In this video I will show you how to fine-tune the Alpaca model for any language. And it only costs $3! How did I figure this out? Watch the whole video to understand. I'll show you how to translate the cleaned Alpaca dataset. We will then use the translated dataset to fine-tune the Alpaca model (not the LLaMA model) for our desired language. For this, we will either use DeepL or ChatGPT. Also, I will show you how to evaluate the quality of your fine-tuned model. Last but not least you will learn how you can interact with your fine-tuned model in a UI. As always, if you have any questions, don't hesitate to reach out. Enjoy! 🙂
My Medium Article for This Video:
Medium Article Showing the Evaluation Results:
GitHub Repository for This Video:
00:00:00 Intro
00:01:52 Calculating Estimated Costs
00:09:03 Decision Making
00:10:03 Creating A Subset Dataset
00:12:00 Dataset Translation
00:17:36 Fine-Tuning the Alpaca Model
00:26:15 Model Inference
00:27:18 How Much Training Data Do We Need?
00:31:24 Evaluation
00:37:32 Outro
References:
Self-Instruct Repository:
Alpaca Blog Post: …
Alpaca Repository:
Alpaca-LoRA:
AlpacaDataCleaned Repository:
GPT-3.5 Documentation:
OpenAI API Introduction:
DeepL Pricing:
LLaMA Paper:
Is ChatGPT A Good Translator? Paper:
OpenAI Pricing:
vast.ai:
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