Hosting a Large Language Model Locally for QA Tasks
Introduction
The biggest nightmare of a QA when it comes to using an AI for client deliverables is data leak. As we are aware, Once data is shared with many public generative AI models—especially those on third-party platforms—it may be stored and used for future training, posing data privacy and compliance risks, unless the user opts out of personalization and other terms of data usage. With industry-specific robust compliance frameworks like HIPAA, engineers are expected to avoid using AI in day-to-day tasks.
Solution
One simple solution to this could be hosting an LLM locally and interacting with it as all the interactions stay within the local machine and the interactions can be reset or deleted in a snap of finger like Thanos.
Steps to follow:
- Install LM studio
- Launch LM studio and check for updates
- Click on ‘Discover’ button on the left pane
- Select a model based on the system specifications. An example of a lightweight model ‘DeepSeek-R1-Distill-Qwen-7B-GGUF/DeepSeek-R1-Distill-Qwen-7B-Q4_K_M.gguf’, which is 4.68 GB
- Click download and wait for it to complete
- Now, click on the ‘Chat’ icon on the left pane
- Click on the ‘Select a model’ button at the top of LM studio window
- Select the downloaded model
- Voila! You are ready to start your first interaction with a local LLM
Note: Local LLM could be slower and less precise than online real-time models
Please refer to the video below
Conclusion
This simple method could help us navigate the barricade of data and security compliance norms without any harm and democratise the usage of LLM by engineers to assist with day-to-day task and impact productivity positively. Local LLMs can also be fine-tuned to meet specific needs thereby cracking the limitations set by online hosted AI models.
