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<h1>Prepare Python environment to download Hugging Face models</h1>
<p>This guide will walk you through setting up a Python environment named
<strong><code>empower</code></strong>, installing the necessary Hugging Face packages, and
downloading and using the <strong><code>microsoft/Phi-4-mini-instruct</code></strong> model on
a GNU/Linux system.</p>
<p>Follow this guide, or <a href="index.html">return to the main page</a> if needed.</p>
<hr />
<h2>1. Set Up Python Environment</h2>
<h3>Check Python Installation</h3>
<p>Ensure Python 3 is installed by running:</p>
<p><code>bash
python3 --version
</code></p>
<p>If Python is not installed, install it using your package manager. For example:</p>
<ul>
<li><p>On Debian/Ubuntu:</p>
<p><code>bash
sudo apt update
sudo apt install python3 python3-venv
</code></p></li>
<li><p>On Fedora:</p>
<p><code>bash
sudo dnf install python3
</code></p></li>
</ul>
<h3>Create a Virtual Environment</h3>
<p>Create a virtual environment named <strong><code>empower</code></strong>:</p>
<p><code>bash
python3 -m venv empower
</code></p>
<p>Activate the virtual environment:
<code>bash
source empower/bin/activate
</code></p>
<hr />
<h2>2. Install Hugging Face Packages</h2>
<p>Install the necessary Hugging Face packages to interact with models and the Hugging Face Hub.</p>
<h3>Install <code>transformers</code> and <code>huggingface_hub</code></h3>
<p>Run the following command to install both packages:</p>
<p><code>bash
pip install transformers huggingface_hub
</code></p>
<h3>Verify Installation</h3>
<p>Check if the packages are installed correctly:</p>
<p><code>bash
python3 -c "from transformers import pipeline; print('Transformers installed successfully!')"
python3 -c "from huggingface_hub import HfApi; print('Hugging Face Hub installed successfully!')"
</code></p>
<hr />
<h2>3. Download the <code>microsoft/Phi-4-mini-instruct</code> Model</h2>
<p>To download and use the <strong><code>microsoft/Phi-4-mini-instruct</code></strong> model, follow these steps.</p>
<h3>Using <code>huggingface-cli</code> to Download the Model</h3>
<p>Run the following command to download the model:</p>
<p><code>bash
huggingface-cli download microsoft/Phi-4-mini-instruct
</code></p>
<p>This will download the model files to your current directory.</p>
<hr />
<h2>4. Load and Use the Model in Python</h2>
<p>Once the model is downloaded, you can load and use it in your Python code.</p>
<h3>Example Code</h3>
<p>```python
from transformers import AutoModelForCausalLM, AutoTokenizer</p>
<h1>Load the model and tokenizer</h1>
<p>model_name = “microsoft/Phi-4-mini-instruct”
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)</p>
<h1>Generate text</h1>
<p>input_text = “What is the capital of France?”
inputs = tokenizer(input_text, return_tensors=“pt”)
outputs = model.generate(**inputs, max_length=50)</p>
<h1>Decode and print the output</h1>
<p>print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```</p>
<hr />
<h2>5. Summary of Commands</h2>
<table>
<thead>
<tr>
<th> Command </th>
<th> Description </th>
</tr>
</thead>
<tbody>
<tr>
<td> <code>python3 -m venv empower</code> </td>
<td> Create a virtual environment named <code>empower</code>. </td>
</tr>
<tr>
<td> <code>source empower/bin/activate</code> </td>
<td> Activate the <code>empower</code> environment. </td>
</tr>
<tr>
<td> <code>pip install transformers huggingface_hub</code> </td>
<td> Install Hugging Face packages. </td>
</tr>
<tr>
<td> <code>huggingface-cli download microsoft/Phi-4-mini-instruct</code> </td>
<td> Download the model. </td>
</tr>
</tbody>
</table>
<hr />
<p>Now you’re ready to use the <strong><code>microsoft/Phi-4-mini-instruct</code></strong> model in your Python projects on GNU/Linux! 🚀</p>
<h1>Proceed to next step</h1>
<p><a href="index.html">Proceed to next step.</a></p>
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