Finetuning
Large
Language Models
Fine-tuning LLMs adjusts their parameters for specific tasks, improving accuracy and relevance, much like adding specialized skills to a student's existing knowledge.
Open vs. Closed Source Fine-Tuning
🌐
🔄
🚀
🎛️
🔒
❓
Open source offers control, allowing model weight changes for tailored applications.
Weight adjustments in open source models impact performance, offering flexibility.
Large language models (GPT-3, LLama, Bloom) undergo pretraining on unstructured data.
Fine-tuning optimizes neural network performance, a step beyond the base model.
Closed-source premium models (ChatGPT, GPT-4, Claude, Google Gemini) lack full finetuning access.
Why the restriction? Massive size—GPT-3 with 150B parameters vs. LLama 2 with 7B, 13B, and 65B.
Maximizing Efficiency and Accuracy:
Why Fine-Tuning Your Model is Essential
Customize for Specific Use Cases
Enhance Response Quality
Lower Token Costs
Minimize Latencies
Which Models Support the Finetuning Process?
Open Source Models
🌬️ Mistral 7B
🌀 Mixtral 8x7B MOE
🦙 Llama 2 7B, 13B, 65B
🦅 Falcon 40B
💬 OpenChat 7B
Closed Source Models
🔒 GPT 3
🚀 GPT 3.5 Turbo
🌐 Google Gemini Pro