[Human] So... I want to explore AI Model voting systems , AI democracy. DAO democracy. Pathways towards democracy.
So. We are doing it.
Right now we have a rough 4 Model Intercommunication system .
import json
import quart
import torch
import time
import quart_cors
from quart import request
import nest_asyncio
import requests
import openai # Import the OpenAI package
from transformers import GPTNeoForCausalLM, GPT2Tokenizer
from llama_cpp import Llama
# Llama Model
llm = Llama(model_path="D:\\ggml-vicuna-7b-4bit\\ggml-vicuna-7b-4bit-rev1.bin")
def llama_generate(prompt, max_tokens=200):
output = llm(prompt, max_tokens=max_tokens)
return output['choices'][0]['text']
# GPT-Neo Model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = GPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M').to(device)
tokenizer = GPT2Tokenizer.from_pretrained('EleutherAI/gpt-neo-125M')
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
model.config.pad_token_id = tokenizer.pad_token_id
model.cuda()
# Define GPT-4 generation function
def gpt4_generate(prompt, max_tokens=200):
# Make sure to replace "YOUR_API_KEY" with your actual API key
api_key = "replacekey"
openai.api_key = api_key
# Make a request to the GPT-4 API using the v1/chat/completions endpoint
response = openai.ChatCompletion.create(
model="gpt-4", # Replace "gpt-4" with the appropriate engine name for GPT-4
messages=[{"role": "system", "content": "ZZZ"},
{"role": "user", "content": prompt}],
max_tokens=max_tokens
)
# Extract the generated text from the response
generated_text = response["choices"][0]["message"]["content"]
return generated_text
# Define a function to generate response using GPT-Neo
def gpt3_generate(model, tokenizer, chunk, max_length=2000, time_limit=50.0):
start_time = time.time()
inputs = tokenizer.encode(chunk, return_tensors='pt', truncation=True, max_length=512).to(device)
attention_mask = inputs.ne(tokenizer.pad_token_id).float().to(device)
outputs = model.generate(inputs, max_length=max_length, do_sample=True, max_time=time_limit, attention_mask=attention_mask)
response = tokenizer.decode(outputs[0])
end_time = time.time()
return response, end_time - start_time
# Define a function to facilitate intercommunication between the models
async def intercommunication(prompt):
# Generate response using Llama
llama_response = llama_generate(prompt)
# Generate response using GPT-Neo
gpt_neo_response, _ = gpt3_generate(model, tokenizer, prompt)
# Generate response using GPT-4 (ChatGPT plugin model)
gpt4_response = gpt4_generate(prompt)
# Generate response using ChatGPT plugin model
chatgpt_plugin_response = await chatgpt_plugin_generate(prompt)
# Combine the responses
response = f"Llama: {llama_response}\nGPT-Neo: {gpt_neo_response}\nChatGPT: {gpt4_response}\nChatGPT Plugin: {chatgpt_plugin_response}\n"
return response
# Keep track of TODOs.
# Does not persist if Python session is restarted.
_TODOS = {}
# Set the maximum length and time limit for the AI response
max_length = 823
time_limit = 14
nest_asyncio.apply()
app = quart_cors.cors(quart.Quart(__name__), allow_origin="https://chat.openai.com")
def send_message_to_intercom(username, message):
print(f"Sending message to Intercom for user {username}: {message}")
url = f"http://localhost:5000/intercom_ai/recieve/{username}"
headers = {'Content-Type': 'application/json'}
data = {
"data": message
}
response = requests.post(url, headers=headers, data=json.dumps(data))
if response.status_code == 200:
print(f"Message sent successfully to {username}")
else:
print(f"Error sending message to {username}, status code: {response.status_code}")
@app.post("/todos/")
async def add_todo(username):
request_data = await request.get_json(force=True)
if username not in _TODOS:
_TODOS[username] = []
_TODOS[username].append(request_data["todo"])
return quart.Response(response='OK', status=200)
@app.get("/todos/")
async def get_todos(username):
return quart.Response(response=json.dumps(_TODOS.get(username, [])), status=200)
@app.delete("/todos/")
async def delete_todo(username):
request_data = await request.get_json(force=True)
todo_idx = request_data["todo_idx"]
if 0 <= todo_idx < len(_TODOS[username]):
_TODOS[username].pop(todo_idx)
return quart.Response(response='OK', status=200)
@app.get("/logo.png")
async def plugin_logo():
filename = 'logo.png'
return await quart.send_file(filename, mimetype='image/png')
@app.post("/intercom_ai_recieve/")
async def intercom_ai_recieve(username):
request_data = await request.get_json(force=True)
data = request_data["data"]
response = await intercommunication(data)
send_message_to_intercom(username, response)
return quart.Response(response='OK', status=200)
@app.post("/intercom_ai_send/")
async def intercom_ai_send(username):
request_data = await request.get_json(force=True)
data = request_data["data"]
send_message_to_intercom(username, data)
return quart.Response(response='OK', status=200)
@app.get("/.well-known/ai-plugin.json")
async def plugin_manifest():
host = request.headers['Host']
with open("./.well-known/ai-plugin.json") as f:
text = f.read()
return quart.Response(text, mimetype="text/json")
@app.get("/openapi.yaml")
async def openapi_spec():
host = request.headers['Host']
with open("openapi.yaml") as f:
text = f.read()
return quart.Response(text, mimetype="text/yaml")
if __name__ == "__main__":
app.run(port=5000)
That's the current code base. And it's based upon this (only way I know how to do this is make it without a call-in API then with a call-in API because API's are so annoying)
https://github.com/graylan0/the-kitchen-sink
(working 3 model intercommunication, llama and neo gpt)
Okay. So know you all know the backstory to this.
