How to Fine-Tune your LLM on Tweets! (large language models for investing)

Transforming a generalist Large Language Model into a specialized financial expert using just a CPU! In this video, we walk you through the fine-tuning process with a hands-on example in financial sentiment analysis. Learn how to harness the power of fine-tuning to analyze financial tweets and make informed investment decisions. Follow the link for the full code and a step-by-step guide! 💼📈🚀
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Chapters:
0:00 Give a thumbs-up to the video and subscribe for more! If you read this, I wish you a fantastic day!
0:36 What is fine-tuning?
1:46 Loading the dataset using DeepLake.
3:13 Structure the training prompt.
3:28 Load tokenizer and create dataset object.
4:27 Initialize the Lora config and training hyperparameters.
6:02 Download and load the model into memory.
6:42 Casting specific layers within the network to complete 32-bit precision.
7:00 Creating the trainer and launching the training process.
7:22 Merging the trained LoRA adapters to the base model.
7:47 Inference time!
8:22 Conclusion.
#ai #languagemodels #llm

[@nikoladjordjevic4477]

Important notice: fine tunning improves performance on data-set but doesn't "teach" LLM. So it works the best with retrieval but it's not replacement for those

[@WhatsAI]

Indeed!