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<h1 class="unturf">unturf.</h1>
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<h1>Welcome to ai.unturf.com</h1>
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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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<h2>Using the Hermes AI Model</h2>
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<p>This guide explains how to use the official OpenAI client to interact with the Hermes AI model hosted at <strong>hermes.ai.unturf.com/v1</strong>. You can use this endpoint without an API key.</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>
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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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# Set your model
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MODEL = "NousResearch/Hermes-3-Llama-3.1-8B"
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# Define your messages
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messages = [{"role": "user", "content": "Give a Python Fizzbuzz solution in one line of code?"}]
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# Make the request
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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 the response
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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>
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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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# Set your model
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MODEL = "NousResearch/Hermes-3-Llama-3.1-8B"
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# Define your messages
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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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# Make the streaming request
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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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# Print each message in the streaming response
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for chunk in response:
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# The 'ChatCompletionChunk' object exposes the 'choices' attribute directly
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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>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>
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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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<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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<h2>Questions, Comments, Discussions</h2>
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