In this video, I'll show you the easiest, simplest and fastest way to fine tune llama-v2 on your local machine for a custom dataset! You can also use the tutorial to train/finetune any other Large Language Model (LLM). In this tutorial, we will be using autotrain-advanced.

AutoTrain Advanced github repo: [ Ссылка ]

Steps:
Install autotrain-advanced using pip:
- pip install autotrain-advanced

Setup (optional, required on google colab):
- autotrain setup --update-torch

Train:
autotrain llm --train --project_name my-llm --model meta-llama/Llama-2-7b-hf --data_path . --use_peft --use_int4 --learning_rate 2e-4 --train_batch_size 12 --num_train_epochs 3 --trainer sft

If you are on free version of colab, use this model instead: [ Ссылка ]. This is a smaller sharded version of llama-2-7b-hf by meta.

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