)
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- Chat with [Mini LLM Flow Assistant](https://chatgpt.com/g/g-677464af36588191b9eba4901946557b-mini-llm-flow-assistant)
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- Use your own LLMs and provide contexts via [this prompt](assets/prompt)
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Documentation: https://minillmflow.github.io/miniLLMFlow/
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## Why Mini LLM Flow?
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Mini LLM Flow is designed to be **the framework used by LLMs**. In the future, LLM projects will *self-programmed* by LLMs themselves: Users specify requirements, and LLMs will design, build, and maintain. Current LLMs are:
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1. **👍 Good at Low-level Details:** LLMs can handle *LLM wrappers, tools, and prompts*, which don't require any framework. Current frameworks are often over-engineered, making them difficult for humans (and LLMs) to understand.
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2. **👎 Bad at High-level Paradigms:** While paradigms like *MapReduce, task decomposition, and agents* are powerful, LLMs still struggle to design them elegantly. These high-level concepts should be emphasized in frameworks.
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The ideal framework for LLMs should (1) **strip away low-level implementation details**, and (2) **keep high-level paradigms** to program against. Hence, we provide this minimal (100-line) framework that allows LLMs to focus on what matters.
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Mini LLM Flow is also a **great learning resource**, as current frameworks abstract too much away.
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<div align="center">
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<img src="/assets/minillmflow.jpg" width="400"/>
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</div>
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## Tutorial
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- Documentation: https://minillmflow.github.io/miniLLMFlow/
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- Beginner Tutorial: [Text summarization for Paul Graham Essay + QA agent](https://colab.research.google.com/github/zachary62/miniLLMFlow/blob/main/cookbook/demo.ipynb)
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- Have questions for this tutorial? Ask LLM assistants through [this prompt](https://chatgpt.com/share/676f16d2-7064-8000-b9d7-f6874346a6b5)
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