Instructions to use QuantFactory/AceReason-Nemotron-1.1-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/AceReason-Nemotron-1.1-7B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/AceReason-Nemotron-1.1-7B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/AceReason-Nemotron-1.1-7B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/AceReason-Nemotron-1.1-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/AceReason-Nemotron-1.1-7B-GGUF",
	filename="AceReason-Nemotron-1.1-7B.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/AceReason-Nemotron-1.1-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/AceReason-Nemotron-1.1-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/AceReason-Nemotron-1.1-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/AceReason-Nemotron-1.1-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M

SGLang

How to use QuantFactory/AceReason-Nemotron-1.1-7B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/AceReason-Nemotron-1.1-7B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/AceReason-Nemotron-1.1-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/AceReason-Nemotron-1.1-7B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/AceReason-Nemotron-1.1-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/AceReason-Nemotron-1.1-7B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/AceReason-Nemotron-1.1-7B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/AceReason-Nemotron-1.1-7B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/AceReason-Nemotron-1.1-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/AceReason-Nemotron-1.1-7B-GGUF to start chatting

How to use QuantFactory/AceReason-Nemotron-1.1-7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/AceReason-Nemotron-1.1-7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/AceReason-Nemotron-1.1-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/AceReason-Nemotron-1.1-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/AceReason-Nemotron-1.1-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.AceReason-Nemotron-1.1-7B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/AceReason-Nemotron-1.1-7B-GGUF

This is quantized version of nvidia/AceReason-Nemotron-1.1-7B created using llama.cpp

Original Model Card

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}
}

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GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Paper for QuantFactory/AceReason-Nemotron-1.1-7B-GGUF

AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy

Paper • 2506.13284 • Published Jun 16, 2025 • 26