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GPT Architecture Family

⬢ 層級 3技術
高
薪資影響
4 個月
學習時長
困難
難度
11
職業
一覽

GPT (Generative Pre-trained Transformer) family: GPT-1 (decoder-only, 2018), GPT-2 (larger, 2019), GPT-3 (175B params, few-shot learning, 2020), GPT-4 (multimodal, 2023). Understand transformer architecture, attention mechanisms, scaling laws, fine-tuning. Mastery takes 4-6 months. Senior practitioners earn 30-50% premium because GPT expertise is rare and high-value. Skill is fundamental to modern AI.

什麼是 GPT Architecture Family

GPT (Generative Pre-trained Transformer) family includes GPT-1 through GPT-4 and their variants. All use the transformer architecture (attention-based) for language modeling. Trained on massive text corpora, they learn to predict next tokens. Once trained, they can be used for: text generation, translation, summarization, question-answering, code generation, reasoning. Understanding GPT family = understanding modern AI. From GPT-1 (2018) to GPT-4 (2023), each iteration demonstrates the power of scale (more data, more compute, larger models).

🔧 工具與生態
Hugging Face TransformersPyTorchOpenAI APIAnthropic SDKONNX RuntimeTensorFlowLangChainPrompt engineering tools

📋 開始之前

Python

💰 各地區薪資

地區初級中級高級
USA$100,000$180,000$300,000
UK£75,000£135,000£230,000
EU€80,000€145,000€240,000
CANADACA$105,000CA$185,000CA$310,000

❓ 常見問題

What's the transformer architecture and why is it revolutionary?
Transformer uses attention instead of recurrence. Attention = each token attends to all other tokens (parallel processing, no sequential bottleneck). Revolutionary because: parallelizable (train on large datasets), learns long-range dependencies, scales with data/compute.
What's the difference between GPT-1, GPT-2, GPT-3, and GPT-4?
GPT-1 (117M params): basic language model. GPT-2 (1.5B): larger, better generation. GPT-3 (175B): few-shot learning (learns from examples in prompt), good at many tasks. GPT-4 (closed, multimodal): image + text, better reasoning. Each is vastly more capable due to scale + compute.
Can I fine-tune GPT models?
GPT-3: limited fine-tuning via API. GPT-4: no fine-tuning (yet). You can fine-tune open models (Llama, Mistral) or use smaller models (DistilBERT). Or use prompt engineering (few-shot) with large models.
What are scaling laws and why do they matter?
Scaling laws: loss improves predictably with more data, more compute, larger models. Empirically: loss ~ (data^a) * (compute^b). Matters because: tells us when to train larger models vs collect more data, predicts performance without training.
How does in-context learning work?
GPT-3 given a prompt with examples (1-shot, few-shot learning), solves new tasks without training. Mechanism not fully understood but appears related to training data memorization + generalization. Scale matters: GPT-2 can't do this, GPT-3 can.
What's prompt engineering and why is it important?
Prompt engineering = crafting the input to get desired output from LLM. Examples: system prompt (role), few-shot examples, chain-of-thought. Critical because same model, different prompts = very different results. 10% of LLM use is fine-tuning, 90% is prompt engineering.

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