Comparison of Different Libraries for Running LLM Models Locally

In today's world, where large language models (LLM) are becoming increasingly popular, many people are looking for ways to run these models locally. This allows avoiding dependence on cloud services, increasing privacy, and having full control over data. In this article, we will compare several popular libraries that allow running LLM models locally.

1. Hugging Face Transformers

Hugging Face Transformers is one of the most popular libraries for working with LLM models. It allows you to easily download and run various models, including those pre-trained on large datasets.

Advantages:

• Ease of use
• Support for many models
• Ability to customize models

Disadvantages:

• May require a lot of RAM
• Some models may be difficult to run on weaker computers

Code Example:

from transformers import pipeline

# Running the text generation model
generator = pipeline('text-generation', model='gpt2')

# Generating text
result = generator("When will spring come, ", max_length=50)
print(result)

2. Ollama

Ollama is a new library that allows running LLM models locally in a simple and efficient way. It allows you to easily download and run various models, including those pre-trained on large datasets.

Advantages:

• Ease of use
• Support for many models
• Ability to customize models

Disadvantages:

• May require a lot of RAM
• Some models may be difficult to run on weaker computers

Code Example:

# Installing Ollama
curl -fsSL https://raw.githubusercontent.com/jmorganca/ollama/main/install.sh | sh

# Downloading the model
ollama pull llama2

# Running the model
ollama run llama2

3. LM Studio

LM Studio is a tool that allows running LLM models locally in a simple and intuitive way. It allows you to easily download and run various models, including those pre-trained on large datasets.

Advantages:

• Ease of use
• Support for many models
• Ability to customize models

Disadvantages:

• May require a lot of RAM
• Some models may be difficult to run on weaker computers

Code Example:

# Installing LM Studio
# Download and run the application from the official website

4. vLLM

vLLM is a library that allows running LLM models locally in an efficient and scalable way. It allows you to easily download and run various models, including those pre-trained on large datasets.

Advantages:

• High performance
• Scalability
• Support for many models

Disadvantages:

• May require a lot of RAM
• Some models may be difficult to run on weaker computers

Code Example:

from vllm import LLM

# Running the model
llm = LLM(model='facebook/opt-1.3b')

# Generating text
outputs = llm.generate(prompts=["When will spring come, "], max_length=50)
print(outputs)

Summary

In this article, we compared four popular libraries for running LLM models locally: Hugging Face Transformers, Ollama, LM Studio, and vLLM. Each has its advantages and disadvantages, so the choice of the appropriate library depends on specific needs and conditions.

If you are looking for simplicity and ease of use, Hugging Face Transformers and LM Studio are good options. If performance and scalability are important to you, vLLM is the best choice. If you want to run LLM models locally in a simple and efficient way, Ollama is a good choice.

Regardless of the choice, running LLM models locally provides many benefits, including greater privacy and control over data. Therefore, it is worth considering using one of these libraries if you want to use LLM models in a local way.