Model Overview
Description:
The mmangkad/Qwen3.6-27B-NVFP4 model is a quantized version of Alibaba's Qwen3.6-27B model, an auto-regressive language model that uses an optimized transformer architecture. For more information on the base model, please check here. This model was quantized using the NVIDIA Model Optimizer.
This model is ready for commercial/non-commercial use.
Third-Party Community Consideration
This model is built upon a third-party base model; see the link to the Non-NVIDIA (Qwen3.6-27B) Model Card for original requirements and use cases.
License/Terms of Use:
Deployment Geography:
Global
Use Case:
Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.
Release Date:
Huggingface 04/22/2026 via https://huggingface.co/mmangkad/Qwen3.6-27B-NVFP4
Model Architecture:
Architecture Type: Transformers Network Architecture: Qwen3.6-27B Number of Model Parameters: 27B
Input:
Input Type(s): Text Input Format(s): String Input Parameters: One-Dimensional (1D) Other Properties Related to Input: Context length up to 262K
Output:
Output Type(s): Text Output Format: String Output Parameters: 1D (One-Dimensional): Sequences Other Properties Related to Output: N/A
This AI model is 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.
Software Integration:
Supported Runtime Engine(s):
- SGLang
Supported Hardware Microarchitecture Compatibility:
- NVIDIA Blackwell
Preferred Operating System(s):
- Linux
Model Version(s):
The model is quantized with nvidia-modelopt v0.42.0rc1.dev107+g3c5d36680
Training, Testing, and Evaluation Datasets:
Calibration Dataset:
** Link: cnn_dailymail, Nemotron-Post-Training-Dataset-v2 ** Data Collection Method by dataset: Automated. ** Labeling method: Automated. ** Properties: The cnn_dailymail dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail.
Training Dataset:
** Data Modality: Undisclosed ** Data Collection Method by dataset: Undisclosed ** Labeling Method by dataset: Undisclosed ** Properties: Undisclosed
Testing Dataset:
** Data Collection Method by dataset: Undisclosed ** Labeling Method by dataset: Undisclosed ** Properties: Undisclosed
Evaluation Dataset:
** Data Collection Method by dataset: Hybrid: Human, Automated ** Labeling Method by dataset: Hybrid: Human, Automated ** Properties: Evaluated on benchmarks including GPQA, which is a dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry.
Inference:
Engine: SGLang Test Hardware: B300
Post Training Quantization
This model was obtained by quantizing the weights and activations of Qwen3.6-27B to NVFP4 data type, ready for inference with SGLang. Only the weights and activations of the linear operators within transformer blocks are quantized.
Usage
To serve this checkpoint with SGLang, you need to use the latest main branch and run the sample command below:
sglang serve --model-path mmangkad/Qwen3.6-27B-NVFP4 --reasoning-parser qwen3 --tool-call-parser qwen3_coder --quantization modelopt_fp4
Model Limitations:
The base model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses, especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.
Ethical Considerations
Trustworthy AI is a shared responsibility. When downloaded or used in accordance with the Apache 2.0 license, developers should work to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.