ML Engineers: NVIDIA has released a guide for beginners on fine-tuning LLMs using Unsloth.

The guide covers:

- training methods: LoRA, FFT, RL
- when and why to do fine-tuning, real use cases
- how much data and VRAM are required
- how to train locally on DGX Spark, RTX graphics cards, and more

Guide: https://blogs.nvidia.com/blog/rtx-ai-garage-fine-tuning-unsloth-dgx-spark/

👉 https://t.iss.one/DataScienceT

𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐪𝐮𝐢𝐜𝐤 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐭𝐨𝐩 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 🔥👇⁣⁣
⁣⁣
✅ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘢 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳 𝘢𝘯𝘥 𝘸𝘩𝘺 𝘸𝘢𝘴 𝘪𝘵 𝘪𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘦𝘥?⁣⁣
𝘐𝘵 𝘴𝘰𝘭𝘷𝘦𝘥 𝘵𝘩𝘦 𝘭𝘪𝘮𝘪𝘵𝘢𝘵𝘪𝘰𝘯𝘴 𝘰𝘧 𝘙𝘕𝘕𝘴 & 𝘓𝘚𝘛𝘔𝘴 𝘣𝘺 𝘶𝘴𝘪𝘯𝘨 𝘴𝘦𝘭𝘧-𝘢𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯, 𝘦𝘯𝘢𝘣𝘭𝘪𝘯𝘨 𝘱𝘢𝘳𝘢𝘭𝘭𝘦𝘭 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨 𝘢𝘯𝘥 𝘤𝘢𝘱𝘵𝘶𝘳𝘪𝘯𝘨 𝘭𝘰𝘯𝘨-𝘳𝘢𝘯𝘨𝘦 𝘥𝘦𝘱𝘦𝘯𝘥𝘦𝘯𝘤𝘪𝘦𝘴 𝘭𝘪𝘬𝘦 𝘯𝘦𝘷𝘦𝘳 𝘣𝘦𝘧𝘰𝘳𝘦!⁣⁣
⁣⁣
✅ 𝘚𝘦𝘭𝘧-𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 – 𝘛𝘩𝘦 𝘮𝘢𝘨𝘪𝘤 𝘣𝘦𝘩𝘪𝘯𝘥 𝘪𝘵⁣⁣
𝘌𝘷𝘦𝘳𝘺 𝘸𝘰𝘳𝘥 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘴 𝘪𝘵𝘴 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘪𝘯 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯 𝘵𝘰 𝘰𝘵𝘩𝘦𝘳𝘴—𝘮𝘢𝘬𝘪𝘯𝘨 𝘦𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨𝘴 𝘴𝘮𝘢𝘳𝘵𝘦𝘳 𝘢𝘯𝘥 𝘮𝘰𝘥𝘦𝘭𝘴 𝘮𝘰𝘳𝘦 𝘤𝘰𝘯𝘵𝘦𝘹𝘵-𝘢𝘸𝘢𝘳𝘦.⁣⁣
⁣⁣
✅ 𝘔𝘶𝘭𝘵𝘪-𝘏𝘦𝘢𝘥 𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 – 𝘚𝘦𝘦𝘪𝘯𝘨 𝘧𝘳𝘰𝘮 𝘮𝘶𝘭𝘵𝘪𝘱𝘭𝘦 𝘢𝘯𝘨𝘭𝘦𝘴⁣⁣
𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘢𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘩𝘦𝘢𝘥𝘴 𝘧𝘰𝘤𝘶𝘴 𝘰𝘯 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯𝘴𝘩𝘪𝘱𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘥𝘢𝘵𝘢. 𝘐𝘵’𝘴 𝘭𝘪𝘬𝘦 𝘩𝘢𝘷𝘪𝘯𝘨 𝘮𝘶𝘭𝘵𝘪𝘱𝘭𝘦 𝘦𝘹𝘱𝘦𝘳𝘵𝘴 𝘢𝘯𝘢𝘭𝘺𝘻𝘦 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯!⁣⁣
⁣⁣
✅ 𝘗𝘰𝘴𝘪𝘵𝘪𝘰𝘯𝘢𝘭 𝘌𝘯𝘤𝘰𝘥𝘪𝘯𝘨 – 𝘛𝘦𝘢𝘤𝘩𝘪𝘯𝘨 𝘵𝘩𝘦 𝘮𝘰𝘥𝘦𝘭 𝘰𝘳𝘥𝘦𝘳 𝘮𝘢𝘵𝘵𝘦𝘳𝘴⁣⁣
𝘚𝘪𝘯𝘤𝘦 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳𝘴 𝘥𝘰𝘯’𝘵 𝘱𝘳𝘰𝘤𝘦𝘴𝘴 𝘥𝘢𝘵𝘢 𝘴𝘦𝘲𝘶𝘦𝘯𝘵𝘪𝘢𝘭𝘭𝘺, 𝘵𝘩𝘪𝘴 𝘵𝘳𝘪𝘤𝘬 𝘦𝘯𝘴𝘶𝘳𝘦𝘴 𝘵𝘩𝘦𝘺 “𝘬𝘯𝘰𝘸” 𝘵𝘩𝘦 𝘱𝘰𝘴𝘪𝘵𝘪𝘰𝘯 𝘰𝘧 𝘦𝘢𝘤𝘩 𝘵𝘰𝘬𝘦𝘯.⁣⁣
⁣⁣
✅ 𝘓𝘢𝘺𝘦𝘳 𝘕𝘰𝘳𝘮𝘢𝘭𝘪𝘻𝘢𝘵𝘪𝘰𝘯 – 𝘚𝘵𝘢𝘣𝘪𝘭𝘪𝘻𝘪𝘯𝘨 𝘵𝘩𝘦 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘱𝘳𝘰𝘤𝘦𝘴𝘴⁣⁣
𝘐𝘵 𝘴𝘱𝘦𝘦𝘥𝘴 𝘶𝘱 𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘢𝘯𝘥 𝘢𝘷𝘰𝘪𝘥𝘴 𝘷𝘢𝘯𝘪𝘴𝘩𝘪𝘯𝘨 𝘨𝘳𝘢𝘥𝘪𝘦𝘯𝘵𝘴, 𝘭𝘦𝘵𝘵𝘪𝘯𝘨 𝘮𝘰𝘥𝘦𝘭𝘴 𝘨𝘰 𝘥𝘦𝘦𝘱𝘦𝘳 𝘢𝘯𝘥 𝘭𝘦𝘢𝘳𝘯 𝘣𝘦𝘵𝘵𝘦𝘳.⁣⁣

👉 @codeprogrammer

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