Working Of Language Models Explained

➤ Join Our community for latest AI updates: https://lnkd.in/gNbAeJG2
➤ Access Top AI models like GPT-4o, Llama, and more, all in one place for FREE: https://thealpha.dev

You Should Know:

1. Data Collection

Linux Command to download text datasets:
```
wget https://example.com/dataset.txt 
```

Python Script to clean text data:

import re 
def clean_text(text): 
return re.sub(r'[^\w\s]', '', text)

2. Tokenization

Using `transformers` library in Python:

from transformers import AutoTokenizer 
tokenizer = AutoTokenizer.from_pretrained("gpt-4") 
tokens = tokenizer.encode("Hello, world!")

Linux `awk` for basic tokenization:

echo "Hello, world!" | awk '{for(i=1;i<=NF;i++) print $i}'

3. Embedding

Generate embeddings with sentence-transformers:

from sentence_transformers import SentenceTransformer 
model = SentenceTransformer('all-MiniLM-L6-v2') 
embeddings = model.encode("Sample text")

4. Model Architecture (Transformers)

Run a Transformer model locally:

git clone https://github.com/huggingface/transformers 
cd transformers && pip install -e .

5. Training

Fine-tune with Hugging Face:

from transformers import Trainer, TrainingArguments 
training_args = TrainingArguments(output_dir="./results") 
trainer = Trainer(model=model, args=training_args, train_dataset=dataset) 
trainer.train()

6. Inference

Run GPT-4o via API:

curl -X POST https://api.openai.com/v1/chat/completions -H "Authorization: Bearer YOUR_KEY" -d '{"model":"gpt-4","messages":[{"role":"user","content":"Explain AI"}]}'

7. Fine-Tuning

Use LoRA for efficient fine-tuning:

pip install peft

from peft import LoraConfig, get_peft_model 
lora_config = LoraConfig(task_type="CAUSAL_LM") 
model = get_peft_model(model, lora_config)

8. Deployment

Deploy with FastAPI:

from fastapi import FastAPI 
app = FastAPI() 
@app.post("/predict") 
def predict(text: str): 
return {"response": model.generate(text)}

Run FastAPI server:
```
uvicorn app:app --reload 
```

9. Evaluation

Calculate BLEU score in Python:

from nltk.translate.bleu_score import sentence_bleu 
score = sentence_bleu([bash], candidate)

What Undercode Say

Language models power modern AI, but their true strength lies in iterative refinement. From tokenization to deployment, each step demands precision. Future advancements will focus on:
– Efficient training (e.g., quantization with bitsandbytes).
– Edge deployment (e.g., ONNX runtime for Windows/Linux).
– Ethical safeguards (e.g., NVIDIA NeMo Guardrails).

Expected Output:

A fully deployed AI model answering queries like:

User: "Explain quantum computing." 
AI: "Quantum computing leverages qubits to perform complex calculations exponentially faster than classical computers..."

Prediction

By 2026, 70% of enterprises will integrate custom fine-tuned LLMs into workflows, driven by tools like LoRA and Hugging Face.

Relevant URLs:

References:

Reported By: Thealphadev Working – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

Join Our Cyber World:

💬 Whatsapp | 💬 Telegram

Listen to this Post