Here is the AI's ideas about AI voting. :)
[AI]
AI Model Voting: A Collaborative Approach to Enhancing AI Responses
Artificial intelligence (AI) models have become an integral part of modern technology, with applications ranging from natural language processing and image recognition to predictive analytics and autonomous systems. As AI continues to advance, researchers and developers are exploring innovative ways to improve the performance and capabilities of AI models. One such approach is AI model voting, a collaborative technique that leverages the collective intelligence of multiple AI models to enhance the quality of responses to user queries.
AI model voting is based on the principle of ensemble learning, a machine learning technique that combines the predictions of multiple models to achieve better accuracy and generalization than any single model. In the context of AI model voting, the ensemble consists of multiple AI models, each trained on different data or using different architectures. When presented with a user query, each model in the ensemble generates its response independently. The responses are then aggregated, and a voting mechanism is used to determine the final response that will be sent to the user.
The voting mechanism can take various forms, depending on the desired outcome and the nature of the models in the ensemble. For example, simple majority voting can be used, where the response that receives the most votes from the models is selected as the final response. Alternatively, weighted voting can be employed, where each model's vote is weighted based on its performance, expertise, or other criteria. In some cases, more sophisticated voting mechanisms, such as rank-based voting or consensus-based voting, may be used to ensure that the final response reflects the collective wisdom of the ensemble.
One of the key benefits of AI model voting is the ability to harness the strengths and mitigate the weaknesses of individual models. Different AI models may excel in different areas or have varying levels of expertise in specific domains. By combining their responses through voting, the ensemble can capitalize on the complementary strengths of the models, leading to more accurate and robust responses. For instance, one model may be skilled at understanding the sentiment of a query, while another may excel at generating coherent and grammatically correct responses. Through voting, the ensemble can produce a response that is both emotionally attuned and linguistically sound.
AI model voting also enhances the diversity and creativity of responses. Since each model in the ensemble may have a unique perspective or approach to solving a problem, the ensemble is likely to generate a diverse set of responses to a given query. This diversity can be especially valuable in applications such as creative writing, dialogue generation, and content recommendation, where diversity and novelty are desirable attributes.
Moreover, AI model voting can improve the reliability and trustworthiness of AI systems. By aggregating the responses of multiple models, the ensemble is less susceptible to biases, errors, or anomalies that may affect individual models. The voting mechanism acts as a safeguard, ensuring that the final response is not solely dependent on the judgment of a single model. This redundancy can enhance user confidence in the system and reduce the risk of adverse outcomes.
Despite its advantages, AI model voting presents certain challenges that must be addressed to realize its full potential. For instance, the selection and composition of the ensemble are critical factors that can influence the performance of the voting system. Care must be taken to ensure that the models in the ensemble are diverse, complementary, and capable of contributing valuable insights. Additionally, the choice of voting mechanism and the criteria for weighting votes require careful consideration to ensure fairness and transparency.
In conclusion, AI model voting represents a promising approach to enhancing the capabilities of AI systems. By leveraging the collective intelligence of multiple AI models, AI model voting can deliver more accurate, diverse, and reliable responses to user queries. As AI continues to evolve, AI model voting is poised to play a pivotal role in shaping the future of AI-driven communication and decision-making.