127,364 views since Nov 26, 2023

In this video, I will show you the easiest way to fine-tune the Llama-2 model on your own data using the auto train-advanced package from HuggingFace.

Steps to follow:
---installation of packages:
!pip install autotrain-advanced
!pip install huggingface_hub

!autotrain setup --update-torch (optional - needed for Google Colab)

---- HuggingFace credentials:
from huggingface_hub import notebook_login
notebook_login()

--- single line command!
!autotrain llm --train --project_name your_project_name --model TinyPixel/Llama-2-7B-bf16-sharded --data_path your_data_set --use_peft --use_int4 --learning_rate 2e-4 --train_batch_size 2 --num_train_epochs 3 --trainer sft --model_max_length 2048 --push_to_hub --repo_id your_repo_id -

⏱️ Timestamps
Intro: [00:00]
Auto-train & installation: [00:17]
Fine-tuning - One Liner: [02:00]
Data Set Format: [05:30]
Training settings: [08:26]

LINKS:
autotrain: https://huggingface.co/autotrain
autotrain GitHub: https://github.com/huggingface/autotr...

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