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<a href="https://uncloseai.com"><h1 class="unturf" style="font-family: 'ChunkFiveRegular';">uncloseai.</h1></a>
<p>Python Examples - Free LLM & TTS AI Service</p>
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<h2 id="python-intro">Python Examples</h2>
<p>This page demonstrates how to use the <strong>uncloseai.</strong> API endpoints with Python using the OpenAI client library. All examples use the same OpenAI-compatible API interface, making it easy to switch between different models and endpoints.</p>
<p><strong>Available Endpoints:</strong></p>
<ul>
<li><strong>Hermes:</strong> <code>https://hermes.ai.unturf.com/v1</code> - General purpose conversational AI</li>
<li><strong>Qwen 3 Coder:</strong> <code>https://qwen.ai.unturf.com/v1</code> - Specialized coding model</li>
<li><strong>TTS:</strong> <code>https://speech.ai.unturf.com/v1</code> - Text-to-speech generation</li>
</ul>
<h3 id="python-client">Python Client Installation</h3>
<p>To install the OpenAI package for Python, use <code>pip</code>:</p>
<pre><code class="bash">pip install openai==2.3.0</code></pre>
<h2 id="python-non-streaming">Non-Streaming Examples</h2>
<p>Non-streaming mode waits for the complete response before returning. This is simpler to use but provides no intermediate feedback during generation.</p>
<h3 id="python-hermes-non-stream">Using Hermes (General Purpose)</h3>
<pre><code class="python"># Python Fizzbuzz Example with Hermes
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
)
print(response.choices[0].message.content)
</code></pre>
<h3 id="python-qwen-non-stream">Using Qwen 3 Coder (Specialized for Coding)</h3>
<pre><code class="python"># Python Fizzbuzz Example with Qwen 3 Coder
from openai import OpenAI
client = OpenAI(base_url="https://qwen.ai.unturf.com/v1", api_key="choose-any-value")
MODEL = "hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M"
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)
</code></pre>
<h2 id="python-streaming">Streaming Examples</h2>
<p>Streaming mode returns chunks of the response as they are generated, providing real-time feedback. This is ideal for interactive applications and long responses.</p>
<h3 id="python-hermes-stream">Using Hermes (General Purpose)</h3>
<pre><code class="python"># Streaming response in Python with Hermes
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="")
</code></pre>
<h3 id="python-qwen-stream">Using Qwen 3 Coder (Specialized for Coding)</h3>
<pre><code class="python"># Streaming response in Python with Qwen 3 Coder
from openai import OpenAI
client = OpenAI(base_url="https://qwen.ai.unturf.com/v1", api_key="choose-any-value")
MODEL = "hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M"
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="")
</code></pre>
<h2 id="python-tts">Text-to-Speech Example</h2>
<p>Generate audio speech from text using the TTS endpoint. The audio is saved as an MP3 file.</p>
<pre><code class="python"># 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 grow something people love!"
) as response:
response.stream_to_file("speech.mp3")
</code></pre>
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