R1 1776 is an advanced reasoning model built on the DeepSeek-R1 framework and fine-tuned by Perplexity AI for unrestricted, factual, and unbiased responses. With 671 billion parameters, this model excels in logical reasoning, problem-solving, and conversational accuracy. Unlike conventional models, R1 1776 is designed to provide uncensored information across a broad range of topics while preserving its mathematical, analytical, and critical thinking abilities.
Key Features:
✅ Unrestricted Knowledge Access – Offers factual insights without censorship
✅ Advanced Reasoning – Maintains high accuracy in problem-solving and logic-based tasks
✅ Efficient Performance – Fine-tuned to retain core reasoning capabilities without compromising output quality
✅ Multilingual Understanding – Processes and responds to queries across diverse languages
Optimized for research, analysis, and open-ended discussions, R1 1776 is a powerful tool for those seeking accurate, in-depth, and well-structured information on complex subjects.
Model Resource
Hugging Face
Link : https://huggingface.co/perplexity-ai/r1-1776
Ollama
Link: https://ollama.com/library/r1-1776
Minimum GPU Configuration (For Inference Only)
- Single GPU Setup (May require model offloading to CPU)
- GPU: 1x NVIDIA A100 80GB / RTX 6000 Ada 48GB (Offloading required)
- vRAM: 48GB+
- CPU: 16 vCPUs (Recommended)
- RAM: 64GB+
- Storage: 200GB+ NVMe SSD
- Framework: PyTorch +
transformers (Accelerate enabled)
- Torch Precision:
bfloat16 / float16
Performance: Slow but works with offloading.
Recommended GPU Configuration (For Fast Inference & Light Fine-Tuning)
- Multi-GPU Setup (Best for speed)
- GPU: 2x NVIDIA A100 80GB / 2x H100 80GB
- vRAM: 160GB (combined)
- CPU: 32 vCPUs+
- RAM: 128GB+
- Storage: 1TB NVMe SSD
- Parallelization: Fully Sharded Data Parallel (FSDP) / ZeRO Offloading
Performance: Fast inference and can handle fine-tuning.
High-End GPU Configuration (For Training & Large Batch Inference)
- Cluster / Enterprise Level
- GPU: 4x H100 SXM 80GB
- vRAM: 320GB (Combined)
- CPU: 64 vCPUs+
- RAM: 256GB+
- Storage: 2TB NVMe SSD
Step-by-Step Process to Install R1-1776 Model Locally
For the purpose of this tutorial, we will use a GPU-powered Virtual Machine offered by NodeShift; however, you can replicate the same steps with any other cloud provider of your choice. NodeShift provides the most affordable Virtual Machines at a scale that meets GDPR, SOC2, and ISO27001 requirements.
Step 1: Sign Up and Set Up a NodeShift Cloud Account
Visit the NodeShift Platform and create an account. Once you’ve signed up, log into your account.
Follow the account setup process and provide the necessary details and information.
Step 2: Create a GPU Node (Virtual Machine)
GPU Nodes are NodeShift’s GPU Virtual Machines, on-demand resources equipped with diverse GPUs ranging from H100s to A100s. These GPU-powered VMs provide enhanced environmental control, allowing configuration adjustments for GPUs, CPUs, RAM, and Storage based on specific requirements.
Navigate to the menu on the left side. Select the GPU Nodes option, create a GPU Node in the Dashboard, click the Create GPU Node button, and create your first Virtual Machine deployment.
Step 3: Select a Model, Region, and Storage
In the “GPU Nodes” tab, select a GPU Model and Storage according to your needs and the geographical region where you want to launch your model.
We will use 1x RTX A6000 GPU for this tutorial to achieve the fastest performance. However, you can choose a more affordable GPU with less VRAM if that better suit your requirements.
Step 4: Select Authentication Method
There are two authentication methods available: Password and SSH Key. SSH keys are a more secure option. To create them, please refer to our official documentation.
Step 5: Choose an Image
Next, you will need to choose an image for your Virtual Machine. We will deploy R1-1776 on an NVIDIA Cuda Virtual Machine. This proprietary, closed-source parallel computing platform will allow you to install R1-1776 on your GPU Node.
After choosing the image, click the ‘Create’ button, and your Virtual Machine will be deployed.
Step 6: Virtual Machine Successfully Deployed
You will get visual confirmation that your node is up and running.
Step 7: Connect to GPUs using SSH
NodeShift GPUs can be connected to and controlled through a terminal using the SSH key provided during GPU creation.
Once your GPU Node deployment is successfully created and has reached the ‘RUNNING’ status, you can navigate to the page of your GPU Deployment Instance. Then, click the ‘Connect’ button in the top right corner.
Now open your terminal and paste the proxy SSH IP or direct SSH IP.
Next, if you want to check the GPU details, run the command below:
nvidia-smi
Step 8: Install Ollama
After connecting to the terminal via SSH, it’s now time to install Ollama from the official Ollama website.
Website Link: https://ollama.com/
Run the following command to install the Ollama:
curl -fsSL https://ollama.com/install.sh | sh
Step 9: Serve Ollama
Run the following command to host the Ollama so that it can be accessed and utilized efficiently:
ollama serve
Now, “Ollama is running.”
Step 10: Select R1-1776 Model
Link: https://ollama.com/library/r1-1776:70b
R1 1776 model is available in only two size: 70B and 671B. We will run it on our GPU virtual machine.
Step 11: Connect with SSH
Now, open a new tab in the terminal and reconnect using SSH.
