uncloseai.
Welcome to uncloseai.com - Free LLM & Text To Speech Artificial Intelligence Service
Introducing uncloseai & Hermes & TTS Speech Endpoints
At uncloseai., we offer free AI services powered by the NousResearch/Hermes-3-Llama-3.1-8B model and a TTS (Text-to-Speech) endpoint. 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 models without any cost, and you are encouraged to contribute and build upon the open-source code & models that we use.
We intend to be a drop in replacement, you can use the existing open source OpenAI client to communicate with us.
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.
Add this LLM to any static site or CDN.
This web client-only solution uses uncloseai.js to make the browser act as a client, directly interacting with the API without needing an intermediary server. By eliminating the need for a valid API key, the API handles requests on behalf of the browser client, making it efficient and accessible thin client, especially those on battery power like phones & laptops.
🚀 Quick Start: Install uncloseai.js on any website
Add AI capabilities to any website with just one line of code. The floating "uncloseai." button appears automatically, providing access to Hermes AI, text-to-speech, translation, and more.
✨ One-Line Installation
Add this script tag to your HTML - that's it! The floating AI button appears automatically:
<script src="https://uncloseai.com/uncloseai.js" type="module"></script>🎯 What You Get
- 🤖 Floating AI Assistant - Always-accessible "uncloseai." button in bottom right
- 📖 Page-Aware - Hermes AI understands your page content and provides contextual help
- 🔊 Text-to-Speech - Convert any text to speech with multiple voice options
- 🌐 Translation - Translate content into 19 languages with formatting preservation
- 💾 Conversation History - Persistent chat history stored in browser
- 🎨 Framework Compatible - Works with any CSS framework
🎮 Live Demo & Complete Documentation: See uncloseai.js in action with 12+ integration examples →
The demo page includes configuration options, embedded widgets, custom implementations, direct API usage, and more advanced features.
Installing the OpenAI Client
Python Client Installation
To install the OpenAI package for Python, use pip:
pip install openaiNode.js Client Installation
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 installUsing the Hermes AI Model
Python Examples
Python Non-Streaming
# Python Fizzbuzz Example
from openai import OpenAI
client = OpenAI(base_url="https://hermes.ai.unturf.com/v1", api_key="choose-any-value")
#MODEL = "NousResearch/Hermes-3-Llama-3.1-8B"
MODEL = "adamo1139/Hermes-3-Llama-3.1-8B-FP8-Dynamic"
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)
Python Streaming
# Streaming response in Python
from openai import OpenAI
client = OpenAI(base_url="https://hermes.ai.unturf.com/v1", api_key="choose-any-value")
MODEL = "adamo1139/Hermes-3-Llama-3.1-8B-FP8-Dynamic"
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 Examples
Node.js Non-Streaming
const OpenAI = require('openai');
const client = new OpenAI({
baseURL: "https://hermes.ai.unturf.com/v1",
apiKey: "dummy-api-key",
});
const MODEL = "adamo1139/Hermes-3-Llama-3.1-8B-FP8-Dynamic";
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();
Node.js Streaming
const OpenAI = require('openai');
const client = new OpenAI({
baseURL: "https://hermes.ai.unturf.com/v1",
apiKey: "dummy-api-key",
});
const MODEL = "adamo1139/Hermes-3-Llama-3.1-8B-FP8-Dynamic";
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();
Using the Text To Speech Endpoint
Python TTS Example
# TTS Speech Example in Python
import openai
client = openai.OpenAI(
api_key = "YOLO",
base_url = "https://speech.ai.unturf.com/v1",
)
with client.audio.speech.with_streaming_response.create(
model="tts-1",
voice="alloy",
speed=0.9,
input="I think so therefore, Today is a wonderful day to build something people love!"
) as response:
response.stream_to_file("speech.mp3")
Node.js TTS Example
const OpenAI = require('openai');
const client = new OpenAI({
baseURL: "https://speech.ai.unturf.com/v1",
apiKey: "YOLO",
});
async function getSpeech() {
try {
const response = await client.audio.speech.with_streaming_response.create({
model: "tts-1",
voice: "alloy",
speed: 0.9,
input: "I think so therefore, Today is a wonderful day to build something people love!"
});
response.stream_to_file("speech.mp3");
} catch (error) {
console.error("Error:", error.response ? error.response.data : error.message);
}
}
getSpeech();
How we run inference
This section is optional. This is only if you wanted to try to contribute idle GPU time to the project or if you wanted to reproduce everything in your own cluster.
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.
sudo apt-get install gcc python3.12-dev python3.12-venv
cd ~
python3 -m venv env
source env/bin/activate
pip install vllm
python -m vllm.entrypoints.openai.api_server --model adamo1139/Hermes-3-Llama-3.1-8B-FP8-Dynamic --host 0.0.0.0 --port 18888 --max-model-len 82000
The Speech endpoint or TTS uses openedai-speech running via Docker.
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
}
}
speech.ai.unturf.com {
reverse_proxy :8000
log {
output file /var/log/caddy/speech.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.
Open Source & Public Domain
uncloseai. is completely public domain - free as in beer, free as in freedom. AI for all, internet for all, open web for everyone.
📦 Source Code: https://git.unturf.com/engineering/unturf/uncloseai.com
📄 Full Documentation: See README.rst in the repository for complete technical details, architecture, and contribution guidelines.