NVIDIA has introduced the NV-Llama2-70B-RLHF-Chat model, a powerful 70 billion parameter generative language model designed for various chat applications, including question answering and summarization. In this article, you’ll learn how to deploy this model using the NVIDIA NeMo framework, ensuring an efficient and effective setup.

Understanding the Basics

Think of NV-Llama2-70B-RLHF-Chat as a highly knowledgeable librarian (the model) who can interactively assist you with inquiries by referencing a vast library (the training data) it has assimilated over time. Each time you consult this librarian, it understands your context and responds based on the ample knowledge it possesses, drawing from the most relevant resources in the library.

Steps to Run Inference

Before diving into the implementation, ensure your machine meets the following prerequisites:

• A minimum of 4 NVIDIA GPUs (40GB each) or 2 NVIDIA GPUs (80GB each).
• At least 300GB of free disk space.

Follow these steps to deploy and utilize the NV-Llama2-70B-RLHF-Chat model:

1. Sign Up for NVIDIA NeMo Framework

Register to get free access to the NVIDIA NeMo Framework container. If you do not have an NVIDIA NGC account, you will need to create one.

2. Generate NGC Key

If you do not already have an NVIDIA NGC key, sign in to NVIDIA NGC, select your organization/team, and click the Generate API key option. Make sure to save this key for the next steps.

3. Login to Docker

Login to Docker on your machine using the command:

docker login nvcr.io
Username: $oauthtoken
Password: Your Saved NGC Key

4. Download the Required Container

Use the following command to pull the required container:

docker pull nvcr.io/ea-bignlpga-participants/nemofw-inference:23.10

5. Download the Checkpoint

Clone the checkpoint repository and pull the data:

git lfs install
git clone https://huggingface.co/nvidia/NV-Llama2-70B-RLHF-Chat
cd NV-Llama2-70B-RLHF-Chat
git lfs pull

6. Convert Checkpoint to NeMo Format

Navigate to the model directory and convert the checkpoint:

cd NV-Llama2-70B-RLHF-Chat
tar -cvf NV-Llama2-70B-RLHF-Chat.nemo .
mv NV-Llama2-70B-RLHF-Chat.nemo ..
cd ..
rm -r NV-Llama2-70B-RLHF-Chat

7. Run the Docker Container

Run the Docker container with the following command:

docker run --gpus all -it --rm --shm-size=300g -p 8000:8000 -v $PWD/NV-Llama2-70B-RLHF-Chat.nemo:/opt/checkpoints/NV-Llama2-70B-RLHF-Chat.nemo -w /opt/NeMo nvcr.io/ea-bignlpga-participants/nemofw-inference:23.10

8. Start the Server in the Background

Within the container, execute the following command to start the server. This will deploy the model:

python scripts/deploy/deploy_triton.py --nemo_checkpoint /opt/checkpoints/NV-Llama2-70B-RLHF-Chat.nemo --model_type=llama --triton_model_name NV-Llama2-70B-RLHF-Chat --triton_http_address 0.0.0.0 --triton_port 8000 --num_gpus 2 --max_input_len 3072 --max_output_len 1024 --max_batch_size 1

9. Verify Server Status

Once the server is operational, look for completion messages in the terminal to confirm everything is ready for code execution.

Example Usage

Utilize the following Python examples to interact with the model in both single-turn and multi-turn scenarios.

python
from nemo.deploy import NemoQuery

# Example for a single-turn prompt
PROMPT_TEMPLATE = "System: {0}\nUser: {1}\nAssistant:"
nq = NemoQuery(url='http://localhost:8000', model_name='NV-Llama2-70B-RLHF-Chat')
output = nq.query_llm(prompts=[PROMPT_TEMPLATE.format("This is a chat...", "What did Michael Jackson achieve?")], max_output_token=256)
print(output[0][0])

Troubleshooting

Here are some common issues you might encounter while working with the NV-Llama2-70B-RLHF-Chat model, along with their solutions:

• Model Doesn’t Start: Ensure all prior steps were followed correctly, and confirm that your Docker service is running.
• Insufficient Disk Space: Verify your available disk space. Increase available space if necessary.
• Network Communication Errors: Make sure your network settings allow communication to the localhost and the ports being used.

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Conclusion

By following the steps outlined above, you can successfully deploy the NV-Llama2-70B-RLHF-Chat model for various applications. This model not only provides strong performance but also opens up numerous opportunities for innovation in AI-driven conversations. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.