Mungert/OpenCodeReasoning-Nemotron-7B-GGUF

<span style="color: #7FFF7F;">OpenCodeReasoning-Nemotron-7B GGUF Models</span>

<span style="color: #7F7FFF;">Model Generation Details</span>

This model was generated using llama.cpp at commit `064cc596`.

<span style="color: #7FFF7F;">Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit)</span>

Our latest quantization method introduces precision-adaptive quantization for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on Llama-3-8B. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.

Benchmark Context

All tests conducted on Llama-3-8B-Instruct using:

• —Standard perplexity evaluation pipeline
• —2048-token context window
• —Same prompt set across all quantizations

Method

• —Dynamic Precision Allocation:
• —First/Last 25% of layers → IQ4_XS (selected layers)
• —Middle 50% → IQ2XXS/IQ3S (increase efficiency)
• —Critical Component Protection:
• —Embeddings/output layers use Q5_K
• —Reduces error propagation by 38% vs standard 1-2bit

Quantization Performance Comparison (Llama-3-8B)

Key:

• —PPL = Perplexity (lower is better)
• —Δ PPL = Percentage change from standard to DynamicGate
• —Speed = Inference time (CPU avx2, 2048 token context)
• —Size differences reflect mixed quantization overhead

Key Improvements:

• —🔥 IQ1_M shows massive 43.9% perplexity reduction (27.46 → 15.41)
• —🚀 IQ2_S cuts perplexity by 36.9% while adding only 0.2GB
• —⚡ IQ1_S maintains 39.7% better accuracy despite 1-bit quantization

Tradeoffs:

• —All variants have modest size increases (0.1-0.3GB)
• —Inference speeds remain comparable (<5% difference)

When to Use These Models

📌 Fitting models into GPU VRAM

✔ Memory-constrained deployments

✔ Cpu and Edge Devices where 1-2bit errors can be tolerated

✔ Research into ultra-low-bit quantization

Choosing the Right Model Format

Selecting the correct model format depends on your hardware capabilities and memory constraints.

BF16 (Brain Float 16) – Use if BF16 acceleration is available

• —A 16-bit floating-point format designed for faster computation while retaining good precision.
• —Provides similar dynamic range as FP32 but with lower memory usage.
• —Recommended if your hardware supports BF16 acceleration (check your device's specs).
• —Ideal for high-performance inference with reduced memory footprint compared to FP32.

📌 Use BF16 if: ✔ Your hardware has native BF16 support (e.g., newer GPUs, TPUs). ✔ You want higher precision while saving memory. ✔ You plan to requantize the model into another format.

📌 Avoid BF16 if: ❌ Your hardware does not support BF16 (it may fall back to FP32 and run slower). ❌ You need compatibility with older devices that lack BF16 optimization.

F16 (Float 16) – More widely supported than BF16

• —A 16-bit floating-point high precision but with less of range of values than BF16.
• —Works on most devices with FP16 acceleration support (including many GPUs and some CPUs).
• —Slightly lower numerical precision than BF16 but generally sufficient for inference.

📌 Use F16 if: ✔ Your hardware supports FP16 but not BF16. ✔ You need a balance between speed, memory usage, and accuracy. ✔ You are running on a GPU or another device optimized for FP16 computations.

📌 Avoid F16 if: ❌ Your device lacks native FP16 support (it may run slower than expected). ❌ You have memory limitations.

Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference

Quantization reduces model size and memory usage while maintaining as much accuracy as possible.

• —Lower-bit models (Q4_K) → Best for minimal memory usage, may have lower precision.
• —Higher-bit models (Q6_K, Q8_0) → Better accuracy, requires more memory.

📌 Use Quantized Models if: ✔ You are running inference on a CPU and need an optimized model. ✔ Your device has low VRAM and cannot load full-precision models. ✔ You want to reduce memory footprint while keeping reasonable accuracy.

📌 Avoid Quantized Models if: ❌ You need maximum accuracy (full-precision models are better for this). ❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).

Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)

These models are optimized for extreme memory efficiency, making them ideal for low-power devices or large-scale deployments where memory is a critical constraint.

