⭐️ Getting Started with Llama 2

It's been some time since Llama 2's celebrated launch and we've seen the dust settle a bit and real use cases come to life.

In this blog post, we answer frequently asked questions on Llama 2's capabilities and when should you be using it. Let's dive in!

What is Llama 2?

Llama 2 is an open-source large language model (LLM) developed by Meta. It is freely available for research and commercial purposes. Llama 2 is a family of LLMs, similar to OpenAI's GPT models and Google's PaLM models.

It can generate incredibly human-like responses by predicting the most plausible follow-on text using its neural network, which consists of billions of parameters.

It's gaining popularity as benchmarks and human tests both show it to be performing as well as the OpenAI models, with the added advantage of it being completely open source.

That means players like Perplexity can tweak and deploy a version of Llama 2 for you to try out with no restrictions! Neat.

Can I use Llama 2 instead of OpenAI's model?

Yes, you can use Llama 2 instead of OpenAI's model. Llama 2 is an open-source large language model (LLM) developed by Meta AI, and it is freely available for research and commercial use. Llama 2 has been shown to have higher performance than OpenAI's ChatGPT, but lower performance than OpenAI's GPT-4.

What are the differences between Llama 2 and OpenAI's models?

1. Size: Llama 2 is a much smaller model than OpenAI's flagship models.
2. Performance: Llama 2 has been shown to have slightly higher performance than OpenAI's ChatGPT, but lower performance than OpenAI's GPT-4.
3. Data: Llama 2 offers more up-to-date data than its OpenAI counterpart.
4. Availability: Llama 2 is open-source and freely available for research and commercial use, while OpenAI's models are not.

Overall, Llama 2 is a viable alternative to OpenAI's models, especially for those who require up-to-date data, prefer an open-source solution, and prioritize ethical considerations. However, OpenAI's models are still more powerful and widely used in the industry.

How do I use Llama 2?

You can try Llama 2's models on llama2.ai (hosted by Replicate), ChatNBX or via Perplexity.

Is there an API for Llama 2?

You can create an inference endpoint for Llama 2's models by deploying it to your hardware.

• Azure provides Llama2 support in it's model catalog
• If you prefer AWS, they have also announced support through SageMaker Jumpstart.
• The easiest way to get started would be through Anyscale's hosted endpoints which host the model for you in a fast & extremely cost-efficient environment.

curl "$OPENAI_API_BASE/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "meta-llama/Llama-2-70b-chat-hf",
    "messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Say 'test'."}],
    "temperature": 0.7
  }'

Are there multiple versions of Llama 2?

Yes, Llama 2 has pretrained models, fine-tuned chat models and code models made available by Meta.

Pre-trained Models: These models are not finetuned for chat or Q&A. They should be prompted so that the expected answer is the natural continuation of the prompt. There are 3 models in this class - llama-2-7b, llama-2-13b and the most capable llama-2-70b.

Fine-tuned Chat Models: The fine-tuned models were trained for dialogue applications. These also come in 3 variants - llama-2-7b-chat, llama-2-13b-chat and llama-2-70b-chat

Code Models: Code Llama is a code-specialized version of Llama 2 that was created by further training Llama 2 on its code-specific datasets. Meta released three sizes of Code Llama with 7B, 13B, and 34B parameters respectively.

The community has also made many more models available on top of Llama 2:

Uncensored models: If you want to turn off safe mode on Llamas, you can try these

• llama2-uncensored (Uncensored Llama2 model by George Sung and Jarrad Hope),
• Nous-Hermes-Llama2-13b (state-of-the-art language model fine-tuned on over 300,000 instructions),
• Wizard-Vicuna-13B-Uncensored(trained with a subset of the dataset - responses that contained alignment / moralizing were removed.)

Many more specialized models can be seen on the Ollama website.

Advanced Topics

Can I run Llama2 on my local machine?

Yes, you can run Llama 2 on your local machine. To do this, you need to follow these steps:

1. Install the required software: Make sure you have Python 3.8 or higher and Git installed on your system. You may also need a C++ compiler depending on your operating system.
2. Clone the Llama 2 repository: Clone the Llama 2 repository from Hugging Face and install the required Python dependencies.
3. Download the Llama 2 model: Download the Llama 2 model of your choice from the Meta AI website or Hugging Face, and place it in the appropriate directory.
4. Create a Python script: Create a Python script to interact with the Llama 2 model using the Hugging Face Transformers library or other available libraries like llama-cpp-python.
5. Run the script: Execute the Python script to interact with the Llama 2 model and generate text, translations, or answers to your questions.

For more detailed instructions and examples, you can refer to various guides available online, such as the ones on Replicate or Medium.

Can I fine-tune Llama2 on my own data?

Yes, you can fine-tune Llama 2 on your own dataset. Here's a quick guide to help you get started with the fine-tuning process:

Prerequisites:

• Machine Specifications: Ensure you have a machine equipped with a reasonably recent version of Python and CUDA. For a smooth experience, it's advisable to use Python 3.10 and CUDA 11.7 as utilized in the example.
• GPU Requirements: A GPU with at least 24GB of VRAM is recommended, such as A100, A10, or A10G. For fine-tuning the larger Llama 2 models, like the 13b and 70b, opting for the A100 is a smart choice to handle the substantial computational load.

Fine-Tuning Process:

The fine-tuning process is quite straightforward. Follow these essential steps to fine-tune Llama 2 on your data:

1. Preparation: Gather and preprocess your dataset for the fine-tuning task.
2. Configuration: Set up your machine and environment, ensuring all necessary software and hardware requirements are met.
3. Fine-Tuning: Launch the fine-tuning process using the appropriate commands and settings.
4. Evaluation: After fine-tuning, evaluate the model's performance on your tasks to ensure it meets your expectations.

For a comprehensive and detailed step-by-step guide on how to fine-tune Llama 2 on your own data, check out this helpful tutorial.