large-language-models

## What I do - Work with large language models - Implement prompt engineering - Fine-tune LLM models - Build RAG systems - Optimize LLM inference - Handle LLM hallucination ## When to use me Use me when: - Building LLM-powered applications - Implementing chatbots - Creating text generation systems - Knowledge-intensive question answering - Text classification with LLMs ## Key Concepts ### LLM Architecture - **Transformer**: Self-attention based - **Encoder-only**: BERT, RoBERTa - **Decoder-only**: GPT, LLaMA, Mistral - **Encoder-decoder**: T5, BART ### Prompt Engineering ```python # Few-shot prompting prompt = """Classify the sentiment as POSITIVE, NEGATIVE, or NEUTRAL. Text: This product is amazing! Sentiment: POSITIVE Text: Terrible experience, would not recommend. Sentiment: NEGATIVE Text: It was okay, nothing special. Sentiment: NEUTRAL Text: I absolutely love this! Sentiment:""" # Chain-of-thought cot_prompt = """Solve the problem step by step. Problem: If there are 5 birds and you shoot 2, how many remain? Solution: First, 5 - 2 = 3. Then, the remaining birds fly away because of the noise, so 0 remain. Answer: 0 Problem: John has 3 apples. He buys 5 more. Then he eats 2. How many? Solution:""" ``` ### Fine-tuning ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import LoraConfig, get_peft_model, TaskType # LoRA fine-tuning model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b") tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b") lora_config = LoraConfig( r=16, lora_alpha=32, target_modules=["q_proj", "v_proj"], lora_dropout=0.05, bias="none", task_type=TaskType.CAUSAL_LM ) model = get_peft_model(model, lora_config) # Fine-tune on custom dataset ``` ### RAG (Retrieval-Augmented Generation) ```python from langchain.vectorstores import Chroma from langchain.embeddings import OpenAIEmbeddings from langchain.chains import RetrievalQA from langchain.llms import OpenAI # Build retriever embeddings = OpenAIEmbeddings() vectorstore = Chroma.from_documents(documents, embeddings) retriever = vectorstore.as_retriever() # Create QA chain qa = RetrievalQA.from_chain_type( llm=OpenAI(), chain_type="stuff", retriever=retriever ) answer = qa.run(query) ``` ### Challenges - Hallucination - Context length limits - Token costs - Latency - Bias and safety