• —IQ3_XS: Ultra-low-bit quantization (3-bit) with extreme memory efficiency.
• —Use case: Best for ultra-low-memory devices where even Q4_K is too large.
• —Trade-off: Lower accuracy compared to higher-bit quantizations.

• —IQ3_S: Small block size for maximum memory efficiency.
• —Use case: Best for low-memory devices where IQ3_XS is too aggressive.

• —IQ3_M: Medium block size for better accuracy than IQ3_S.
• —Use case: Suitable for low-memory devices where IQ3_S is too limiting.

• —Q4_K: 4-bit quantization with block-wise optimization for better accuracy.
• —Use case: Best for low-memory devices where Q6_K is too large.

• —Q4_0: Pure 4-bit quantization, optimized for ARM devices.
• —Use case: Best for ARM-based devices or low-memory environments.

Summary Table: Model Format Selection

Included Files & Details

OpenCodeReasoning-Nemotron-7B-bf16.gguf

• —Model weights preserved in BF16.
• —Use this if you want to requantize the model into a different format.
• —Best if your device supports BF16 acceleration.

OpenCodeReasoning-Nemotron-7B-f16.gguf

• —Model weights stored in F16.
• —Use if your device supports FP16, especially if BF16 is not available.

OpenCodeReasoning-Nemotron-7B-bf16-q8_0.gguf

• —Output & embeddings remain in BF16.
• —All other layers quantized to Q8_0.
• —Use if your device supports BF16 and you want a quantized version.

OpenCodeReasoning-Nemotron-7B-f16-q8_0.gguf

• —Output & embeddings remain in F16.
• —All other layers quantized to Q8_0.

OpenCodeReasoning-Nemotron-7B-q4_k.gguf

• —Output & embeddings quantized to Q8_0.
• —All other layers quantized to Q4_K.
• —Good for CPU inference with limited memory.

OpenCodeReasoning-Nemotron-7B-q4_k_s.gguf

• —Smallest Q4_K variant, using less memory at the cost of accuracy.
• —Best for very low-memory setups.

OpenCodeReasoning-Nemotron-7B-q6_k.gguf

• —Output & embeddings quantized to Q8_0.
• —All other layers quantized to Q6_K .

OpenCodeReasoning-Nemotron-7B-q8_0.gguf

• —Fully Q8 quantized model for better accuracy.
• —Requires more memory but offers higher precision.

OpenCodeReasoning-Nemotron-7B-iq3_xs.gguf

• —IQ3_XS quantization, optimized for extreme memory efficiency.
• —Best for ultra-low-memory devices.

OpenCodeReasoning-Nemotron-7B-iq3_m.gguf

• —IQ3_M quantization, offering a medium block size for better accuracy.
• —Suitable for low-memory devices.

OpenCodeReasoning-Nemotron-7B-q4_0.gguf

• —Pure Q4_0 quantization, optimized for ARM devices.
• —Best for low-memory environments.
• —Prefer IQ4_NL for better accuracy.

<span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>

❤ Please click "Like" if you find this useful! Help me test my AI-Powered Network Monitor Assistant with quantum-ready security checks: 👉 Quantum Network Monitor

💬 How to test: Choose an AI assistant type:

• —TurboLLM (GPT-4o-mini)
• —HugLLM (Hugginface Open-source)
• —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 scans
• —Quantum-readiness checks
• —Network Monitoring tasks

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

• —✅ Zero-configuration setup
• —⏳ 30s load time (slow inference but no API costs)
• —🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4o-mini for:

• —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

💡 Example commands to you could test:

1. 1."Give me info on my websites SSL certificate"
2. 2."Check if my server is using quantum safe encyption for communication"
3. 3."Run a comprehensive security audit on my server"
4. 4.'"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code from. 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! 😊

OpenCodeReasoning-Nemotron-7B Overview

Description: <br>

OpenCodeReasoning-Nemotron-7B is a large language model (LLM) which is a derivative of Qwen2.5-7B-Instruct (AKA the reference model). It is a reasoning model that is post-trained for reasoning for code generation. The model supports a context length of 32K tokens. <br>

This model is ready for commercial/non-commercial use. <br>

[image]

Results from OpenCodeReasoning

Below results are the average of 64 evaluations on each benchmark.

