Скачать или смотреть LLM Fine-Tuning Crash Course: Finetune model on PDFs, Instruction FT, Preference Training (DPO/RLHF)

This single video gives you the complete roadmap to transform a base LLM into a domain-specific, instruction-following, preference-aligned AI assistant using Hugging Face Transformers + PEFT + TRL.

What You Will Learn
1️⃣ Domain-Specific Fine-Tuning on Your PDFs
How to extract clean text from PDFs
Chunking & preprocessing
Preparing domain datasets (json, jsonl)
Training LLMs on your organization’s documents

2️⃣ Instruction Fine-Tuning (SFT)
What is SFT & why it is required
Prompt → Response formats
Creating your own instruction dataset
Fine-tuning with LoRA/QLoRA
Evaluation & checkpoint best practices

3️⃣ Preference Alignment (DPO, RLHF, RLAIF)
Why LLMs need preference alignment
Chosen vs rejected datasets
DPO training intuition + formula + implementation
RLHF vs RLAIF
How companies align LLMs (OpenAI, Anthropic, Meta)

🔧 Technologies, Tools & Frameworks Used
Hugging Face Transformers
PEFT (LoRA, QLoRA)
TRL (DPO / RLHF)
BitsandBytes
LLaMA / Mistral / Qwen models
Python, JSONL, PyTorch

By the end of this video, you will:
Build a full dataset pipeline from PDFs to clean training data
Prepare instruction datasets for SFT
Perform Domain-Specific Fine-Tuning
Perform Preference-Based Training (DPO)
Export & use your final fine-tuned model in real applications

Material & Resources:
https://github.com/sunnysavita10/Comp...

https://github.com/sunnysavita10/Comp...

https://github.com/sunnysavita10/Comp...

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Connect with me on social media
LinkedIn:   / sunny-savita  
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GitHub: https://github.com/sunnysavita10
Telegram: https://t.me/aimldlds