How to Download a Model from Hugging Face (original) (raw)

Last Updated : 11 May, 2026

Hugging Face models are pre-trained machine learning models that you can directly download and plug into our applications for tasks like text classification, translation, summarization and more without training from scratch.

• It is an open-source AI platform best known for the transformers library and the Model Hub, which hosts thousands of ready-to-use models for NLP, vision and multimodal tasks.
• Model Hub lets you search, compare and use community and organisation models with just a few lines of code.
• Pre-trained models save you from collecting huge datasets and training deep networks from the beginning.
• They can be fine-tuned on our own data, giving you both speed and flexibility.

Importance of Using Pre-Trained Models

Pre-trained models are models that have already been trained on large datasets. Using them offers several advantages:

• **Saves time and computing: We don’t need to train from scratch on massive datasets.
• **State-of-the-art performance: Many models on Hugging Face are based on cutting edge research.
• **Easy to use: Just load the model and tokeniser with a few lines of code.
• **Customizable: You can fine tune them on our own dataset for our specific task.
• **Consistent APIs: Same pattern for different tasks and architectures.

Downloading a Model from Hugging Face

Step 1: Environment Setup

• We need to install the required packages and libraries.
• Run the following command in your command prompt Python `

pip install transformers pip install torch

`

Step 2: Choose a Model from the Model Hub

• Go to the Hugging Face Model Hub on the browser and we can choose any model based on our task from the wide range of pre trained models available.
• For example, we will use "bert-base-uncased" model.

Models from the Model Hub

Step 3: Download the Model and Tokenizer

We use AutoModelForSequenceClassification and AutoTokenizer from transformers to download and load the model.

Python `

from transformers import AutoModelForSequenceClassification, AutoTokenizer model_name = "bert-base-uncased" model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name)

`

**Output:

Downloading

Step 4: Saving and Loading the Model Locally

1. If we want to keep a copy for offline use or reuse, we can save our downloaded model into a local directory.

Python `

model.save_pretrained("./my_local_model") tokenizer.save_pretrained("./my_local_model")

`

**Output:

Saving the Model

2. To load the saved model from local directory, we perform the following step

Python `

from transformers import AutoModelForSequenceClassification, AutoTokenizer local_model = AutoModelForSequenceClassification.from_pretrained( "./my_local_model") local_tokenizer = AutoTokenizer.from_pretrained("./my_local_model")

`

Step 5: Use the Model

Let's use the model to verify if it is working or not by performing a simple text classification.

Python `

text = "I love using Hugging Face models for NLP!" inputs = local_tokenizer(text, return_tensors="pt") outputs = local_model(**inputs) print(outputs.logits)

`

**Output:

tensor([[-0.1011, 0.3558]], grad_fn=)

Applications

• Used for tasks like text classification, generation, translation and entity recognition
• Enables rapid prototyping and deployment without training from scratch
• Supports multiple domains including NLP, computer vision and audio

Limitations

While convenient, using pre trained models also comes with certain constraints.

• Large models require high memory and computational resources
• May need fine-tuning for task specific accuracy
• Performance depends on hardware and model compatibility