Instructions to use gabriellarson/NFT-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gabriellarson/NFT-7B-GGUF with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("gabriellarson/NFT-7B-GGUF", dtype="auto")

llama-cpp-python

How to use gabriellarson/NFT-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="gabriellarson/NFT-7B-GGUF",
	filename="NFT-7B-IQ1_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use gabriellarson/NFT-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 gabriellarson/NFT-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf gabriellarson/NFT-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 gabriellarson/NFT-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf gabriellarson/NFT-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 gabriellarson/NFT-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf gabriellarson/NFT-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 gabriellarson/NFT-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gabriellarson/NFT-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/gabriellarson/NFT-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use gabriellarson/NFT-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gabriellarson/NFT-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": "gabriellarson/NFT-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gabriellarson/NFT-7B-GGUF:Q4_K_M

SGLang

How to use gabriellarson/NFT-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 "gabriellarson/NFT-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": "gabriellarson/NFT-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 "gabriellarson/NFT-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": "gabriellarson/NFT-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use gabriellarson/NFT-7B-GGUF with Ollama:
```
ollama run hf.co/gabriellarson/NFT-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use gabriellarson/NFT-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 gabriellarson/NFT-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 gabriellarson/NFT-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 gabriellarson/NFT-7B-GGUF to start chatting

Pi new

How to use gabriellarson/NFT-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 gabriellarson/NFT-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": "gabriellarson/NFT-7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use gabriellarson/NFT-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 gabriellarson/NFT-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 gabriellarson/NFT-7B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use gabriellarson/NFT-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/gabriellarson/NFT-7B-GGUF:Q4_K_M
```

Lemonade

How to use gabriellarson/NFT-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gabriellarson/NFT-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.NFT-7B-GGUF-Q4_K_M

List all available models

lemonade list

Negative-aware Fine-Tuning: Bridging Supervised Learning and Reinforcement Learning in Math Reasoning

🚨 NFT-7B is specifically designed for mathematical reasoning tasks. We do not recommend using this model for general conversation or non-mathematical tasks.

Tsinghua University, NVIDIA, Stanford University

Huayu Chen, Kaiwen Zheng, Qinsheng Zhang, Ganqu Cui, Yin Cui, Haotian Ye, Tsung-Yi Lin, Ming-Yu Liu, Jun Zhu, Haoxiang Wang

Model Overview

Description

NFT-7B is a math reasoning model finetuned from Qwen2.5-Math-7B using the Negative-aware Fine-Tuning (NFT) algorithm. NFT is a supervised learning approach that enables LLMs to reflect on their failures and improve autonomously with no external teachers. Unlike traditional supervised methods that discard incorrect answers, NFT constructs an implicit negative policy to model and learn from these failures, achieving performance comparable to leading RL algorithms like GRPO and DAPO.

This model demonstrates that self-reflective improvement is not exclusive to RL methods—supervised learning can effectively leverage negative feedback for continuous improvement in mathematical reasoning tasks.

This model is for research and development only.

Model Developer

NVIDIA, Tsinghua University, Stanford University

License

NVIDIA Non-Commercial License

This model is released under the NVIDIA Non-Commercial License. The model is for research and development only.

Deployment Geography

Global

Release Date

Huggingface 06/27/2025

NFT-7B: https://huggingface.co/nvidia/NFT-7B/
NFT-32B: https://huggingface.co/nvidia/NFT-32B/

Use Case

Mathematical reasoning and problem-solving, including:

Competition-level mathematics (AIME, AMC, Olympiad)
General mathematical reasoning (MATH500, Minerva Math)
Step-by-step mathematical solution generation

Model Architecture

Architecture Type: Transformer decoder-only language model

Network Architecture: Qwen2.5

NFT-7B is post-trained based on Qwen2.5-Math-7B and follows the same model architecture. The model has 7B parameters.

Input

Input Type(s): Text

Input Format: String

Input Parameters: One-dimensional (1D)

Other Properties Related to Input:

Context length up to 32,768 tokens
Mathematical problems should be clearly stated
Supports LaTeX notation for mathematical expressions

Output

Output Type(s): Text

Output Format: String

Output Parameters: One-dimensional (1D)

Other Properties Related to Output:

Step-by-step mathematical reasoning
Final answers should be enclosed in \boxed{}
Supports LaTeX notation for mathematical expressions
Maximum generation length: 8,192 tokens

Software Integration

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Runtime Engine(s):

Transformers
vLLM
TensorRT-LLM

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper
NVIDIA Blackwell

Operating System(s):

Linux

Model version: v1.0

Training Method

The NFT training pipeline consists of three main components:

