Knowledge base vs fine-tune LLMs // pros & cons
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
https://lnkd.in/dtiky_k8- Llama 2 Fine-Tuning using QLora
https://lnkd.in/dEd4xTAR- Fine-tune Llama 2 in Google Colab
https://lnkd.in/dHi2JHcf- llama-2–70b-chat-agent
https://lnkd.in/dWjSdqRB- text-generation-inference
https://lnkd.in/dvmMGhD6- text-generation-webui
https://lnkd.in/dQ_NVNkP- llama2-webui
https://lnkd.in/dkY3c_Pc- llama_cpu_interface
https://lnkd.in/dyq2ft-q
