Introduction
Pre-Training
Step 1: Download and preprocess the internet
Step 2: Tokenization
Step 3: Neural network training
Step 4: Inference
Base model
Post-Training: Supervised Finetuning
Conversations
Hallucinations
Knowledge of Self
Models need tokens to think
Things the model cannot do well
Post-Training: Reinforcement Learning
Reinforcement learning
DeepSeek-R1
AlphaGo
Reinforcement learning from human feedback (RLHF)
Preview of things to come
Keeping track of LLMs
Where to find LLMs
If you want to leverage and experiment with LLM, you can find it on these platforms! 🚀
Popular large-scale AI models can be accessed directly from each company's official website .
✅ Representative LLM provider sites:
They are cloud-based and can be accessed directly from your website!
🔓 Open weight models that are free to download and use can be found on multiple platforms.
✅ Open source LLM provider sites:
Together AI → Various open source LLMs can be run 💡
Hugging Face → Provides numerous open source models
Hyperbolic → Llama 3.1 Base model provided
🛠 Inference(Inference) platform allows you to directly select and test multiple models!
You can also run LLM directly on your computer!
Especially if you use the lightweight or low-precision models, it can even run on your personal PC 🎯
✅ How to run local LLM:
1️⃣ LM Studio → Download link
💻 Run AI models directly locally
📌 Supports Mac and Windows
The UI/UX is a bit difficult, but once you get used to it, it's a powerful tool.
You can choose and run various models.
2️⃣ Ollama → Download link
🎯 Run AI models locally with simple commands
Supports the latest models including Llama 3 and DeepSeek
Performance is especially good on Mac
💡 Local running tips:
Use small model (lightweight version) → avoid memory shortage
Low Precision settings (FP8, INT4, etc.) → Runs on smaller PCs too