LLM Engineer
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<h1>LLM Engineering Concepts</h1>
<p><strong>Understanding key concepts in Large Language Models (LLMs), Agents, OpenAI, RAG, and Fine-Tuning.</strong></p>
<hr>
<h2>1. Introduction to LLMs</h2>
<p>Large Language Models (LLMs) process text using deep learning. Examples include OpenAI's GPT, Claude, LLaMA, and Falcon.</p>
<pre><code>from openai import OpenAI
client = OpenAI(api_key="your_api_key")
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "What is LLM?"}]
)
print(response.choices[0].message["content"])</code></pre>
<hr>
<h2>2. Retrieval-Augmented Generation (RAG)</h2>
<p>Enhances LLMs by integrating retrieval mechanisms using vector databases like FAISS, Pinecone, and Weaviate.</p>
<pre><code>import faiss
import numpy as np
d = 128 # Vector dimension
index = faiss.IndexFlatL2(d)
data = np.random.random((100, d)).astype('float32')
index.add(data)
query = np.random.random((1, d)).astype('float32')
D, I = index.search(query, 5)
print(I) # Indices of closest vectors</code></pre>
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<h2>3. Fine-Tuning LLMs</h2>
<p>Fine-tuning is training a pre-trained model on domain-specific data.</p>
<pre><code>openai api fine_tunes.create -t "training_data.jsonl" -m "gpt-3.5-turbo"</code></pre>
<hr>
<h2>4. Agents in LLMs</h2>
<p>Agents use planning & tool execution to complete tasks. Example using LangChain:</p>
<pre><code>from langchain.agents import AgentType, initialize_agent
from langchain.llms import OpenAI
from langchain.tools import Tool
tools = [Tool(name="Calculator", func=lambda x: eval(x), description="Performs math operations.")]
agent = initialize_agent(tools, OpenAI(model="gpt-4"), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("What is 10 + 25?")</code></pre>
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<h2>5. Vector Databases & Embeddings</h2>
<pre><code>from openai import OpenAI
client = OpenAI(api_key="your_api_key")
response = client.embeddings.create(
input="LLMs are powerful tools.",
model="text-embedding-ada-002"
)
print(response["data"][0]["embedding"])</code></pre>
<hr>
<h2>6. Prompt Engineering</h2>
<p>Crafting effective prompts for better LLM responses.</p>
<pre><code>prompt = """
Convert the following sentences to past tense:
1. She runs fast.
2. They eat dinner.
3. I write a book.
"""
response = client.chat.completions.create(model="gpt-4", messages=[{"role": "user", "content": prompt}])
print(response.choices[0].message["content"])</code></pre>
<hr>
<h2>7. LLM Deployment & API Integration</h2>
<pre><code>from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "meta-llama/Llama-2-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
input_text = "Explain quantum computing."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))</code></pre>
<hr>
<h2>8. Ethical Considerations & Biases</h2>
<p>LLMs inherit biases from training data. Example of OpenAI's moderation API:</p>
<pre><code>response = client.moderations.create(input="Some sensitive content")
print(response["results"][0]["flagged"]) # Returns True if flagged</code></pre>
<hr>
<h2>Conclusion</h2>
<p>Understanding LLMs, RAG, Fine-Tuning, Agents, and Deployment is crucial for AI engineers. These concepts help build powerful AI-driven applications.</p>
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