How to Install LLaMa 3 on Your Computer
Meta has introduced LLaMa 3, their latest Large Language Model. This model offers a dynamic tool for individuals, creators, researchers, and businesses. LLaMa 3 features models ranging from 8 billion to 70 billion parameters, providing diverse capabilities for various applications. This guide outlines the steps required to install LLaMa 3 on your computer.
Prerequisites
Before starting the installation, ensure your system meets these requirements:
- Python environment with PyTorch and CUDA: A functional Python environment with PyTorch and CUDA is necessary for effective model operation.
- Wget and md5sum: These tools are used for downloading and verifying model files.
- Git: Required to clone necessary repositories.
Step-by-Step Installation Guide
Step 1: Set Up Your Python Environment
Create a suitable Python environment using Conda or another virtual environment tool compatible with PyTorch and CUDA.
conda create -n llama3 python=3.8 conda activate llama3
Step 2: Install Required Packages
In your new environment, install the essential Python packages.
pip install torch transformers
Step 3: Clone the LLaMa 3 Repository
Clone the LLaMa 3 repository from Meta’s GitHub page.
git clone https://github.com/meta-llama/llama3.git cd llama3 pip install -e .
Step 4: Register and Download the Model
Register on Meta LLaMa Website
Visit the Meta LLaMa website and register for model access. This step ensures compliance with Meta’s licensing agreements.
Download the Model
After registration approval, you'll receive an email with a signed URL. This URL will expire after 24 hours or after a specified number of downloads.
-
Navigate to your downloaded LLaMa repository:
cd your-path-to-llama3
-
Run the download script:
chmod +x download.sh ./download.sh
Enter the URL from your email when prompted. Manually copy the link to avoid errors.
Step 5: Running the Model
Once the model is downloaded, run inference using one of the example scripts. Modify the parameters to match the model you downloaded.
torchrun --nproc_per_node=1 example_chat_completion.py \ --ckpt_dir Meta-Llama-3-8B-Instruct/ \ --tokenizer_path Meta-Llama-3-8B-Instruct/tokenizer.model \ --max_seq_len 512 --max_batch_size 6
Ensure to replace the checkpoint directory and tokenizer path with the appropriate paths.
Additional Considerations
- Model Parallel Values: Adjust the
--nproc_per_node
parameter based on the model's parallel requirements (e.g., MP value of 1 for 8B and 8 for 70B models). - Sequence Length and Batch Size: Modify
--max_seq_len
and--max_batch_size
based on your hardware capabilities and application needs.
Handling Issues and Feedback
If you experience bugs or other issues, Meta provides channels for reporting:
- Software bugs and model problems: Meta LLaMa Issues
- Risky content feedback: Meta Developers Feedback
- Security concerns: Facebook Whitehat
Installing LLaMa 3 involves setting up a Python environment, registering for access, downloading the model, and adjusting the inference parameters. These steps will help you utilize LLaMa 3's capabilities effectively.
(Edited on September 4, 2024)