Large language models (LLMs) like ChatGPT have taken the AI world by storm. Their advanced natural language abilities are powering a new generation of conversational apps and services. However, their massive computational requirements have so far limited their use to big tech companies running large server farms.
But what if you could tap into the power of LLMs right from your personal laptop or desktop? New open-source tools like Adept are making this possible by optimizing models to run efficiently on consumer hardware. In this guide, we’ll cover how to locally deploy powerful LLMs with just a few simple steps using Adept.
What is Adept?
Adept is an open-source project from Anthropic focused on efficiently “distilling” large models into smaller versions that retain performance while slashing resource usage. For example, their Claude model delivers over 90% of ChatGPT’s capabilities but with 3,000x fewer parameters.
Adept streamlines deploying these condensed models locally with Docker containers. Once set up, you can interface with the LLMs entirely on your own device instead of relying on external APIs. This allows full customization for your specific needs.
Why Run LLMs Locally?
Locally running LLMs with Adept offers several key benefits:
- Privacy – No sending user data to external servers
- Customization – Fine-tune models for specialized domains
- Ownership – Full control without relying on vendors
- Cost – Avoid ongoing API subscription fees
- Convenience – Integrate into local apps and projects
Of course, specialized hardware like high-powered GPUs can enable running even larger models. But Adept unlocks substantial LLM capacity even on typical consumer laptops.
Deploying Adept LLMs on Linux/MacOS
With just a few terminal commands, you can have a powerful LLM running on your local machine via Adept:
-
Install Docker if you don’t already have it
-
Pull the Claude Docker container:
docker pull anthropic/claude
- Start Claude interactively:
docker run -it --rm anthropic/claude
The model will load in and you’ll enter an input prompt where you can start querying Claude!
Under the hood, Claude is consuming a tiny fraction of resources compared to equivalently capable LLMs. Even on a laptop, you likely won’t notice any meaningful system slowdowns.
Alternative Tools
Adept makes locally running condensed LLMs straightforward. But there are other options as well:
- TensorFlow Lite – Shrink models for mobile/edge devices
- ONNX Runtime – Accelerate model inference on laptops
- BitFIT – Compress LLMs like GPT-3 for local use
- DynaText – TinyML models optimized for apps/products
Each solution makes different tradeoffs between model size, speed, and accuracy. Mix and match to find the best fit for your specific needs.
Next Steps
Thanks to ingenious compression techniques and optimization tools like Adept, once unfathomable LLMs can now be productively deployed on ordinary laptops.
Experiment with Claude or another condenser model as a springboard to develop specialized local AI capabilities catered to your use cases. Then scale up with more GPU power as needed over time. The world of powerful yet efficient LLMs is now open to all via these versatile open-source projects!