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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

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