>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/autotrain-advanced

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All Interesting Videos:
Everything LangChain: https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr />
Everything LLM:
https://youtube.com/playlist?list=PLVEEucA9MYhNF5-zeb4Iw2Nl1OKTH-Txw />
Everything Midjourney:
https://youtube.com/playlist?list=PLVEEucA9MYhMdrdHZtFeEebl20LPkaSmw />
AI Image Generation:
https://youtube.com/playlist?list=PLVEEucA9MYhPVgYazU5hx6emMXtargd4z />

#llama #finetune #llama2 #artificialintelligence #tutorial #stepbystep #llm #largelanguagemodels #largelanguagemodel

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