In 2025, LLaMA (Large Language Model Meta AI) has consolidated as one of the most versatile options for chatbots local or cloud-based, capable of processing text, images, and audio. In this tutorial, you will learn to create a multimodal chatbot using only LLaMA.
LLaMA 3 has versions with 7B, 13B, and 70B parameters; for local testing, 7B-13B are usually sufficient.
python -m venv llama-env source llama-env/bin/activate # Linux / Mac .\llama-env\Scripts\activate # Windows
Install necessary libraries:
pip install torch torchvision torchaudio pip install transformers sentencepiece pillow pip install pyttsx3 # For local TTS
Download LLaMA 3 from your official source (for example Ollama or Hugging Face).
Llama 3 Chatbot Base Code
from transformers import LlamaForCausalLM, LlamaTokenizer import torch import pyttsx3 from PIL import Image # Cargar modelo y tokenizer model_name = "llama-3-7b" tokenizer = LlamaTokenizer.from_pretrained(model_name) model = LlamaForCausalLM.from_pretrained(model_name, device_map="auto") # Función para generar respuesta def generate_response(prompt): inputs = tokenizer(prompt, return_tensors="pt").to(model.device) output = model.generate(**inputs, max_new_tokens=200) response = tokenizer.decode(output[0], skip_special_tokens=True) return response # TTS function def speak(texto): engine = pyttsx3.init() engine.say(texto) engine.runAndWait() # Example usage prompt = "Hello, show me a funny example of a multimodal chatbot." response = generate_response(prompt) print(response) speak(response)
Tip: For multimodal input, you can integrate LLaMA with CLIP or BLIP to process images, and then pass the generated text to the main model.
Integrating Images
from transformers import BlipProcessor, BlipForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") model_blip = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") def describe_image(ruta_imagen): image = Image.open(ruta_imagen) inputs = processor(image, return_tensors="pt") out = model_blip.generate(**inputs) return processor.decode(out[0], skip_special_tokens=True) # Usage descripcion = describe_image("mi_foto.jpg") response = generate_response(f"Based on this: {descripcion}, tell me something funny.") print(response)
