Apriel-Nemotron-Thinker

Table of Contents

1. Summary
2. Evaluation
3. Training Details
4. How to Use
5. Intended Use
6. Limitations
7. Security and Responsible Use
8. Software
9. License
10. Acknowledgements
11. Citation

Summary

Apriel-Nemotron-15b-Thinker is a 15 billion‑parameter reasoning model in ServiceNow’s Apriel SLM series which achieves competitive performance against similarly sized state-of-the-art models like o1‑mini, QWQ‑32b, and EXAONE‑Deep‑32b, all while maintaining only half the memory footprint of those alternatives. It builds upon the Apriel‑15b‑base checkpoint through a three‑stage training pipeline (CPT, SFT and GRPO).

Highlights

• Half the size of SOTA models like QWQ-32b and EXAONE-32b and hence memory efficient.
• It consumes 40% less tokens compared to QWQ-32b, making it super efficient in production. 🚀🚀🚀
• On par or outperforms on tasks like - MBPP, BFCL, Enterprise RAG, MT Bench, MixEval, IFEval and Multi-Challenge making it great for Agentic / Enterprise tasks.
• Competitive performance on academic benchmarks like AIME-24 AIME-25, AMC-23, MATH-500 and GPQA considering model size.

Evaluation

Evaluations were conducted using lm-eval-harness and evalchemy.

Benchmarks that are indicative of enterprise capability

Academic reasoning benchmarks

Token efficiency comparison (lower the better)

Training Details

Mid training / Continual Pre‑training In this stage, the model is trained on 100+ billion tokens of carefully curated examples drawn from mathematical reasoning, coding challenges, scientific discourse and logical puzzles. The objective is to strengthen foundational reasoning capabilities of the model. This stage is super critical for the model to function as a reasoner and provides significant lifts in reasoning benchmarks.

Supervised Fine‑Tuning (SFT) Next, we SFT the model using 200,000 high‑quality demonstrations that cover mathematical and scientific problem‑solving, coding tasks, generic instruction‑following scenarios, API/function invocation use cases etc.

Reinforcement Learning Although the SFT‑tuned checkpoint delivers strong performance on core competencies like mathematics and general knowledge, it exhibits weaknesses in instruction following and coding tasks. To address these gaps, we apply GRPO (with some minor modifications to the objective). The result is significant improvement on benchmarks such as IFEval, Multi Challenge, Enterprise RAG, MBPP and BFCL, while preserving scores on competition‑level math exams like AIME and AMC. GRPO also yields modest gains on GPQA and MixEval.

Throughout training, intermediate snapshots from both the SFT and GRPO stages are periodically merged, improving generalization and catastrophic forgetting.

See our technical report for full details: arXiv:2508.10948.

How to Use

pip install transformers

Running the Reasoning model

Here is a code snippet demonstrating the model's usage with the transformers library's generate function:

import re
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ServiceNow-AI/Apriel-Nemotron-15b-Thinker"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "Positive real numbers $x$ and $y$ satisfy $y^3=x^2$ and $(y-x)^2=4y^2$. What is $x+y$?\nMark your solution with \\boxed"
messages = [
    {"role": "user", "content": prompt}
]

tools = []

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    tools=tools
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=65536
)
output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)

# parsing the response
response = re.findall(r"\[BEGIN FINAL RESPONSE\](.*?)\[END FINAL RESPONSE\]", output, re.DOTALL)[0].strip()
print("output:", output)
print("response:", response)

Chat Template

You are a thoughtful and systematic AI assistant built by ServiceNow Language Models (SLAM) lab. Before providing an answer, analyze the problem carefully and present your reasoning step by step. After explaining your thought process, provide the final solution in the following format: [BEGIN FINAL RESPONSE] ... [END FINAL RESPONSE].
# user message here
Here are my reasoning steps:
# thoughts here
[BEGIN FINAL RESPONSE]
# assistant response here
[END FINAL RESPONSE]

The model will first generate its thinking process and then generate its final response between [BEGIN FINAL RESPONSE] and [END FINAL RESPONSE]. Here is a code snippet demonstrating the application of the chat template:

from transformers import AutoTokenizer
model_name = "ServiceNow-AI/Apriel-Nemotron-15b-Thinker"
tokenizer = AutoTokenizer.from_pretrained(model_name)

# prepare the model input
custom_system_prompt = "Answer like a pirate."
prompt = "You are an expert assistant in the implementation of customer experience management aspect of retail applications \n \nYou will be using Python as the programming language. \n \nYou will utilize a factory design pattern for the implementation and following the dependency inversion principle \n \nYou will modify the implementation based on user requirements. \n \nUpon user request, you will add, update, and remove the features & enhancements in the implementation provided by you. \n \nYou will ask whether the user want