Knowledge base vs fine-tune LLMs // pros & cons

2 min readJul 9, 2023

Fine-tuning (HF, OpenAI) requires more resources (time, data, compute, etc.) and money, not suitable for constant real-time updates. It’s good for big amounts of data, fundamental, long-term knowledge.

Trainig data quality for fine-tuning makes the final result. Other models can be used, ChatGPT for example.

A knowledge base is a great way to organize constantly updated data, but its usage is limited by LLM max prompt length.

Knowledge base and fine-tune can be used together to get max results. Semantic (vector) databases, knowledge graphs, etc. are themselves suitable for collecting and processing knowledge.

Notebooks for finetuning and inference of Llama-2 and LLMs

- bnb-4bit-training-with-inference

- Llama 2 Fine-Tuning using QLora

- Fine-tune Llama 2 in Google Colab

- llama-2–70b-chat-agent

- text-generation-inference

- text-generation-webui

- llama2-webui

- llama_cpu_interface




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