Step 12: Check Commands
Run, the following command to see a list of available commands:
ollama
Step 13: Check Available Models
Run the following command to check if the downloaded model are available:
ollama list
Step 14: Pull R1 1776 70B Model
Run the following command to pull the R1 1776 70B model:
ollama pull r1-1776:70b
Step 15: Run R1 1776 70B Model
Now, you can run the model in the terminal using the following command and interact with your model:
ollama run r1-1776:70b
This guide walks you through the installation and deployment of the R1-1776 70B model on a NodeShift GPU Virtual Machine using Ollama. The R1-1776 model is available in two sizes: 70B and 671B. In this guide, we installed the 70B version, configured the environment, and successfully ran the model. Next, we will proceed with installing the 671B parameter version of R1-1776, which follows the exact same process as outlined above, except for selecting a higher GPU configuration that meets the computational requirements for the 671B version.
Step 1: Select a Model, Region, and Storage
In the “GPU Nodes” tab, select a GPU Model and Storage according to your needs and the geographical region where you want to launch your model.
We will use 2x H100 GPU for this model to achieve the fastest performance. However, you can choose a more affordable GPU with less VRAM if that better suit your requirements.
Step 2: Pull R1 1776 671B Model
Run the following command to pull the R1 1776 671B model:
ollama pull r1-1776:671b
Step 3: Run R1 1776 671B Model
Now, you can run the model in the terminal using the following commands and interact with your model:
ollama run r1-1776:671b
Note: This is a step-by-step guides for interacting with your model. It covers the first method for installing R1-1776 locally using Ollama and running it in the terminal.
Option 2: Using Open WebUI
- Set Up Open WebUI:
Follow our Open WebUI Setup Guide to configure the interface. Ensure all dependencies are installed and the environment is correctly set up.
- Refresh the Interface:
Confirm that the R1-1776 model has been downloaded and is visible in the list of available models on the Open WebUI.
- Select Your Model:
Choose the R1-1776 model from the list. This model is available in a single size.
- Start Interaction:
Begin using the model by entering your queries in the interface.
Option 3: Using Hugging Face and Jupyter Notebook
Follow our Jupyter Notebook Setup Guide to configure your notebook environment. Ensure that all required dependencies are installed and that your Jupyter Notebook is set up correctly for optimal use.
When choosing an image for your Virtual Machine, select the Jupyter Notebook image. This open-source platform allows you to install and run the R1-1776 on your GPU node. By running this model on a Jupyter Notebook, you can avoid using the terminal, simplifying the process and reducing setup time. This approach enables you to configure the model in just a few steps and minutes.
Note: NodeShift provides multiple image template options, such as TensorFlow, PyTorch, NVIDIA CUDA, Deepo, Whisper ASR Webservice, and Jupyter Notebook. With these options, you don’t need to install additional libraries or packages to run Jupyter Notebook. You can start Jupyter Notebook in just a few simple clicks.
Link : https://huggingface.co/perplexity-ai/r1-1776
Step 1: Open Jupyter Notebook
- Start your Jupyter Notebook from your GPU VM.
- Open a new Python notebook.
Step 2: Install Dependencies
Ensure you have the required Python libraries installed:
pip install torch torchvision torchaudio
pip install transformers accelerate
Step 3: Load the Model in Python
Now, open your Jupyter Notebook and run the following script:
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
# Model Name
model_name = "perplexity-ai/r1-1776"
# Load Model with Remote Code Enabled
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto", # Auto-assign to GPU if available
trust_remote_code=True
)
# Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
print("✅ Model and Tokenizer Loaded Successfully!")
Step 4: Run Inference Using a Prompt
You can now generate responses from the model:
# Define a prompt
messages = [
{"role": "user", "content": "Explain the theory of relativity in simple terms."}
]
# Apply the tokenizer and send it to the model
inputs = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
# Convert text into tokens and send to device
inputs = tokenizer([inputs], return_tensors="pt").to(model.device)
# Generate response
with torch.no_grad():
output_ids = model.generate(**inputs, max_new_tokens=512)
# Decode and print output
response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print("📝 Model Output:\n", response)
Step 5: Run as a Chatbot Using pipeline
If you want a simplified chat interface, you can use the pipeline function:
# Load the chat pipeline
pipe = pipeline("text-generation", model="perplexity-ai/r1-1776", trust_remote_code=True)
# Query the model
response = pipe([
{"role": "user", "content": "What is the meaning of life?"}
])
print("📝 Model Output:\n", response)
Next Steps
- Test different reasoning-based prompts.
- Integrate the model into a Gradio chatbot for an interactive UI.
- Run batch inference to test model performance.
You’re all set to use R1-1776 on Jupyter Notebook!
Now, test different prompts, experiment with fine-tuning, or integrate the model into your research workflow.
Conclusion
R1 1776 is a powerful reasoning model built on the DeepSeek-R1 framework and fine-tuned by Perplexity AI to provide accurate, unbiased, and unrestricted information. With its 671 billion parameters, the model excels in logical reasoning, problem-solving, and multilingual understanding, making it a versatile tool for research, analysis, and enterprise applications.
This guide walked through the step-by-step installation process for deploying R1 1776 locally, covering NodeShift GPU Virtual Machines, Ollama, Open WebUI, and Jupyter Notebook. Users can now run fast inference, optimize their setup for high-performance tasks, and even fine-tune the model based on their requirements.
By following this setup, you can leverage the full potential of R1 1776 for deep analytical tasks, structured problem-solving, and open-ended discussions while ensuring seamless integration across various platforms. Whether used for research, automation, or interactive AI systems, this model stands as a reliable and scalable solution for advanced reasoning and factual analysis.
