Listen to this Post
This repository contains the complete code examples from the book “Hands-On Large Language Models”, covering everything from foundational concepts to advanced fine-tuning techniques.
🔗 GitHub Repo: https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
Chapters Covered:
1. to Language Models
2. Tokens and Embeddings
3. Inside Transformer LLMs
4. Text Classification
5. Text Clustering and Topic Modeling
6. Prompt Engineering
7. Advanced Text Generation Techniques
8. Semantic Search & Retrieval-Augmented Generation (RAG)
9. Multimodal Large Language Models
10. Creating Text Embedding Models
11. Fine-tuning Representation Models for Classification
12. Fine-tuning Generation Models
You Should Know:
1. Running LLMs Locally
To experiment with LLMs, you can use Hugging Face’s Transformers library. Install it via:
pip install transformers torch
Then load a model (e.g., GPT-2) in Python:
from transformers import GPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")
input_text = "Large Language Models are"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(inputs, max_length=50)
print(tokenizer.decode(outputs[bash], skip_special_tokens=True))
2. Fine-tuning with LoRA (Low-Rank Adaptation)
For efficient fine-tuning, use PEFT (Parameter-Efficient Fine-Tuning):
pip install peft accelerate datasets
Example fine-tuning script:
from peft import LoraConfig, get_peft_model
model = GPT2LMHeadModel.from_pretrained("gpt2")
lora_config = LoraConfig(
r=8,
lora_alpha=32,
target_modules=["c_attn"],
lora_dropout=0.1,
)
peft_model = get_peft_model(model, lora_config)
peft_model.train()
3. Retrieval-Augmented Generation (RAG) Setup
Use FAISS for efficient semantic search:
pip install faiss-cpu sentence-transformers
Example RAG implementation:
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
model = SentenceTransformer('all-MiniLM-L6-v2')
sentences = ["LLMs are powerful.", "RAG improves accuracy."]
embeddings = model.encode(sentences)
index = faiss.IndexFlatL2(embeddings.shape[bash])
index.add(embeddings)
query = "What is RAG?"
query_embedding = model.encode([bash])
distances, indices = index.search(query_embedding, k=1)
print("Most relevant sentence:", sentences[indices[bash][0]])
What Undercode Say:
Large Language Models (LLMs) are revolutionizing AI, but practical implementation requires hands-on experimentation. Key takeaways:
– Fine-tuning is essential for domain-specific tasks.
– Prompt Engineering can drastically improve output quality.
– RAG bridges knowledge gaps in LLMs by integrating external data.
For cybersecurity professionals, LLMs can be used for:
- Log Analysis: Automate threat detection using NLP.
- Phishing Detection: Train models to identify malicious emails.
- Incident Response: Generate automated reports from security logs.
Linux & Windows Commands for AI Workflows:
Monitor GPU usage (Linux)
nvidia-smi
Kill a process hogging GPU (Linux)
kill -9 $(ps aux | grep 'python' | awk '{print $2}')
Set up a Python virtual env (Windows/Linux)
python -m venv llm_env
source llm_env/bin/activate Linux
.\llm_env\Scripts\activate Windows
Clone the repo
git clone https://github.com/HandsOnLLM/Hands-On-Large-Language-Models.git
Expected Output:
A structured, code-heavy guide to implementing LLMs, covering fine-tuning, RAG, and prompt engineering with executable examples.
Prediction:
LLMs will increasingly integrate with cybersecurity tools, automating threat analysis and response while requiring robust adversarial training to prevent misuse.
References:
Reported By: Sumanth077 Hands – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
