### Install Dependencies

Source: https://github.com/huggingface/blog/blob/main/sentiment-analysis-python.md

Installs the necessary libraries for the tutorial: datasets, transformers, and huggingface_hub.

```bash
!pip install datasets transformers huggingface_hub
```

--------------------------------

### Installing GUI Dependencies and Starting Server

Source: https://github.com/huggingface/blog/blob/main/cost-efficient-rag-applications-with-intel.md

Commands to install frontend dependencies and start the GUI server.

```bash
npm install
nohup npm run dev &
```

--------------------------------

### Install ScreenEnv and Try Examples

Source: https://github.com/huggingface/blog/blob/main/screenenv.md

Command-line instructions to install ScreenEnv and run its examples.

```bash
# Install ScreenEnv
pip install screenenv

# Try the examples
git clone git@github.com:huggingface/screenenv.git
cd screenenv
python -m examples.desktop_agent
# use 'sudo -E python -m examples.desktop_agent` if you're not in 'docker' group
```

--------------------------------

### Set up PopTorch for the IPU

Source: https://github.com/huggingface/blog/blob/main/graphcore-getting-started.md

Commands to create and activate a PopTorch environment and install the PopTorch wheel.

```bash
$ virtualenv -p python3 ~/workspace/poptorch_env
$ source ~/workspace/poptorch_env/bin/activate
$ pip3 install -U pip
$ pip3 install /opt/gc/poplar_sdk-ubuntu_18_04-2.3.0+774-b47c577c2a/poptorch-<sdk-version>.whl
```

--------------------------------

### Wall-X Installation and Training Example

Source: https://github.com/huggingface/blog/blob/main/lerobot-release-v050.md

Commands to install the Wall-X extra and train a Wall-X policy.

```bash
pip install lerobot[wall_x]
lerobot-train \
  --policy.type=wall_x \
  --dataset.repo_id=lerobot/aloha_sim_insertion_human
```

--------------------------------

### X-VLA Installation and Training Example

Source: https://github.com/huggingface/blog/blob/main/lerobot-release-v050.md

Commands to install the X-VLA extra and train an X-VLA policy.

```bash
pip install lerobot[xvla]
lerobot-train \
  --policy.type=xvla \
  --dataset.repo_id=lerobot/bimanual-so100-handover-cube
```

--------------------------------

### Install Text Classification Example Requirements

Source: https://github.com/huggingface/blog/blob/main/getting-started-habana.md

Navigates to the text classification example directory and installs its dependencies.

```bash
cd examples/text-classification
pip install -r requirements.txt
```

--------------------------------

### Install necessary libraries

Source: https://github.com/huggingface/blog/blob/main/notebooks/85_sentiment_analysis_twitter.ipynb

Installs the tweepy, matplotlib, and wordcloud libraries, with '-q' for quiet installation.

```python
!pip install -q tweepy matplotlib wordcloud
```

--------------------------------

### Installing Frontend GUI Components

Source: https://github.com/huggingface/blog/blob/main/cost-efficient-rag-applications-with-intel.md

Commands to install Node.js and npm for the frontend GUI.

```bash
sudo apt-get install npm && \
    npm install -g n && \
    n stable && \
    hash -r && \
    npm install -g npm@latest
```

--------------------------------

### Example Texts for Embedding

Source: https://github.com/huggingface/blog/blob/main/getting-started-with-embeddings.md

A list of example questions to be embedded using the configured model and API.

```python
texts = ["How do I get a replacement Medicare card?",
        "What is the monthly premium for Medicare Part B?",
        "How do I terminate my Medicare Part B (medical insurance)?",
        "How do I sign up for Medicare?",
        "Can I sign up for Medicare Part B if I am working and have health insurance through an employer?",
        "How do I sign up for Medicare Part B if I already have Part A?",
        "What are Medicare late enrollment penalties?",
        "What is Medicare and who can get it?",
        "How can I get help with my Medicare Part A and Part B premiums?",
        "What are the different parts of Medicare?",
        "Will my Medicare premiums be higher because of my income?",
        "What is TRICARE ?",
        "Should I sign up for Medicare Part B if I have Veterans' Benefits?"]

output = query(texts)
```

--------------------------------

### Server Setup Steps

Source: https://github.com/huggingface/blog/blob/main/intel-sapphire-rapids-inference.md

These commands set up the necessary software environment on the test servers, including PyTorch, Intel Extension for PyTorch, and Transformers. It also includes optional performance enhancements like libtcmalloc.

```bash
sudo apt-get update

# Add libtcmalloc for extra performance
sudo apt install libgoogle-perftools-dev -y
export LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libtcmalloc.so"

sudo apt-get install python3-pip -y
pip install pip --upgrade
export PATH=/home/ubuntu/.local/bin:$PATH
pip install virtualenv
virtualenv inference_env
source inference_env/bin/activate

pip3 install torch==1.13.0 -f https://download.pytorch.org/whl/cpu
pip3 install intel_extension_for_pytorch==1.13.0 -f https://developer.intel.com/ipex-whl-stable-cpu
pip3 install transformers

# Only needed on the r7iz instance
pip3 install optimum[intel]
```

