LLM-VM lets developers easily build apps powered by LLMs without managing infra. Just provide data/APIs, and it handles prompt engineering, fine-tuning, load balancing between models, and more!
LLM-VM is an open source platform that dramatically simplifies building applications powered by large language models (LLMs). 🤖
It acts as a virtual machine sitting between your code and LLMs, taking care of the heavy lifting so you can focus on your app's business logic.
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✅ Natural Language Compilation - Translates conversational instructions into dynamic LLM prompts and commands. 💬
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✅ Automatic Fine-Tuning - Iteratively improves data and parameters for your models and use cases. 🧑🔧
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✅ Load Balancing - Splits requests across multiple models and providers. 📊
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✅ Tool Orchestration - Coordinates data sources, APIs, code hooks and more into LLM workflows. ⚙️
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✅ Optimization - State-of-the-art optimizations like batching and quantization customized per model. ⚡️
The goal is to make leveraging LLMs reliable and scalable while abstracting away the complexity. This means faster iteration and cheaper, more robust applications!
Whether you're a solo developer or enterprise team, LLM-VM is the fastest way to build the next generation of language-powered products. 🚀
- 🛠 Simplicity - Abstracts away infrastructure so engineers can focus on product logic and capabilities using LLMs versus managing complexity.
- 📦 Modularity - Swap out models, data sources, and APIs with no code changes. Great for testing ideas.
- ⚡️ Optimization - State-of-the-art batching, quantization, etc., which would be costly to build custom means better performance.
- 💪 Reliability - Handles load balancing across models & providers, auto fine-tuning for consistency, and failover for robustness.
- 🔌 Extensibility - Add agents to connect new data sources and services with just descriptions for easy extensibility.
In summary, LLM-VM handles the undifferentiated heavy lifting so engineers can rapidly build and iterate language-based products. It saves time and cost while providing guardrails and best practices for success with LLMs.
- 👷🏽♀️ Builders: Abhigya Sodani, Matthew Mirman, Carter Schonwald
- 👩🏽💼 Builders on LinkedIn: https://www.linkedin.com/in/abhigya-sodani-405918160/, https://www.linkedin.com/in/matthewmirman/, https://www.linkedin.com/in/carter-schonwald-aa178132/
- 👩🏽🏭 Builders on X: https://twitter.com/mmirman, https://twitter.com/OdedeVik
- 👩🏽💻 Contributors: 39
- 💫 GitHub Stars: 265
- 🍴 Forks: 109
- 👁️ Watch: 8
- 🪪 License: MIT
- 🔗 Links: Below 👇🏽
- GitHub Repository: https://github.com/anarchy-ai/LLM-VM
- Official Website: https://anarchy.ai/
- LinkedIn Page: https://www.linkedin.com/company/anarchy-ai/
- X Page: https://twitter.com/anarchy_ai_inc
- Profile in The AI Engineer: https://github.com/theaiengineer/awesome-opensource-ai-engineering/blob/main/libraries/llm-vm.md
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