I recently hosted a session with Simon Willison for Hamel Husain and Dan Becker’s Mastering LLMs: A Conference For Developers & Data Scientists. Simon’s talk, which I encourage you to watch, was all about
- Using LLMs from the command line with his CLI utility llm,
- Piping and automating with llm,
- Exploring LLM conversations with his tool datasette (llm conversations are automatically logged to a local SQLite database!),
- Working with embeddings from the command line, and
- Building RAG systems from the command line.
You can watch it here and read more about it here:
What I actually want to write about is spurred by Dan Becker asking me why I like using local LLMs and I don’t think I had a satisfactory answer at the time. To explore this topic, I chatted with Claude and ChatGPT, which generated the following list of points based on our conversations:
1. Data Privacy and Security
Control Over Data: Running models locally ensures that sensitive data never leaves your machine, providing higher levels of privacy and security.
Compliance: For industries with strict data regulations, local processing helps in adhering to compliance requirements.
2. Performance and Latency
Reduced Latency: Local models eliminate the need for network requests, resulting in faster response times, especially important for real-time applications.
Consistency: Local processing avoids variability in latency that can occur due to network issues or server-side processing delays.
3. Cost Efficiency
Lower Costs: Avoiding cloud-based service fees can be cost-effective, especially for frequent or large-scale usage.
Predictable Expenses: Costs are more predictable as they mainly involve hardware and occasional software updates rather than ongoing cloud service charges.
4. Customization and Control
Tailored Solutions: Local deployment allows for greater customization of the models and integration with specific workflows.
Full Control: Users have full control over the environment, configurations, and updates, which is beneficial for debugging and optimizing performance.
5. Offline Capabilities
Offline Access: Local models can function without an internet connection, which is crucial for remote locations or scenarios with unreliable connectivity.
Disaster Recovery: Ensures that applications remain operational even during network outages or cloud service disruptions.
6. Learning and Experimentation
Hands-On Experience: Running models locally provides a deeper understanding of how they work, beneficial for learning and research.
Experimentation: Facilitates rapid experimentation and prototyping without the constraints or delays of cloud-based services.
7. Open Source and Community Support
Community Innovation: Many local deployment tools are open source, fostering community contributions and rapid innovation.
Transparency: Open source tools provide transparency in how models are implemented and executed, which can build trust and facilitate customization.
8. Scalability for Development
Development and Testing: Local environments are ideal for development and testing before deploying to production environments, ensuring robustness and reliability.
Resource Optimization: Allows for efficient use of local resources, scaling up as needed without relying on cloud infrastructure.
9. Ethical and Environmental Considerations
Sustainable Practices: Using local resources can be more environmentally friendly by reducing the energy consumption associated with cloud data centers.
Ethical Considerations: Promotes ethical use of AI by keeping sensitive data within trusted boundaries and reducing dependency on centralized services.
10. Autonomy and Independence
Independence from Providers: Reduces dependency on cloud service providers, giving users more autonomy and reducing the risk of vendor lock-in.
Resilience: Enhances resilience against changes in cloud service policies, pricing, or availability.
Do any of these resonate with you? I’d be excited to hear which ones on twitter and/or LinkedIn.
Getting Started with Local LLMs
- Ollama is a great way to get started locally with SOTA models, such as Llama 3, Phi 3, Mistral, and Gemma;
- Simon’s LLM cli utility allows you to explore LLMs of all kinds from the command and has all the fun mentioned above: can be piped according to unix-like philosophy, logs to local sqlite db, can be explored interactively using datasette, can work with embeddings to build RAG apps, and more!
- LlamaFile is a great project from Mozilla and Justine Tunney that is “an open source initiative that collapses all the complexity of a full-stack LLM chatbot down to a single file that runs on six operating systems”, has a cool front-end GUI, and you can get up and running with lots of models, including multimodal models such as LLaVa, immediately;
- LM Studio is one of the more advanced GUIs I’ve seen to interact with local LLMs: you can discover new models on the homepage, browse and download lots of models from HuggingFace, easily chat with models, and even chat with several simultaneously to compare responses, latency, and more.
- Oobagooba’s text generation webUI, which allows you to interact with local models (and others) through a webUI – lots of fun stuff for fine-tuning, chatting and so on.
I use all of these tools a bunch and hope to demo some of them soon and perhaps do some write-ups so let me know on twitter and/or LinkedIn, if you’d find this useful, and I’ll likely get to it sooner!