--------------------------------

### Install Dependencies

Source: https://github.com/huggingface/blog/blob/main/sentiment-analysis-twitter.md

Installs the necessary Python libraries: transformers, tweepy, matplotlib, and wordcloud.

```python
!pip install -q transformers tweepy matplotlib wordcloud
```

--------------------------------

### Installation commands

Source: https://github.com/huggingface/blog/blob/main/hf-bitsandbytes-integration.md

Commands to install the necessary libraries for bitsandbytes integration with Hugging Face Transformers.

```bash
pip install accelerate
pip install bitsandbytes
pip install git+https://github.com/huggingface/transformers.git
```

--------------------------------

### Install transformers

Source: https://github.com/huggingface/blog/blob/main/notebooks/trainer/01_text_classification.ipynb

Installs the transformers library from master and clones the repository to get utility files.

```shell
!pip transformers==3.5.1
```

--------------------------------

### Environment Setup and Local Training Check

Source: https://github.com/huggingface/blog/blob/main/intel-sapphire-rapids.md

Sets up the environment and installs dependencies, then runs a local training job as a sanity check.

```bash
source ~/cluster_env/bin/activate
cd ~/transformers/examples/pytorch/question-answering
pip3 install -r requirements.txt
```

```bash
export LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libtcmalloc.so"
python run_qa.py --model_name_or_path distilbert-base-uncased \
--dataset_name squad --do_train --do_eval --per_device_train_batch_size 32 \
--num_train_epochs 1  --output_dir /tmp/debug_squad/ \
--use_ipex --bf16 --no_cuda
```

--------------------------------

### Install the library

Source: https://github.com/huggingface/blog/blob/main/hello-hf-kernels.md

Installs the kernels library and its dependencies.

```bash
pip install kernels torch numpy
```

--------------------------------

### Install huggingface-hub

Source: https://github.com/huggingface/blog/blob/main/notebooks/80_getting_started_with_embeddings.ipynb

Installs the huggingface-hub library, which is necessary for interacting with the Hugging Face Hub.

```python
%%capture
!pip install huggingface-hub
```

--------------------------------

### Install Optimum Habana from Source

Source: https://github.com/huggingface/blog/blob/main/getting-started-habana.md

Installs the Optimum Habana package from its source code.

```bash
cd optimum-habana
pip install .
```

--------------------------------

### Install Optimum Intel and Transformers

Source: https://github.com/huggingface/blog/blob/main/openvino-vlm.md

Installs the necessary libraries for optimizing and running models with OpenVINO.

```bash
pip install optimum-intel[openvino] transformers==4.52.*

```

--------------------------------

### Install Git LFS

Source: https://github.com/huggingface/blog/blob/main/sentiment-analysis-python.md

Installs git-lfs, which is required for using git in model repositories.

```bash
!apt-get install git-lfs
```

--------------------------------

### Compiling and running a ggml example on Ubuntu

Source: https://github.com/huggingface/blog/blob/main/introduction-to-ggml.md

This code snippet demonstrates how to install build dependencies, clone the ggml repository, compile a simple example, and run it.

```shell
# Start by installing build dependencies
# "gdb" is optional, but is recommended
sudo apt install build-essential cmake git gdb

# Then, clone the repository
git clone https://github.com/ggerganov/ggml.git
cd ggml

# Try compiling one of the examples
cmake -B build
cmake --build build --config Release --target simple-ctx

# Run the example
./build/bin/simple-ctx
```

--------------------------------

### Install sentence-transformers library

Source: https://github.com/huggingface/blog/blob/main/notebooks/80_getting_started_with_embeddings.ipynb

Installs or upgrades the sentence-transformers library, which provides utilities for semantic search.

```python
%%capture
!pip install -U sentence-transformers
```

--------------------------------

### Install Retry Package

Source: https://github.com/huggingface/blog/blob/main/notebooks/80_getting_started_with_embeddings.ipynb

Installs the 'retry' package, which is used for handling API request retries.

```python
%%capture
!pip install retry
```

--------------------------------

### Install necessary libraries

Source: https://github.com/huggingface/blog/blob/main/notebooks/115_introducing_contrastive_search.ipynb

Installs PyTorch and the Hugging Face Transformers library.

```bash
! pip install torch
! pip install "transformers>=4.24.0"
```

--------------------------------

### Install Dependencies

Source: https://github.com/huggingface/blog/blob/main/sentiment-analysis-python.md

Installs the necessary Python libraries for the tutorial: transformers, tweepy, wordcloud, and matplotlib.

```python
!pip install -q transformers tweepy wordcloud matplotlib
```

--------------------------------

### Set up the Poplar SDK Environment

Source: https://github.com/huggingface/blog/blob/main/graphcore-getting-started.md

Commands to set environment variables for Graphcore tools and Poplar libraries.

```bash
$ cd /opt/gc/poplar_sdk-ubuntu_18_04-2.3.0+774-b47c577c2a/
$ source poplar-ubuntu_18_04-2.3.0+774-b47c577c2a/enable.sh
$ source popart-ubuntu_18_04-2.3.0+774-b47c577c2a/enable.sh
```

