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README.md

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+
+
+
+---
+library_name: transformers
+license: other
+license_name: nvidia-open-model-license
+license_link: >-
+  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
+pipeline_tag: text-generation
+language:
+  - en
+tags:
+  - nvidia
+  - reasoning
+  - math
+  - code
+  - reinforcement learning
+  - pytorch
+---
+
+# <span style="color: #7FFF7F;">AceReason-Nemotron-7B GGUF Models</span>
+
+
+## <span style="color: #7F7FFF;">Model Generation Details</span>
+
+This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`b9c3eefd`](https://github.com/ggerganov/llama.cpp/commit/b9c3eefde1b67104bd993485ff38dd62abe9d70c).
+
+
+
+
+
+---
+
+## <span style="color: #7FFF7F;">Quantization Beyond the IMatrix</span>
+
+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](https://github.com/Mungert69/GGUFModelBuilder/blob/main/model-converter/tensor_list_builder.py)
+
+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?**
+
+
+
+
+---
+
+<a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;">
+  Click here to get info on choosing the right GGUF model format
+</a>
+
+---
+
+
+
+<!--Begin Original Model Card-->
+
+
+# AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning
+
+<p align="center">
+
+[![Technical Report](https://img.shields.io/badge/2505.16400-Technical_Report-blue)](https://arxiv.org/abs/2505.16400)
+[![Dataset](https://img.shields.io/badge/🤗-Math_RL_Datset-blue)](https://huggingface.co/datasets/nvidia/AceReason-Math)
+[![Models](https://img.shields.io/badge/🤗-Models-blue)](https://huggingface.co/collections/nvidia/acereason-682f4e1261dc22f697fd1485)
+[![Eval Toolkit](https://img.shields.io/badge/🤗-Eval_Code-blue)](https://huggingface.co/nvidia/AceReason-Nemotron-14B/blob/main/README_EVALUATION.md)
+</p>
+
+ <img src="fig/main_fig.png" alt="main_fig" style="width: 600px; max-width: 100%;" />
+
+## 🔥News
+- **6/16/2025**: We are excited to share our new release combining SFT with RL: **AceReason-Nemotron-1.1-7B**
+  - Paper: https://arxiv.org/pdf/2506.13284
+  - Model: https://huggingface.co/nvidia/AceReason-Nemotron-1.1-7B
+  - 4M SFT Data: https://huggingface.co/datasets/nvidia/AceReason-1.1-SFT
+- **6/11/2025**: We share our evaluation toolkit at [AceReason Evalution](https://huggingface.co/nvidia/AceReason-Nemotron-14B/blob/main/README_EVALUATION.md) 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](https://huggingface.co/datasets/nvidia/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<br>(avg@64)** | **AIME 2025<br>(avg@64)** | **LCB v5<br>(avg@8)** | **LCB v6<br>(avg@8)** |
+| :---: | :---: | :---: | :---: | :---: |
+| <small>QwQ-32B</small> | 79.5 | 65.8 | 63.4 | - |
+| <small>DeepSeek-R1-671B</small> | 79.8 | 70.0 | 65.9 | - |
+| <small>Llama-Nemotron-Ultra-253B</small> | 80.8 | 72.5 | 66.3 | - |
+| <small>o3-mini (medium)</small> | 79.6 | 76.7 | 67.4 | - |
+| <small>Light-R1-7B</small> | 59.1 | 44.3 | 40.6 | 36.4 |
+| <small>Light-R1-14B</small> | 74 | 60.2 | 57.9 | 51.5 |
+| <small>DeepCoder-14B (32K Inference)</small> | 71 | 56.1 | 57.9 | 50.4 |
+| <small>OpenMath-Nemotron-7B</small> | 74.8 | 61.2 | - | - |
+| <small>OpenCodeReasoning-Nemotron-7B</small> | - | - | 51.3 | 46.1 |
+| <small>Llama-Nemotron-Nano-8B-v1</small> | 61.3 | 47.1 | 46.6 |46.2 |
+| <small>DeepSeek-R1-Distilled-Qwen-7B</small> | 55.5 | 39.0 | 37.6 | 34.1 |
+| <small>DeepSeek-R1-Distilled-Qwen-14B</small> | 69.7 | 50.2 | 53.1 | 47.9 |
+| <small>DeepSeek-R1-Distilled-Qwen-32B</small> | 72.6 | 54.9 | 57.2 | - |
+| [AceReason-Nemotron-7B 🤗](https://huggingface.co/nvidia/AceReason-Nemotron-7B)| 69.0 | 53.6 | 51.8 | 44.1 |
+| [AceReason-Nemotron-14B 🤗](https://huggingface.co/nvidia/AceReason-Nemotron-14B)| 78.6 | 67.4 | 61.1 | 54.9 |
+
+
+
+
+
+## How to use
+```python
+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
+
+1. Don't include a system prompt; instead, place all instructions directly in the user prompt.
+2. We recommend using the following instruction for math questions: Please reason step by step, and put your final answer within \\boxed{}. 
+3. We recommend using the following instruction for code questions:
+```python
+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"
+```
+4. 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 (yachen@nvidia.com), Zhuolin Yang (zhuoliny@nvidia.com), Zihan Liu (zihanl@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](https://www.nvidia.com/en-us/agreements/enterprise-software/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}
+}
+```
+
+
+
+<!--End Original Model Card-->
+
+---
+
+# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>
+
+Help me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**:  
+
+👉 [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme)  
+
+
+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](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/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**:  
+1. `"Give me info on my websites SSL certificate"`  
+2. `"Check if my server is using quantum safe encyption for communication"`  
+3. `"Run a comprehensive security audit on my server"`
+4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) 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](https://github.com/Mungert69). Feel free to use whatever you find helpful.
+
+If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. 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! 😊