Mindscend fine-tunes compact AI model on MacBook to improve chatbot, halves prompt length

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Mindscend engineers fine-tuned the Qwen2.5-1.5B-Instruct model using LoRA on a MacBook to improve their company's chatbot. The model had been parroting identical responses to different user queries due to its small size and long system prompts. The team initially implemented prompt engineering and a secondary classifier to filter off-topic requests, but this approach was slow and brittle. They then created a training dataset using a larger teacher model to teach desired behaviors, such as concise responses and refusal of off-topic questions. The fine-tuned model now runs on a CPU-only AWS server with a significantly shorter, facts-only system prompt.

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