OpenReasoning-Nemotron - Open Source Series of Reasoning Models from NVIDIA

What is OpenReasoning-Nemotron?

OpenReasoning-Nemotron is a series of large-scale language models open-sourced by NVIDIA to support processing of reasoning tasks in math, science, and code. The models are based on DeepSeek The R1 0528 model is distilled from the R1 0528 model, with parameter scales of 1.5B, 7B, 14B and 32B to meet different computational needs. Based on large-scale data distillation and supervised fine-tuning training, the model has excellent inference ability and outperforms competitors in the field of mathematics. openReasoning-Nemotron supports "heavy" inference mode, combining with the GenSelect algorithm and multi-intelligence collaboration, to further enhance the inference effect. The model runs locally and is supported by LM. The model runs locally and can be deployed with tools such as LM Studio, providing a powerful tool for education, research, and code development.

Key Features of OpenReasoning-Nemotron

• Highly effective reasoning skills: excels in complex areas such as math, science, and code, generating high-quality reasoning solutions that provide users with accurate logical analysis and answers.
• Flexible model sizesThe model provides a variety of parameter scales (1.5B, 7B, 14B, and 32B) so that users can choose the appropriate model version according to their computational resources and task requirements.
• The "heavy" reasoning model: Based on the GenSelect algorithm combining the reasoning results of multiple intelligences to further optimize performance, it excels in mathematical and coding tasks and provides better solutions.
• Strong baseline support: provide a powerful starting point for future reinforcement learning (RL)-based reasoning research and help develop more efficient reasoning techniques.
• Local runtime support: Supports local 100% operation, which is easily deployed and utilized by users with tools such as LM Studio.

OpenReasoning-Nemotron official website address

• HuggingFace Model Library:: https://huggingface.co/collections/nvidia/openreasoning-nemotron-687730dae0170059860f1f01

How to Use OpenReasoning-Nemotron

• Hugging Face Hub Usage::Access the HuggingFace model library address.
• Install the Hugging Face Transformers library: Installation of Hugging Face's transformers library, install it with the following command:

pip install transformers

• Loading Models: Use of transformers The library loads the selected OpenReasoning-Nemotron model. For example, a model with 7B parameters is loaded:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "nvidia/OpenReasoning-Nemotron-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

• Generating inference results: Generate reasoning results with models. For example, generating an answer to a math problem:

input_text = "Solve the equation 2x + 3 = 7."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)

Core Benefits of OpenReasoning-Nemotron

• Powerful reasoning: Focuses on math, science, and code reasoning tasks, providing high-quality reasoning solutions based on multi-intelligence collaboration and superior mathematical performance.
• Flexible model sizesThe program is available in four parameter sizes: 1.5B, 7B, 14B, and 32B to meet different computing resources and task requirements.
• Advanced training techniques: Based on large-scale data distillation and supervised fine-tuning (SFT), reinforcement learning (RL) was not used, providing a solid foundation for subsequent research.
• Efficient deployment and utilization: Supports local operation, LM Studio deployment, and integration with Hugging Face Hub for quick loading and use.
• Powerful baseline modeling: Provide a powerful starting point for reinforcement learning (RL) research to support reasoning and solutions to complex problems in multiple domains.
• Open Source and Community Support: As an open source model, users have free access to the code and the rich resources and support of the Hugging Face community.

Who is OpenReasoning-Nemotron for?

• research worker: Scholars and researchers working in the fields of artificial intelligence, machine learning, and natural language processing to explore and optimize inference algorithms.
• educator: Teachers who need aids to help students understand and solve complex problems in teaching math, science, and other subjects.
• schoolchildren: Students studying math, science, programming, and other subjects answer questions and study aids with the help of models.
• software developer: Programmers and development teams who need to generate code, optimize code performance, or debug code.
• data scientist: Reasoning support with models for professionals dealing with complex data analysis and scientific computing tasks.