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A beginners guide to fine tuning LLM using LoRA
Discover how to create a synthetic dataset, select the right metrics for evaluation, and fine-tune your model using LoRA for a narrow scenario. Plus, learn how to serve your model efficiently using LLaMa.cpp on Mac/Linux.
This was done so I don't pigeon hole my data to only complete sentences with only grammatical issues i.e. adding diversity to my dataset so it can work for wide set of scenarios. BLEU: It attempts to evaluate the quality of the machine generated text with the ground truth (our target correct) using n-gram overlap. ROUGE: ROUGE-L attempts to measure the longest common subsequence between generated text and ground truth while ROUGE-N uses an N-gram overlap approach.
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