Arpay

Large Language Models (LLMs) are transforming the way companies operate, enabling powerful AI-driven automation, content generation, and decision support...

1. Choosing the Right Infrastructure

On-Premises vs. Cloud Deployment: Your choice depends on factors like budget, scalability, and security.

On-Premises

Ideal for organizations with strict compliance requirements. Requires high-performance hardware.

Cloud

Scalable and easier to manage. Providers include AWS, Azure, and Google Cloud.

2. Selecting the Right LLM

Popular self-hosted models:

• Llama 2 (Meta): Various sizes available.
• Mistral 7B: Efficient and powerful.
• Falcon (TII): Optimized for multilingual applications.

3. Setting Up the LLM

Install dependencies and download the model using Python:

pip install torch transformers vllm fastapi

4. Deploying with an API

Use FastAPI to serve your model:

from fastapi import FastAPI
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

app = FastAPI()
model_name = "meta-llama/Llama-2-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16).cuda()

@app.post("/generate")
def generate_text(prompt: str):
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    output = model.generate(**inputs)
    return {"response": tokenizer.decode(output[0])}
          

Run the API:

uvicorn app:app --host 0.0.0.0 --port 8000

5. Security & Compliance

Encrypt data, implement role-based access control, and comply with GDPR.

How Arpay Can Help

We help businesses deploy secure self-hosted LLMs. Contact us to get started!