Running Local LLMs

A comprehensive guide to running Large Language Models (LLMs) on your local machine using various frameworks and tools.

Table of Contents

• Overview
• Requirements
• Frameworks and Tools
  • llama.cpp
  • Ollama
  • HuggingFace Transformers
  • HuggingFace Transformers - Quantized (BitsAndBytes)
  • TorchAO
  • vLLM
  • [GPT-NeoX] (#GPT-Neox)
  • [Triton] - (# Triton Inference Server TensorRT backend)
  • LM Studio
• Performance Comparison
• Contributing
• License

Overview

Running LLMs locally offers several advantages including privacy, offline access, and cost efficiency. This repository provides step-by-step guides for setting up and running LLMs using various frameworks, each with its own strengths and optimization techniques.

Requirements

General requirements for running LLMs locally:

• Hardware:
  • CPU: Modern multi-core processor (8+ cores recommended)
  • RAM: 16GB minimum, 32GB+ recommended
  • GPU: NVIDIA GPU with 8GB+ VRAM (16GB+ recommended for larger models)
  • Storage: 20GB+ free space (varies by model size)
• Software:
  • Python 3.8+
  • CUDA 11.7+ and cuDNN (for GPU acceleration)
  • Git

Specific requirements are listed in each framework section.

Frameworks and Tools

llama.cpp

llama.cpp is a C/C++ implementation of LLaMA that’s optimized for CPU and GPU inference.

Installation

# Clone the repository
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp

# Build the project
make

# Download and convert a model (example with TinyLlama)
python3 -m pip install torch numpy sentencepiece
python3 scripts/convert.py <path_to_hf_model>

# Quantize the model (optional)
./quantize <path_to_model>/ggml-model-f16.bin <path_to_model>/ggml-model-q4_0.bin q4_0

Usage

# Run inference
./main -m <path_to_model>/ggml-model-q4_0.bin -n 512 -p "Write a short poem about programming:"

Advantages

• Extremely memory-efficient through quantization
• Works well on CPU-only setups
• Supports various model architectures (LLaMA, Mistral, Falcon, etc.)
• Available as a library for integration into other applications

Ollama

Ollama provides an easy way to run open-source LLMs locally with a simple API.

Installation

# macOS/Linux
curl -fsSL https://ollama.com/install.sh | sh

# Windows
# Download from https://ollama.com/download/windows

Usage

# Pull a model
ollama pull mistral

# Run a model
ollama run mistral

# API usage
curl -X POST http://localhost:11434/api/generate -d '{
  "model": "mistral",
  "prompt": "What is computational linguistics?"
}'

Advantages

• Simplified setup and usage
• Integrated model library with one-command downloads
• REST API for easy integration
• Cross-platform support
• No Python environment needed

HuggingFace Transformers

HuggingFace Transformers is a popular library that provides thousands of pre-trained models.

Installation

pip install transformers torch

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_name = "meta-llama/Llama-2-7b-hf"  # Requires HF auth token
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Write a short story about:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Advantages

• Extensive model support
• Easy integration with PyTorch ecosystem
• Rich documentation and community support
• Seamless model switching

HuggingFace Transformers - Quantized (BitsAndBytes)

Quantization with BitsAndBytes allows running larger models with reduced memory requirements.

Installation

pip install transformers torch bitsandbytes accelerate

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

# Configure quantization
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True
)

# Load model with quantization
model_name = "meta-llama/Llama-2-13b-hf"  # Requires HF auth token
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=quantization_config,
    device_map="auto"
)

# Generate text
inputs = tokenizer("Explain quantum computing:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Advantages

• Run larger models on consumer hardware
• Minimal performance impact despite compression
• Compatible with most HuggingFace models
• 4-bit and 8-bit quantization options

TorchAO

TorchAO (PyTorch Ahead-of-Time Optimization) enables efficient inference through quantization and optimization techniques.

