AceReason-Nemotron-1.1-7B GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit aa0ef5c5.

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 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy

We're thrilled to introduce AceReason-Nemotron-1.1-7B, a math and code reasoning model built upon the Qwen2.5-Math-7B base. The model is first trained with supervised fine-tuning (SFT) on math and code tasks, then further enhanced through reinforcement learning (RL) using the same recipe as AceReason-Nemotron-1.0-7B. We initiate RL training from various SFT models and find that stronger SFT models continue to produce consistently better results after large-scale RL, although the performance gap narrows during RL training. Thanks to its stronger SFT backbone, AceReason-Nemotron-1.1-7B significantly outperforms its predecessor and sets a record-high performance among Qwen2.5-7B-based reasoning models on challenging math and code reasoning benchmarks. For more details, check our technical report.

Results

We evaluate our model against competitive reasoning models of comparable size on AIME 2024, AIME 2025, and LiveCodeBench (LCB) v5 (2024/08/01 - 2025/02/01) and v6 (2025/02/01-2025/05/01). For AceReason-Nemotron-1.0-7B, the RL training recipe improves its starting SFT model, DeepSeek-R1-Distill-Qwen-7B, by 13.5% on AIME24, 14.6% on AIME25, 14.2% on LCB v5, and 10.0% on LCB v6. In comparison, AceReason-Nemotron-1.1-7B, built on a stronger SFT model, also benefits substantially from the same RL recipe, achieving absolute improvements of 10.6% on AIME24, 16.4% on AIME25, 8.4% on LCB v5, and 8.3% on LCB v6.

Model	AIME 2024 (avg@64)	AIME 2025 (avg@64)	LCB v5 (avg@8)	LCB v6 (avg@8)
Skywork-OR1-7B	70.2	54.6	47.6	42.7
MiMo-7B-RL	68.2	55.4	57.8	49.3
o3-mini (low)	60.0	48.3	60.9	-
OpenMath-Nemotron-7B	74.8	61.2	-	-
OpenCodeReasoning-Nemotron-7B	-	-	51.3	46.1
Magistral Small (24B)	70.7	62.8	55.8	47.4
DeepSeek-R1-Distill-Qwen-7B	55.5	39.0	37.6	34.1
AceReason-Nemotron-1.0-7B	69.0	53.6	51.8	44.1
Our SFT-7B (starting point of RL)	62.0	48.4	48.8	43.8
AceReason-Nemotron-1.1-7B 🤗	72.6	64.8	57.2	52.1

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = 'nvidia/AceReason-Nemotron-1.1-7B'
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

We recommend using the system prompt: "You are a helpful and harmless assistant. You should think step-by-step."
We recommend using the following instruction for math questions:

math_question = "MATH_QUESTION"
math_instruction = "Please place your final answer inside \\boxed{}."
system_instruction = "You are a helpful and harmless assistant. You should think step-by-step."

final_prompt = "<|im_start|>system\n" + system_instruction + "<|im_end|>\n<|im_start|>user\n" + math_question + "\n\n" + math_instruction + "<|im_end|>\n<|im_start|>assistant\n<think>\n"

We recommend using the following instruction for code questions:

code_question = "CODE_QUESTION"
starter_code = "STARTER_CODE" # starter code function header, set empty string ("") if there is no starter code

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 != "":
    code_question += "\n\n" + "Solve the problem starting with the provided function header.\n\nFunction header:\n" + "```\n" + starter_code + "\n```"
    code_question += "\n\n" + code_instruction_hasstartercode
else:
    code_question += "\n\n" + code_instruction_nostartercode

final_prompt = "<|im_start|>system\n" + system_instruction + "<|im_end|>\n<|im_start|>user\n" + code_question + "<|im_end|>\n<|im_start|>assistant\n<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 refer to the evaluation code and scripts in https://huggingface.co/nvidia/AceReason-Nemotron-14B/blob/main/README_EVALUATION.md. For model inference, modify the prompt according to the guidelines in the Usage Recommendations section.

Correspondence to

Zihan Liu (zihanl@nvidia.com), Zhuolin Yang (zhuoliny@nvidia.com), Yang Chen (yachen@nvidia.com), Chankyu Lee (chankyul@nvidia.com), Wei Ping (wping@nvidia.com)

License

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

Release Date

June 16, 2025

Citation

@article{liu2025acereason,
  title={AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy},
  author={Liu, Zihan and Yang, Zhuolin and Chen, Yang and Lee, Chankyu and Shoeybi, Mohammad and Catanzaro, Bryan and Ping, Wei},
  journal={arXiv preprint arXiv:2506.13284},
  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! 😊