Reproducing our results

• —Models
• —Dataset
• —Paper

How to use the models?

To run inference on coding problems:

import transformers
import torch

model_id = "nvidia/OpenCodeReasoning-Nemotron-7B"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

prompt = """You are a helpful and harmless assistant. You should think step-by-step before responding to the instruction below.

Please use python programming language only.

You must use ```python for just the final solution code block with the following format:

Your code here

{user}
"""

messages = [
    {
        "role": "user",
        "content": prompt.format(user="Write a program to calculate the sum of the first $N$ fibonacci numbers")},
]

outputs = pipeline(
    messages,
    max_new_tokens=32768,
)
print(outputs[0]["generated_text"][-1]['content'])

Citation

If you find the data useful, please cite:

@article{ahmad2025opencodereasoning,
      title={OpenCodeReasoning: Advancing Data Distillation for Competitive Coding}, 
      author={Wasi Uddin Ahmad, Sean Narenthiran, Somshubra Majumdar, Aleksander Ficek, Siddhartha Jain, Jocelyn Huang, Vahid Noroozi, Boris Ginsburg},
      year={2025},
      eprint={2504.01943},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2504.01943}, 
}

Additional Information

Model Architecture: <br>

Architecture Type: Dense decoder-only Transformer model Network Architecture: Qwen-7B-Instruct <br> This model was developed based on Qwen2.5-7B-Instruct and has 7B model parameters. <br> OpenCodeReasoning-Nemotron-7B was developed based on Qwen2.5-7B-Instruct and has 7B model parameters. <br>

Input: <br>

Input Type(s): Text <br> Input Format(s): String <br> Input Parameters: One-Dimensional (1D) <br> Other Properties Related to Input: Context length up to 32,768 tokens <br>

Output: <br>

Output Type(s): Text <br> Output Format: String <br> Output Parameters: One-Dimensional (1D) <br> Other Properties Related to Output: Context length up to 32,768 tokens <br>

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. <br>

Software Integration : <br>

• —Runtime Engine: NeMo 2.3.0 <br>
• —Recommended Hardware Microarchitecture Compatibility: <br> NVIDIA Ampere <br> NVIDIA Hopper <br>
• —Preferred/Supported Operating System(s): Linux <br>

Model Version(s):

1.0 (4/25/2025) <br> OpenCodeReasoning-Nemotron-7B<br> OpenCodeReasoning-Nemotron-14B<br> OpenCodeReasoning-Nemotron-32B<br> OpenCodeReasoning-Nemotron-32B-IOI<br>

Training and Evaluation Datasets: <br>

Training Dataset:

The training corpus for OpenCodeReasoning-Nemotron-7B is OpenCodeReasoning dataset, which is composed of competitive programming questions and DeepSeek-R1 generated responses.

Data Collection Method: Hybrid: Automated, Human, Synthetic <br> Labeling Method: Hybrid: Automated, Human, Synthetic <br> Properties: 736k samples from OpenCodeReasoning (https://huggingface.co/datasets/nvidia/OpenCodeReasoning)

Evaluation Dataset:

We used the datasets listed in the next section to evaluate OpenCodeReasoning-Nemotron-7B. <br> Data Collection Method: Hybrid: Automated, Human, Synthetic <br> Labeling Method: Hybrid: Automated, Human, Synthetic <br>

License/Terms of Use: <br>

GOVERNING TERMS: Use of this model is governed by Apache 2.0.

Deployment Geography:

Global<br>

Use Case: <br>

This model is intended for developers and researchers building LLMs. <br>

Release Date: <br>

Huggingface [04/25/2025] via https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-7B/ <br>

Reference(s):

[2504.01943] OpenCodeReasoning: Advancing Data Distillation for Competitive Coding <br>

Inference:

Engine: vLLM <br> Test Hardware NVIDIA H100-80GB <br>

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.