Data Collection: The model generates answers to math questions, which are split into positive (correct) and negative (incorrect) datasets based on answer correctness.
Implicit Negative Policy: NFT constructs an implicit negative policy to model negative answers, parameterized with the same positive policy targeted for optimization, enabling direct policy optimization on all generations.
Policy Optimization: Both positive and negative answers are used to optimize the LLM policy via supervised learning with the NFT objective:

L_NFT(θ) = r[-log(π_θ⁺(a|q) / π(a|q))] + (1-r)[-log((1 - r_q * (π_θ⁺(a|q) / π(a|q))) / (1-r_q))]

Training Datasets

Dataset: DAPO-Math-17k

Dataset Size: 17k (math problems)

Data Collection Method: Automated

Labeling Method by dataset: Automated

The whole dataset is used for training. We directly evaluate the model on several other math evaluation datasets.

Evaluation Datasets

NFT-7B is evaluated on 6 mathematical reasoning benchmarks:

AIME 2024 (30 problems) & 2025 (30 problems): American Invitational Mathematics Examination
AMC 2023 (40 problems): American Mathematics Competitions
MATH500 (500 problems): A subset of the MATH dataset
OlympiadBench (675 problems): International Mathematical Olympiad problems
Minerva Math (272 problems): Google's mathematical reasoning benchmark

Data Collection Method: Human

Labeling Method by dataset: Human

Performance

NFT-7B achieves competitive performance compared to other 7B math models, with particularly strong results on competition mathematics:

Benchmark	NFT-7B	Qwen2.5-Math-7B	Improvement
AIME24 (avg@32)	32.0%	13.3%	+18.7%
AIME25 (avg@32)	18.3%	5.5%	+12.8%
MATH500	83.2%	69.0%	+14.2%
AMC23 (avg@32)	88.5%	45.8%	+42.7%
OlympiadBench	47.3%	34.7%	+12.6%
Minerva Math	40.8%	21.3%	+19.5%
Average	51.7%	31.6%	+20.1%

Usage

NFT-7B is optimized for mathematical reasoning tasks. For best results, use clear mathematical prompts and request step-by-step reasoning.

The model can be used with the Hugging Face Transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "nvidia/NFT-7B"
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Example math problem
problem = "Find the value of $x$ that satisfies the equation $\\sqrt{x+7} = x-5$."
# Format the prompt to encourage step-by-step reasoning
prompt = f"{problem}\nPlease reason step by step, and put your final answer within \\boxed{{}}."
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(device)
# Generate response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512,
    temperature=0  # Use 0 for deterministic output
)
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]
print(response)

Usage Recommendations

Temperature: Use temperature=0 for deterministic outputs, or 0.1-0.3 for slight variation
Sampling: For best results on competition problems, consider using multiple samples with majority voting
Format: Include instructions for step-by-step reasoning directly in the user prompt
Final Answer: Instruct the model to put the final answer in \boxed{}
Language: This model is primarily trained on English mathematical problems

Requirements

The code of Qwen2.5 has been integrated into Hugging Face transformers, and we recommend using the latest version:

transformers>=4.37.0

Citation

If you find our project helpful, please consider citing

@article{chen2025bridging,
      title         = {Bridging Supervised Learning and Reinforcement Learning in Math Reasoning},
      author        = {Huayu Chen, Kaiwen Zheng, Qinsheng Zhang, Ganqu Cui, Yin Cui, Haotian Ye, Tsung-Yi Lin, Ming-Yu Liu, Jun Zhu, Haoxiang Wang},
      journal       = {arXiv preprint arXiv:2505.18116},
      year          = {2025}
}

Known Limitations

Domain Specificity: This model is specifically trained for mathematical reasoning and may not perform well on general conversation or non-mathematical tasks
Calculation Errors: While the model shows strong reasoning abilities, it may still make arithmetic errors in complex calculations
Context Understanding: The model may struggle with problems requiring real-world context or domain knowledge outside mathematics

Bias Considerations

The model is trained on mathematical problems which are inherently objective. However, the training data may reflect biases in problem selection, difficulty distribution, and mathematical notation preferences from the source datasets.

Inference:

Acceleration Engine: TensorRT-LLM, vLLM, SGLang
Test Hardware: NVIDIA H100

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.

Downloads last month: 72

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for gabriellarson/NFT-7B-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Math-7B

Finetuned

nvidia/NFT-7B

Quantized

(3)

this model

Dataset used to train gabriellarson/NFT-7B-GGUF

Paper for gabriellarson/NFT-7B-GGUF

Bridging Supervised Learning and Reinforcement Learning in Math Reasoning

Paper • 2505.18116 • Published May 23, 2025 • 4