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You Should Know:
1. LLM (Large Language Models)
➤ Definition: Advanced AI trained on vast datasets for human-like text generation.
➤ Commands & Tools:
Install Hugging Face Transformers for LLM access
pip install transformers
Load a pre-trained GPT model
from transformers import GPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")
2. Transformers
➤ Definition: Neural networks using attention mechanisms for sequential data processing.
➤ Implementation:
Fine-tuning a Transformer model
from transformers import BertForSequenceClassification, BertTokenizer
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
3. Prompt Engineering
➤ Definition: Crafting precise inputs to guide AI responses.
➤ Example:
Using OpenAI API for prompt-based generation
curl https://api.openai.com/v1/completions \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"prompt": "Explain quantum computing", "model": "text-davinci-003"}'
4. Fine-tuning
➤ Definition: Adapting pre-trained models for specific tasks.
➤ Steps:
Fine-tuning with PyTorch python run_glue.py \ --model_name_or_path bert-base-uncased \ --task_name mrpc \ --do_train \ --do_eval \ --output_dir /tmp/finetuned-bert
5. Embeddings
➤ Definition: Converting text into numerical vectors.
➤ Code:
Generating embeddings with Sentence-BERT
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = model.encode("Generative AI is transformative.")
6. RAG (Retrieval-Augmented Generation)
➤ Definition: Combining retrieval and generation for accuracy.
➤ Implementation:
Using FAISS for vector search (RAG) pip install faiss-cpu from faiss import IndexFlatL2 index = IndexFlatL2(embeddings.shape[bash]) index.add(embeddings)
7. Tokens
➤ Definition: Small units processed by AI models.
➤ Check Token Count:
Count tokens in a text
from transformers import GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokens = tokenizer.tokenize("Hello, AI world!")
print(len(tokens))
8. Hallucination
➤ Definition: AI generating incorrect but plausible responses.
➤ Mitigation:
Reduce hallucination with temperature control output = model.generate(input_ids, temperature=0.7, max_length=50)
9. Zero-shot Learning
➤ Definition: AI performs tasks without prior examples.
➤ Example:
Zero-shot classification with Hugging Face
from transformers import pipeline
classifier = pipeline("zero-shot-classification")
result = classifier("AI is revolutionizing healthcare", candidate_labels=["tech", "health", "business"])
10. Chain-of-Thought (CoT)
➤ Definition: Guiding AI through logical reasoning steps.
➤ Prompt Example:
"Solve step-by-step: If 3x + 5 = 20, what is x?"
11. Context Window
➤ Definition: Maximum input size an AI can process.
➤ Check Model Limits:
Check max sequence length for GPT-3 print(tokenizer.model_max_length) Output: 2048 (for GPT-3)
12. Temperature
➤ Definition: Controls randomness in AI outputs.
➤ Adjust in API Calls:
curl -X POST https://api.openai.com/v1/completions \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{"prompt": "Write a poem", "temperature": 0.5, "max_tokens": 100}'
What Undercode Say:
Generative AI is reshaping industries, from automated coding to dynamic content creation. Mastering these terms ensures better AI utilization. Future advancements will integrate deeper reasoning, reducing hallucinations and improving zero-shot capabilities.
Prediction:
By 2026, AI models will achieve near-human contextual understanding, making prompt engineering and RAG even more critical for precision tasks.
Expected Output:
A structured guide on Generative AI terms with practical commands and future insights.
Relevant URLs:
References:
Reported By: Vishnunallani 12 – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
