#!/usr/bin/env python
# coding: utf-8

# <br>
# <br>
# 
# # **Modelos del lenguaje basados en redes neuronales artificiales**

# ## **GPT (Generative Pre-Training Transformer)**
# 
# Consituyen una serie de modelos del lenguaje desarrollados por OpenAI. Estos modelos son conocidos por su capacidad para generar texto, responder preguntas, traducir idiomas, resumir documentos y realizar una variedad de otras tareas lingüísticas. La arquitectura de GPT se basa en el concepto de *Transformer*. 

# In[1]:


from ctransformers import AutoModelForCausalLM

llm = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GGUF", model_file="mistral-7b-instruct-v0.1.Q5_K_M.gguf", model_type="mistral", gpu_layers=50)


# In[2]:


print(llm("¿Si tengo 15 manzanas y me como 3, cuántas me quedan?"))


# <img src="imgs/LMStudio.png" width="300">
# 
# Descarga e instala LM Studio desde [aquí](https://lmstudio.ai/).

# In[3]:


import os
import openai

openai.api_base = "http://localhost:1234/v1" # point to the local server
openai.api_key = "" # no need for an API key

completion = openai.ChatCompletion.create(
  model="local-model", # this field is currently unused
  messages=[
    {"role": "system", "content": "You are an assistant helping a student with their homework"},
    {"role": "assistant", "content": "Hi, how can I help you today?"},
    {"role": "user", "content": "I need help with my chemistry homework. Can you help me?"},
    {"role": "assistant", "content": "Of course! What specific topic or problem are you working on? Please provide more details so I can better assist you."},
    {"role": "user", "content": "Do you know how to formulate oxides?"},
  ]
)

print(completion.choices[0].message)


# 

# In[4]:


print(completion.choices[0].message["content"])