Build Your Own Local GPT-o1 Alternative with Nemotron

The o1 model is recognized as one of the most powerful reasoning AI models today, often outperforming experts on complex, Ph.D.-level questions.

Yet, many clients—particularly those in privacy-conscious areas like Germany—remain cautious about using OpenAI’s models due to data privacy concerns. Data passed through OpenAI’s systems may become exposed, which can be a serious limitation for organizations prioritizing data control.

Now imagine the possibility of building a model comparable to o1 that runs locally on private infrastructure. With this approach, businesses could access o1-level performance while maintaining full data ownership.

A promising route to achieving this combines Nemotron, an advanced model powered by Llama-31-nemotron-70b-instruct, with a structured reasoning approach known as "Chain of Thought" (CoT).

Why is GTP-o1 so superior?

o1 is distinguished as a "reasoning model," spending between 10 and 60 seconds to carefully construct responses. Unlike faster models, o1’s deliberate pace allows it to employ Chain of Thought (CoT), breaking complex questions into smaller, sequential steps. This systematic process results in highly accurate and transparent answers (especially in comparison with older models like GPT4o).

What are these Chains of Thought (CoT) ?

Let’s examine a typical query made to a local language model, such as Llama 3.1 8B 
"How many r are in the word strawberry"

 As shown, the initial result is significantly incorrect.

Now, let’s see what happens if we modify the system prompt to:

You are a highly logical assistant. When answering a question, provide a clear, step-by-step explanation of your thought process. Break the problem into smaller parts, explain each one thoroughly, and conclude with the final answer.

Here is the outcome with this adjusted prompt.

The result is now correct. Logical prompts like these are essential for generating the high-quality, structured responses that define o1's capabilities.

What is Nemotron?

Nvidia has launched a customized and optimized version of Llama 3.1, dubbed 'Nemotron.' This 70-billion-parameter model has shaken up the AI field by outperforming language models like GPT-4 and Claude 3.5 Sonic in multiple benchmarks.

For those seeking an alternative model to achieve similar results to GPT o1, Nemotron is a compelling option. 

The combination of Llama Nemotron and Chain of Thought offers a high-performing open-source solution with reasoning depth comparable to o1. 

Why is Llama 3.1 Nemotron 70B so good?

NVIDIA hasn’t just released an enhanced version of the Llama 3.1 model; they've also introduced the HelpSteer2 dataset and the Nemotron-4-340B-Reward model, which play a crucial role in crafting highly responsive and well-aligned models like Llama-3.1-Nemotron-70B-Instruct. Here’s how these innovations together drive its success:

1. Foundational Dataset and Reward Model: NVIDIA first published HelpSteer2—a dataset containing human-annotated response pairs that score helpfulness, correctness, coherence, complexity, and verbosity. Alongside this, they released the Nemotron-4-340B-Reward model, specifically designed to evaluate responses based on these attributes. Together, HelpSteer2 and the reward model set a high-quality standard for training language models aligned with human values.

   HelpSteer Dataset: https://huggingface.co/datasets/nvidia/HelpSteer2
   
   
   
   

2. Reinforcement Learning with Human Feedback (RLHF): NVIDIA utilized the Nemotron-4-340B-Reward model with reinforcement learning (specifically the REINFORCE algorithm) to fine-tune the Llama-3.1-70B-Instruct model. This technique involves giving feedback to the model based on the reward model’s scoring, guiding it to generate responses that maximize scores for alignment with user preferences.
   
   

3. Enhanced Response Quality: The integration of the HelpSteer2 dataset and reward model allowed the 70B variant to learn desirable traits, like accuracy and helpfulness, in a more nuanced way. As a result, it’s particularly skilled at handling complex or ambiguous queries by producing

   responses that are both helpful and precise, reinforcing its effectiveness in real-world applications.

By combining Nemotron’s robust capabilities with the Chain of Thought approach, users gain access to a high-performing local language model that can effectively replace or compete with models like Claude 3.5, Sonnet, GPT-4o, and even o1.

How can I test it?

A full video tutorial on running the model locally or on RunPod and integrating it with your code is available here.