AceReason-Nemotron-7B GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit b9c3eefd.

Quantization Beyond the IMatrix

I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the --tensor-type option in llama.cpp to manually "bump" important layers to higher precision. You can see the implementation here:
👉 Layer bumping with llama.cpp

While this does increase model file size, it significantly improves precision for a given quantization level.

I'd love your feedback—have you tried this? How does it perform for you?

Click here to get info on choosing the right GGUF model format

AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning

🔥News

6/16/2025: We are excited to share our new release combining SFT with RL: AceReason-Nemotron-1.1-7B
6/11/2025: We share our evaluation toolkit at AceReason Evalution including:
- scripts to run inference and scoring
- LiveCodeBench (avg@8): model prediction files and scores for each month (2023/5-2025/5)
- AIME24/25 (avg@64): model prediction files and scores
6/2/2025: We are excited to share our Math RL training dataset at AceReason-Math

We're thrilled to introduce AceReason-Nemotron-7B, a math and code reasoning model trained entirely through reinforcement learning (RL), starting from the DeepSeek-R1-Distilled-Qwen-7B. It delivers impressive results, achieving 69.0% on AIME 2024 (+14.5%), 53.6% on AIME 2025 (+17.4%), 51.8% on LiveCodeBench v5 (+8%), 44.1% on LiveCodeBench v6 (+7%). We systematically study the RL training process through extensive ablations and propose a simple yet effective approach: first RL training on math-only prompts, then RL training on code-only prompts. Notably, we find that math-only RL not only significantly enhances the performance of strong distilled models on math benchmarks, but also code reasoning tasks. In addition, extended code-only RL further improves code benchmark performance while causing minimal degradation in math results. We find that RL not only elicits the foundational reasoning capabilities acquired during pre-training and supervised fine-tuning (e.g., distillation), but also pushes the limits of the model's reasoning ability, enabling it to solve problems that were previously unsolvable.

We share our training recipe, training logs in our technical report.

Results

We evaluate our model against competitive reasoning models of comparable size within Qwen2.5 and Llama3.1 model family on AIME 2024, AIME 2025, LiveCodeBench v5 (2024/08/01 - 2025/02/01), and LiveCodeBench v6 (2025/02/01-2025/05/01). More evaluation results can be found in our technical report.

Model	AIME 2024 (avg@64)	AIME 2025 (avg@64)	LCB v5 (avg@8)	LCB v6 (avg@8)
QwQ-32B	79.5	65.8	63.4	-
DeepSeek-R1-671B	79.8	70.0	65.9	-
Llama-Nemotron-Ultra-253B	80.8	72.5	66.3	-
o3-mini (medium)	79.6	76.7	67.4	-
Light-R1-7B	59.1	44.3	40.6	36.4
Light-R1-14B	74	60.2	57.9	51.5
DeepCoder-14B (32K Inference)	71	56.1	57.9	50.4
OpenMath-Nemotron-7B	74.8	61.2	-	-
OpenCodeReasoning-Nemotron-7B	-	-	51.3	46.1
Llama-Nemotron-Nano-8B-v1	61.3	47.1	46.6	46.2
DeepSeek-R1-Distilled-Qwen-7B	55.5	39.0	37.6	34.1
DeepSeek-R1-Distilled-Qwen-14B	69.7	50.2	53.1	47.9
DeepSeek-R1-Distilled-Qwen-32B	72.6	54.9	57.2	-
AceReason-Nemotron-7B 🤗	69.0	53.6	51.8	44.1
AceReason-Nemotron-14B 🤗	78.6	67.4	61.1	54.9

How to use

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = 'nvidia/AceReason-Nemotron-7B'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

prompt = "Jen enters a lottery by picking $4$ distinct numbers from $S=\\{1,2,3,\\cdots,9,10\\}.$ $4$ numbers are randomly chosen from $S.$ She wins a prize if at least two of her numbers were $2$ of the randomly chosen numbers, and wins the grand prize if all four of her numbers were the randomly chosen numbers. The probability of her winning the grand prize given that she won a prize is $\\tfrac{m}{n}$ where $m$ and $n$ are relatively prime positive integers. Find $m+n$."
messages = [{"role": "user", "content": prompt}]

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

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768,
    temperature=0.6,
    top_p=0.95
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Usage Recommendations

Don't include a system prompt; instead, place all instructions directly in the user prompt.
We recommend using the following instruction for math questions: Please reason step by step, and put your final answer within \boxed{}.
We recommend using the following instruction for code questions:

question = "" # code question
starter_code = "" # starter code function header

code_instruction_nostartercode = """Write Python code to solve the problem. Please place the solution code in the following format:\n```python\n# Your solution code here\n```"""
code_instruction_hasstartercode = """Please place the solution code in the following format:\n```python\n# Your solution code here\n```"""
if starter_code != "":
    question += "\n\n" + "Solve the problem starting with the provided function header.\n\nFunction header:\n" + "```\n" + starter_code + "\n```"
    question += "\n\n" + code_instruction_hasstartercode
else:
    question += "\n\n" + code_instruction_nostartercode

final_prompt = "<｜User｜>" + question + "<｜Assistant｜><think>\n"

Our inference engine for evaluation is vLLM==0.7.3 using top-p=0.95, temperature=0.6, max_tokens=32768.

Evaluation Toolkit

Please check evaluation code, scripts, cached prediction files in https://huggingface.co/nvidia/AceReason-Nemotron-14B/blob/main/README_EVALUATION.md

Correspondence to

Yang Chen ([email protected]), Zhuolin Yang ([email protected]), Zihan Liu ([email protected]), Chankyu Lee ([email protected]), Wei Ping ([email protected])

License

Your use of this model is governed by the NVIDIA Open Model License.

Citation

@article{chen2025acereason,
  title={AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning},
  author={Chen, Yang and Yang, Zhuolin and Liu, Zihan and Lee, Chankyu and Xu, Peng and Shoeybi, Mohammad and Catanzaro, Bryan and Ping, Wei},
  journal={arXiv preprint arXiv:2505.16400},
  year={2025}
}

🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

TurboLLM (GPT-4.1-mini)
HugLLM (Hugginface Open-source models)
TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

Function calling against live network services
How small can a model go while still handling:
- Automated Nmap security scans
- Quantum-readiness checks
- Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

✅ Zero-configuration setup
⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

**It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
Create custom cmd processors to run .net code on Quantum Network Monitor Agents
Real-time network diagnostics and monitoring
Security Audits
Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

"Give me info on my websites SSL certificate"
"Check if my server is using quantum safe encyption for communication"
"Run a comprehensive security audit on my server"
'"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