From Evaluation to Deployment — The Complete Fine-tuning Guide
Evaluate with Perplexity and KoBEST benchmarks, merge LoRA weights, and deploy with vLLM/Ollama/HuggingFace Spaces.
From Evaluation to Deployment — The Complete Fine-tuning Guide
In Part 1 we covered LoRA fundamentals and ran our first fine-tuning. In Part 2 we tackled QLoRA and Korean dataset construction. Training is done. Now two questions remain:
Series: Part 1: LoRA Theory | Part 2: QLoRA + Korean | Part 3 (this post)
- Did the model actually improve? (Evaluation)
- How do we serve it to users? (Deployment)
The rest of this series is paid
- ✓ The whole “LoRA fine-tuning” series: 3 posts in English, 3 in Korean
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