unturf.

Using the Hermes AI Model

At ai.unturf.com, we offer a free AI service powered by the model NousResearch/Hermes-3-Llama-3.1-8B. Our mission is to provide accessible AI tools for everyone, embodying the principles of both free as in beer & free as in freedom. You can interact with our model without any cost, and you are encouraged to contribute and build upon the open-source code & models that we use.

Installing the OpenAI Client

Python

To install the OpenAI package for Python, use pip:

pip install openai

Node.js

To install the OpenAI package for Node.js, you can use npm in your package.json:

{
  "dependencies": {
    "openai": "^v4.67.3"  // Use the latest version
  }
}
        

Run the following command to install it:

npm install

Python Example

Non-Streaming

# Python Fizzbuzz Example
from openai import OpenAI

client = OpenAI(base_url="https://hermes.ai.unturf.com/v1", api_key="none")

MODEL = "NousResearch/Hermes-3-Llama-3.1-8B"

messages = [{"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}]

response = client.chat.completions.create(
    model=MODEL,
    messages=messages,
    temperature=0.5,
    max_tokens=150
)

print(response.choices[0].message.content)
        

Streaming

# Streaming response in Python
from openai import OpenAI

client = OpenAI(base_url="https://hermes.ai.unturf.com/v1", api_key="none")

MODEL = "NousResearch/Hermes-3-Llama-3.1-8B"

messages = [
    {"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}
]

response = client.chat.completions.create(
    model=MODEL,
    messages=messages,
    temperature=0.5,
    max_tokens=150,
    stream=True,  # Enable streaming
)

for chunk in response:
    if hasattr(chunk.choices[0].delta, "content"):
        print(chunk.choices[0].delta.content, end="")
        

Node.js Example

Non-Streaming

const OpenAI = require('openai');

const client = new OpenAI({
    baseURL: "https://hermes.ai.unturf.com/v1",
    apiKey: "dummy-api-key",
});

const MODEL = "NousResearch/Hermes-3-Llama-3.1-8B";

const messages = [{"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}];

async function getResponse() {
    try {
        const response = await client.chat.completions.create({
            model: MODEL,
            messages: messages,
            temperature: 0.5,
            max_tokens: 150,
        });

        console.log(response.choices[0].message.content);
    } catch (error) {
        console.error("Error:", error.response ? error.response.data : error.message);
    }
}

getResponse();
        

Streaming

const OpenAI = require('openai');

const client = new OpenAI({
    baseURL: "https://hermes.ai.unturf.com/v1",
    apiKey: "dummy-api-key",
});

const MODEL = "NousResearch/Hermes-3-Llama-3.1-8B";

const messages = [{"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}];

async function streamResponse() {
    try {
        const stream = await client.chat.completions.create({
            model: MODEL,
            messages: messages,
            temperature: 0.5,
            max_tokens: 150,
            stream: true,  // Enable streaming
        });

        // Use async iterator to read each chunk
        for await (const chunk of stream) {
            const msg = chunk.choices[0].delta.content;
            process.stdout.write(msg);  // Print each chunk as it arrives
        }
    } catch (error) {
        console.error("Error:", error.response ? error.response.data : error.message);
    }
}

streamResponse();
        

How we run inference if you wanted to try to contribute

We use vLLM to run models, currently full f16 safetensors. We make sure to use a virtualenv to hold the dependencies.

We are considering supporting ollama for better quant support.

Stand up a replica cluster on a new domain.

cd ~
python3 -m venv env
source env/bin/activate
pip install vllm
python -m vllm.entrypoints.openai.api_server --model NousResearch/Hermes-3-Llama-3.1-8B --host 0.0.0.0 --port 18888 --max-model-len 16000
        

If you want to see how we setup the proxy, check out /etc/caddy/Caddyfile

ai.unturf.com {
    root * /opt/www
    file_server
    log {
        output file /var/log/caddy/ai.unturf.com.log {
            roll_size 50mb
            roll_keep 5
        }
    }
    tls {
        on_demand
    }
}

hermes.ai.unturf.com {
    reverse_proxy :18888
    log {
        output file /var/log/caddy/hermes.ai.unturf.com.log {
            roll_size 50mb
            roll_keep 5
        }
    }
    tls {
        on_demand
    }
}

	

We will likely implement a rate limit based on client IP address.

Web Client-Only Solution: Interact with AI Services Directly from Static Sites or CDNs

Because we don't require a valid API key, we don't have any real need for a server.

This web client-only solution uses uncloseai.js which is designed to support static sites or CDNs hosting HTML content. In this architecture, the browser serves as the client, directly interacting with the API without the need for an intermediary server/client. Because we eliminate the requirement for a valid API key, we allow the API to handle requests on behalf of the browser client, making it an efficient & accessible for thin clients which are often on battery.

Feel free to message us in the box below.

Questions & Comments & Discussions