--------------------------------

### Install datasets library

Source: https://github.com/huggingface/blog/blob/main/notebooks/80_getting_started_with_embeddings.ipynb

Installs the Hugging Face datasets library, which is used for loading and processing datasets.

```python
%%capture
!pip install datasets
```

--------------------------------

### Getting Started with Aya Vision

Source: https://github.com/huggingface/blog/blob/main/aya-vision.md

Instructions and links to get started with the Aya Vision model, including downloading weights, trying it on Hugging Face Space or WhatsApp, and a Colab example.

```text
Download weights and datasets from the [Aya Vision collection](https://huggingface.co/collections/CohereForAI/c4ai-aya-vision-67c4ccd395ca064308ee1484) on Hugging Face.  
Try Aya Vision using our [Hugging Face Space](https://huggingface.co/spaces/CohereForAI/aya_expanse) or text it on [Whatsapp](https://cohere.com/research/aya/whatsapp)  
Build on Aya using our [colab example](https://colab.research.google.com/drive/1jHYi8WVyRE6-imTRA37h_9txjrr8WNZd?usp=sharing).
```

--------------------------------

### Environment Setup Commands

Source: https://github.com/huggingface/blog/blob/main/gpt-oss-on-intel-xeon.md

Commands to clone the transformers repository, checkout a specific commit, build a Docker image, and run the container for setting up the environment.

```bash
$ git clone https://github.com/huggingface/transformers.git
$ cd transformers/
$ git checkout 26b65fb5168f324277b85c558ef8209bfceae1fe
$ cd docker/transformers-intel-cpu/
$ sudo docker build . -t <your_docker_image_tag>
$ sudo docker run -it --rm --privileged -v /home/<your_home_folder>:/workspace <your_docker_image_tag> /bin/bash
```

```bash
pip install git+https://github.com/huggingface/transformers.git@26b65fb5168f324277b85c558ef8209bfceae1fe
pip install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
```

--------------------------------

### Install Optimum Graphcore

Source: https://github.com/huggingface/blog/blob/main/graphcore-getting-started.md

Command to install the Optimum Graphcore package within the activated PopTorch virtual environment.

```bash
(poptorch_env) user@host:~/workspace/poptorch_env$ pip3 install optimum[graphcore] optuna
```

--------------------------------

### API Key Setup

Source: https://github.com/huggingface/blog/blob/main/upskill.md

Environment variables to set for API keys required by upskill.

```shell
export ANTHROPIC_API_KEY=sk-ant-...
export HF_TOKEN=hf_...
```

--------------------------------

### Install transformers and run sentiment analysis

Source: https://github.com/huggingface/blog/blob/main/sentiment-analysis-python.md

This code snippet installs the transformers library, initializes a sentiment analysis pipeline with the default model, and analyzes a list of texts.

```python
pip install -q transformers
from transformers import pipeline
sentiment_pipeline = pipeline("sentiment-analysis")
data = ["I love you", "I hate you"]
sentiment_pipeline(data)
```

--------------------------------

### Quick test run for SFT

Source: https://github.com/huggingface/blog/blob/main/hf-skills-training.md

Example command for a quick test run to SFT Qwen-0.6B with a small subset of a custom dataset.

```bash
Do a quick test run to SFT Qwen-0.6B with 100 examples of my-org/support-conversations.
```

--------------------------------

### Install Dependencies

Source: https://github.com/huggingface/blog/blob/main/openvino.md

Creates a virtual environment and installs the necessary Python packages for quantization.

```bash
virtualenv openvino
source openvino/bin/activate
pip install pip --upgrade
pip install optimum[openvino,nncf] torchvision evaluate
```

--------------------------------

### MLX inference with vision

Source: https://github.com/huggingface/blog/blob/main/gemma3n.md

Example command to get started with vision inference using MLX.

```shell
python -m mlx_vlm.generate --model google/gemma-3n-E4B-it --max-tokens 100 --temperature 0.5 --prompt "Describe this image in detail." --image https://huggingface.co/datasets/ariG23498/demo-data/resolve/main/airplane.jpg
```

--------------------------------

### Running the demo

Source: https://github.com/huggingface/blog/blob/main/introduction-to-ggml.md

This snippet shows the command to run the demo and its expected output.

```bash
./build/bin/demo

Expected result:

```
mul mat (4 x 3) (transposed result):
[ 60.00 55.00 50.00 110.00
 90.00 54.00 54.00 126.00
 42.00 29.00 28.00 64.00 ]
```
```

--------------------------------

### Displaying Sample Tweets by Sentiment

Source: https://github.com/huggingface/blog/blob/main/sentiment-analysis-twitter.md

Shows one example tweet for each sentiment category (Positive, Neutral, Negative).

```python
display(df[df["sentiment"] == 'Positive'].head(1))
display(df[df["sentiment"] == 'Neutral'].head(1))
display(df[df["sentiment"] == 'Negative'].head(1))
```

--------------------------------

### Launching `transformers serve`

Source: https://github.com/huggingface/blog/blob/main/welcome-openai-gpt-oss.md

Command to start the `transformers serve` utility for local model experimentation.

```shell
transformers serve
```