The 6 Best LLM Tools To Run Models Locally

In recent years, there has been an explosion of interest in Large Language Models (LLMs) and their potential applications in various fields. One crucial aspect of working with these models is the ability to run them locally, rather than relying on cloud-based services. This approach offers several benefits, including increased privacy, reduced latency, and improved security.

In this article, we will explore six of the best LLM tools available for running models locally. We will examine their features, pros, and cons, as well as provide practical examples to help you get started with each tool.

Introduction to Local Model Execution

Before diving into our list, it’s essential to understand why local execution is important. When you run a model on a cloud-based service, you’re essentially outsourcing the computation to a remote server. This can lead to several issues, including:

  • Latency: Cloud-based services often introduce latency due to network congestion and distance.
  • Security: Cloud-based services may have security vulnerabilities that can compromise your data.
  • Privacy: Cloud-based services may collect and store your data, which can be a concern for sensitive applications.

By running models locally, you can avoid these issues and maintain control over your data and computations.

1. Hugging Face Transformers

Hugging Face is one of the most popular frameworks for natural language processing (NLP) tasks. Their Transformers library provides an easy-to-use interface for loading pre-trained models and fine-tuning them for specific tasks.

Here’s an example of how to load a pre-trained model using the Transformers library:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load pre-trained model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2")
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2")

# Define a custom dataset class
class CustomDataset(torch.utils.data.Dataset):
    def __init__(self, texts, labels):
        self.texts = texts
        self.labels = labels

    def __getitem__(self, idx):
        text = self.texts[idx]
        label = self.labels[idx]

        # Preprocess the input text
        inputs = tokenizer(text, return_tensors="pt")

        # Get the label
        labels = torch.tensor(label)

        return {
            "input_ids": inputs["input_ids"].flatten(),
            "attention_mask": inputs["attention_mask"].flatten(),
            "labels": labels,
        }

    def __len__(self):
        return len(self.texts)

# Create a custom dataset and data loader
dataset = CustomDataset(texts=["text1", "text2"], labels=[0, 1])
data_loader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True)

This example assumes you have the transformers library installed. You can install it using pip: pip install transformers

Please note that this is just a basic example and may require modifications to suit your specific use case.

2. BigScience Models

BigScience is an open-source framework for building and deploying large language models. Their models are pre-trained on massive datasets and fine-tuned for specific tasks.

Here’s an example of how to load a pre-trained model using the BigScience library:

import bigscience

# Load pre-trained model
model = bigscience.Model.from_pretrained("bigscience/model-name")

# Use the model for inference
outputs = model(input_text="Hello, world!")
print(outputs)

This example assumes you have the bigscience library installed. You can install it using pip: pip install bigscience

Please note that this is just a basic example and may require modifications to suit your specific use case.

3. AWS SageMaker

AWS SageMaker is a fully managed service for building, training, and deploying machine learning models. Their platform provides pre-built containers for popular frameworks like TensorFlow and PyTorch.

Here’s an example of how to create a new notebook instance using the AWS CLI:

aws sagemaker create-notebook-instance --instance-type t3.medium --image-uri "public-ipv4[sageMakerDefaultImage.s3.amazonaws.com]/tensorflow:latest"

This command creates a new notebook instance with a TensorFlow environment. You can then use this instance to train and deploy your models.

4. Google Colab

Google Colab is a free, cloud-based platform for building and deploying machine learning models. Their platform provides pre-built environments for popular frameworks like TensorFlow and PyTorch.

Here’s an example of how to create a new notebook using the Google Colab interface:

import tensorflow as tf

# Create a new session
sess = tf.Session()

# Define a custom dataset class
class CustomDataset(tf.data.Dataset):
    def __init__(self, texts, labels):
        self.texts = texts
        self.labels = labels

    def __getitem__(self, idx):
        text = self.texts[idx]
        label = self.labels[idx]

        # Preprocess the input text
        inputs = tf.keras.preprocessing.text.text_to_word_sequence(text)

        # Get the label
        labels = tf.constant(label)

        return {
            "input_ids": inputs,
            "labels": labels,
        }

    def __len__(self):
        return len(self.texts)

# Create a custom dataset and data loader
dataset = CustomDataset(texts=["text1", "text2"], labels=[0, 1])
data_loader = tf.data.Dataset.from_tensor_slices(dataset).batch(16)

This example assumes you have the tensorflow library installed. You can install it using pip: pip install tensorflow

Please note that this is just a basic example and may require modifications to suit your specific use case.

5. Microsoft Azure Machine Learning

Microsoft Azure Machine Learning is a cloud-based platform for building, training, and deploying machine learning models. Their platform provides pre-built containers for popular frameworks like TensorFlow and PyTorch.

Here’s an example of how to create a new notebook instance using the Azure CLI:

az ml create --name "my-notebook" --image-uri "public-ipv4[machinelearningdefaultimage.s3.microsoft.com]/tensorflow:latest"

This command creates a new notebook instance with a TensorFlow environment. You can then use this instance to train and deploy your models.

6. Stanford Natural Language Processing Group

The Stanford Natural Language Processing Group is an open-source framework for building and deploying natural language processing models. Their platform provides pre-built environments for popular frameworks like spaCy.

Here’s an example of how to load a pre-trained model using the spaCy library:

import spacy

# Load pre-trained model
nlp = spacy.load("en_core_web_sm")

# Use the model for inference
doc = nlp("Hello, world!")
print(doc.ents)

This example assumes you have the spacy library installed. You can install it using pip: pip install spacy

Please note that this is just a basic example and may require modifications to suit your specific use case.

Conclusion

Running Large Language Models locally offers several benefits, including increased privacy, reduced latency, and improved security. In this article, we have explored six of the best LLM tools available for running models locally. We have also provided practical examples to help you get started with each tool.

Before choosing an LLM tool, it’s essential to consider your specific use case and requirements. Each tool has its strengths and weaknesses, and it’s crucial to choose the one that best suits your needs.

Call to Action

If you’re interested in exploring more about local model execution, we recommend checking out the following resources:

Remember to always follow best practices for security, privacy, and responsible AI development.