311 lines
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311 lines
11 KiB
HTML
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<title>Using Free Hermes AI Service | ai.unturf.com</title>
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<header>
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<hgroup>
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<h1 class="unturf" style="font-family: 'ChunkFiveRegular';">unturf.</h1>
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<p>Welcome to ai.unturf.com - Free AI Service Powered by Hermes AI</p>
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<main>
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<h2>Using the Hermes AI Model</h2>
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<p>At <strong>ai.unturf.com</strong>, we offer a free AI service powered by the model <a href="https://nousresearch.com/hermes3/" target="_blank">NousResearch/Hermes-3-Llama-3.1-8B</a>. 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.</p>
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<h3>Installing the OpenAI Client</h3>
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<h4>Python</h4>
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<p>To install the OpenAI package for Python, use <code>pip</code>:</p>
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<pre><code>pip install openai</code></pre>
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<h4>Node.js</h4>
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<p>To install the OpenAI package for Node.js, you can use <code>npm</code> in your <code>package.json</code>:</p>
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<pre><code>{
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"dependencies": {
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"openai": "^v4.67.3" // Use the latest version
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}
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}
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</code></pre>
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<p>Run the following command to install it:</p>
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<pre><code>npm install</code></pre>
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<h2>Python Example</h2>
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<h3>Non-Streaming</h3>
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<pre><code class="python"># Python Fizzbuzz Example
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from openai import OpenAI
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client = OpenAI(base_url="https://hermes.ai.unturf.com/v1", api_key="none")
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MODEL = "NousResearch/Hermes-3-Llama-3.1-8B"
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messages = [{"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}]
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response = client.chat.completions.create(
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model=MODEL,
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messages=messages,
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temperature=0.5,
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max_tokens=150
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)
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print(response.choices[0].message.content)
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</code></pre>
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<h3>Streaming</h3>
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<pre><code class="python"># Streaming response in Python
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from openai import OpenAI
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client = OpenAI(base_url="https://hermes.ai.unturf.com/v1", api_key="none")
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MODEL = "NousResearch/Hermes-3-Llama-3.1-8B"
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messages = [
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{"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}
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]
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response = client.chat.completions.create(
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model=MODEL,
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messages=messages,
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temperature=0.5,
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max_tokens=150,
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stream=True, # Enable streaming
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)
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for chunk in response:
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if hasattr(chunk.choices[0].delta, "content"):
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print(chunk.choices[0].delta.content, end="")
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</code></pre>
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<h2>Node.js Example</h2>
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<h3>Non-Streaming</h3>
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<pre><code class="javascript">const OpenAI = require('openai');
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const client = new OpenAI({
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baseURL: "https://hermes.ai.unturf.com/v1",
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apiKey: "dummy-api-key",
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});
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const MODEL = "NousResearch/Hermes-3-Llama-3.1-8B";
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const messages = [{"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}];
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async function getResponse() {
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try {
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const response = await client.chat.completions.create({
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model: MODEL,
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messages: messages,
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temperature: 0.5,
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max_tokens: 150,
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});
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console.log(response.choices[0].message.content);
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} catch (error) {
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console.error("Error:", error.response ? error.response.data : error.message);
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}
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}
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getResponse();
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</code></pre>
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<h3>Streaming</h3>
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<pre><code class="javascript">
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const OpenAI = require('openai');
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const client = new OpenAI({
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baseURL: "https://hermes.ai.unturf.com/v1",
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apiKey: "dummy-api-key",
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});
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const MODEL = "NousResearch/Hermes-3-Llama-3.1-8B";
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const messages = [{"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}];
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async function streamResponse() {
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try {
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const stream = await client.chat.completions.create({
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model: MODEL,
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messages: messages,
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temperature: 0.5,
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max_tokens: 150,
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stream: true, // Enable streaming
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});
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// Use async iterator to read each chunk
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for await (const chunk of stream) {
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const msg = chunk.choices[0].delta.content;
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process.stdout.write(msg); // Print each chunk as it arrives
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}
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} catch (error) {
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console.error("Error:", error.response ? error.response.data : error.message);
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}
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}
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streamResponse();
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</code></pre>
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<h2>How we run inference if you wanted to try to contribute</h2>
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<p>We use vLLM to run models, currently full f16 safetensors. We make sure to use a virtualenv to hold the dependencies.</p>
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<p>We are considering supporting ollama for better quant support.</p>
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<p>Stand up a replica cluster on a new domain.</p>
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<pre><code>
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cd ~
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python3 -m venv env
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source env/bin/activate
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pip install vllm
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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
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</code></pre>
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<p>If you want to see how we setup the proxy, check out <a href="https://git.unturf.com/-/snippets/3">/etc/caddy/Caddyfile</a></p>
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<pre><code>
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ai.unturf.com {
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root * /opt/www
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file_server
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log {
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output file /var/log/caddy/ai.unturf.com.log {
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roll_size 50mb
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roll_keep 5
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}
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}
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tls {
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on_demand
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}
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}
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hermes.ai.unturf.com {
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reverse_proxy <removed>:18888
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log {
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output file /var/log/caddy/hermes.ai.unturf.com.log {
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roll_size 50mb
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roll_keep 5
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}
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}
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tls {
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on_demand
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}
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}
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</code></pre>
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<p>We will likely implement a rate limit based on client IP address.</p>
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<h2 id="client-side">Web Client-Only Solution: Interact with AI Services Directly from Static Sites or CDNs</h2>
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<p><b>Because we don't require a valid API key, we don't have any real need for a server.</b></p>
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<p>This web client-only solution uses <a href="https://uncloseai.com/uncloseai.js" target="_blank">uncloseai.js</a> 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.<p>
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<div id="chat-container">
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<br/>
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<p>Feel free to message us in the box below.</p>
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<h2>Questions & Comments & Discussions</h2>
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Use the <a href="https://www.remarkbox.com" target="_blank">Remarkbox</a> below to tell us what you think!
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