Installation

pip install torch torchao

Usage

import torch
import torchao
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_name = "meta-llama/Llama-2-7b-hf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Apply TorchAO optimizations
optimized_model = torchao.optimize(
    model,
    quantization=True,
    dtype=torch.float16,
    inplace=False
)

# Generate text
inputs = tokenizer("Explain how solar panels work:", return_tensors="pt")
outputs = optimized_model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Advantages

• Advanced quantization techniques
• Hardware-specific optimizations
• Compatible with PyTorch ecosystem
• Flexible configuration options

GPT-NeoX - (#GPT-Neox)

GPT-NeoX is a series of models developed by EleutherAI and built using a modified version of Megatron. It offers various sizes, ranging from 20B to 100B parameters.

Installation

pip install gpt-neo

Usage

from gpt_neo import GPTPredictor
from gpt_neo.utils import download_model

# Download the model (requires EleutherAI auth token)
download_model("gpt-neo-2.7B")  # Change the model size as
needed

# Load and initialize the model predictor
predictor = GPTPredictor.from_pretrained("gpt-neo-2.7B",
device="cuda:0")

# Generate text
inputs = "Write a short story about:"
outputs = predictor(inputs, max_length=100)
print(outputs["text"])

Advantages

• Large model capacity
• Open source and transparent development
• Supports multiple architectures (e.g., BERT, Megatron)
• Strong performance on various tasks

Triton Inference Server (TensorRT backend)

Triton Inference Server is an open-source, extensible, and customizable AI inferencing server developed by NVIDIA. It supports a wide variety of models, including LLMs like BERT, RoBERTa, DistilBert, and Electra, among others.

Installation

1. Install Triton Inference Server: Follow the instructions for [installing Triton Inference Server](https://github.com/NVIDIA/Triton/blob/main/docs/quicksServer](https://github.com/NVDIA/Triton/blob/main/docs/quickstarts/quickstart-docker.md).

Usage

1. Prepare the model: Convert your Hugging Face Transformers model to an ONNX format using [the ONNX model converter](https://github.com/onnx/onnx/blob/master/tools/onnxconverter](https//github.com/onnx/onnx/blob/master/tools/onnxtools/README.md).
2. Export the ONNX model for TensorRT: Use the TensorRT optimizer to convert your ONNX model to a TensorRT engine.
3. Deploy the model in Triton Inference Server: Create and configure a model_config.pbtxt file for your model. Then, create and register a Triton model server with the configuration file and the TensorRT engine.
4. Use the model in your application: Integrate Triton Inference Server into your application using the Triton Inference Client Library or any other method that suits your needs.

Advantages

• High performance through the use of TensorRT optimizations
• Flexible deployment options (e.g., on-premises, in the cloud)
• Supports a wide variety of models and customization
• Efficient handling of multiple models at scale

vLLM

vLLM is a high-throughput and memory-efficient inference engine.

Installation

pip install vllm

Usage

from vllm import LLM, SamplingParams

# Initialize the model
llm = LLM(model="meta-llama/Llama-2-7b-hf")  # Requires HF auth token

# Set sampling parameters
sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.95,
    max_tokens=100
)

# Generate text
prompts = ["Write a short story about space exploration:"]
outputs = llm.generate(prompts, sampling_params)

# Print generated text
for output in outputs:
    print(output.text)

Command-line usage

python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-2-7b-hf

Advantages

• PagedAttention for efficient memory usage
• Multi-GPU support with tensor parallelism
• OpenAI-compatible API
• High throughput for batch processing
• Efficient continuous batching

LM Studio

LM Studio is a desktop application for running local LLMs with a graphical interface.

Installation

1. Download the installer from https://lmstudio.ai/
2. Install and launch the application

Usage

1. Download models from the built-in model library
2. Configure inference parameters using the GUI
3. Chat with the model through the interface
4. Optionally expose an API server compatible with OpenAI

Advantages

• User-friendly GUI
• No coding required
• Built-in model discovery and management
• Visual parameter tuning
• Performance metrics visualization
• Compatible with various model formats

Performance Comparison

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.