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Bias Subcard

Participation considerations from adversely impacted groups protected classes in model design and testing:

Not Applicable

Measures taken to mitigate against unwanted bias:

Not Applicable

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Model Overview

NVIDIA CUDA Autocomplete is a fine-tuned version of Qwen/Qwen2.5-Coder-7B enhanced for CUDA code completion. The model takes as input two strings of code context: the prefix (code before the cursor) and the suffix (code after the cursor), and outputs several lines of code that logically continues the prefix. By analyzing the surrounding code structure, variable names, and CUDA-specific patterns, the model predicts the most likely next line of code, enabling intelligent autocomplete functionality for general programming and CUDA development in the Nsight Copilot extension for VSCode and Cursor.

This model is ready for commercial/non-commercial use.

License/Terms of Use

Use of this model is governed by the NVIDIA Open Model License Agreement.

Additional Information.For Qwen2.5-Coder-7B, Apache License, Version 2.0.

Deployment Geography

Global

Use Case

This model is intended to be used for code completion in the Nsight Copilot extension for VSCode / Cursor.

Release Date

Huggingface : 06/09/2026 via https://huggingface.co/nvidia/CUDA-Autocomplete

Reference(s)

Qwen2.5-Coder paper\nQwen2.5-Coder blog\nQwen2.5-Coder GitHub repository

Model Architecture

Architecture Type: Transformer\nNetwork Architecture: Qwen2ForCausalLM\nThis model was developed based on Qwen/Qwen2.5-Coder-7B.\nNumber of model parameters: 7B (7*10^9)

Input

Input Type(s): Code\nInput Format(s): String of code (meant for prefix code and suffix code)\nInput Parameters: One-Dimensional (1D)\nOther Properties Related to Input:

Context Window: The model processes sequential code text with prefix and suffix context
Encoding: UTF-8 text encoding
Input Structure: Fill-in-the-middle (FIM) format with prefix and suffix tokens

Output

Output Type(s): Code\nOutput Format: String\nOutput Parameters: One-Dimensional (1D)\nOther Properties Related to Output:

Output Length: Single line of code completion
Generation Method: Autoregressive token-by-token generation
Encoding: UTF-8 text encoding
Output Structure: Sequential code text that continues from the input prefix

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.

Software Integration

Runtime Engine(s): vLLM\nSupported Hardware Microarchitecture Compatibility:

H100
DGX Spark\n[Supported] Operating System(s): Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s)

v0.3.0

Training, Testing, and Evaluation Datasets

Training Dataset

Source: Subset of bigcode/the-stack-v2 & synthetically generated CUDA data using OSS models like GPT-OSS 120B
Data Modality: Text
Text Training Data Size: ~700000 samples
Data Collection Method by dataset: Hybrid: Automated, Synthetic
Labeling Method by dataset: Not Applicable
Properties (Quantity, Dataset Descriptions, Sensor(s)): ~700,000 samples. Text modality (source code). Content includes open-source CUDA and general programming code collected from permissive-licensed repositories, as well as machine-generated synthetic CUDA code produced by OSS models. Primarily English-language code with CUDA-specific constructs and APIs. No sensor data involved.

Testing Dataset

Source: NVIDIA Internal Data
Data Collection Method by dataset: Automated
Labeling Method by dataset: Not Applicable
Properties (Quantity, Dataset Descriptions, Sensor(s)): 2,156 samples. Text modality (source code). Content consists of internal proprietary CUDA and HPC library code (e.g., cuDNN, cuda-hpc) parsed from internal GitLab repositories. Code is CUDA-specific with domain-specific APIs and patterns. No sensor data involved.

Evaluation Dataset

Source: Subset of bigcode/the-stack-v2
Data Collection Method by dataset: Automated
Labeling Method by dataset: Not Applicable
Properties (Quantity, Dataset Descriptions, Sensor(s)): ~33,000 samples. Each sample corresponds to a single source code file. Text modality (source code). Content includes open-source code collected from permissive-licensed repositories. CUDA and general programming code in English. No sensor data involved.

Inference

Acceleration Engine: vLLM

Test Hardware:

H100
DGX Spark

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.\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

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The model takes as input two strings of code context: the prefix (code before the cursor) and the suffix (code after the cursor), and outputs several lines of code that logically continues the prefix. By analyzing the surrounding code structure, variable names, and CUDA-specific patterns, the model predicts the most likely next line of code, enabling intelligent autocomplete functionality for general programming and CUDA development in the Nsight Copilot extension for VSCode and Cursor.\n\n_This model is ready for commercial/non-commercial use._\n\n\n### License/Terms of Use\nUse of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\nAdditional Information.For Qwen2.5-Coder-7B, [Apache License, Version 2.0](https://huggingface.co/Qwen/Qwen2.5-Coder-7B/blob/main/LICENSE).\n\n### Deployment Geography\nGlobal\n\n### Use Case\nThis model is intended to be used for code completion in the Nsight Copilot extension for VSCode / Cursor.\n\n\n### Release Date\nHuggingface : 06/09/2026 via [https://huggingface.co/nvidia/CUDA-Autocomplete](https://huggingface.co/nvidia/CUDA-Autocomplete)\n\n## Reference(s)\n[Qwen2.5-Coder paper](https://arxiv.org/abs/2409.12186)\n[Qwen2.5-Coder blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/)\n[Qwen2.5-Coder GitHub repository](https://github.com/QwenLM/Qwen2.5-Coder)\n\n## Model Architecture\n**Architecture Type:** Transformer\n**Network Architecture:** Qwen2ForCausalLM\n**This model was developed based on Qwen/Qwen2.5-Coder-7B.**\n**Number of model parameters:** 7B (7*10^9)\n\n\n## Input\n**Input Type(s):** Code\n**Input Format(s):** String of code (meant for prefix code and suffix code)\n**Input Parameters:** One-Dimensional (1D)\n**Other Properties Related to Input:**\n- **Context Window:** The model processes sequential code text with prefix and suffix context\n- **Encoding:** UTF-8 text encoding\n- **Input Structure:** Fill-in-the-middle (FIM) format with prefix and suffix tokens\n\n\n## Output\n**Output Type(s):** Code\n**Output Format:** String\n**Output Parameters:** One-Dimensional (1D)\n**Other Properties Related to Output:**\n- **Output Length:** Single line of code completion\n- **Generation Method:** Autoregressive token-by-token generation\n- **Encoding:** UTF-8 text encoding\n- **Output Structure:** Sequential code text that continues from the input prefix\n\n\nOur 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.\n\n## Software Integration\n**Runtime Engine(s):** vLLM\n**Supported Hardware Microarchitecture Compatibility:**\n* H100\n* DGX Spark\n**[Supported] Operating System(s):** Linux\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.\n\n\n## Model Version(s)\nv0.3.0\n\n## Training, Testing, and Evaluation Datasets\n\n\n### Training Dataset\n\n* **Source:** Subset of [bigcode/the-stack-v2](https://huggingface.co/datasets/bigcode/the-stack-v2) & synthetically generated CUDA data using OSS models like GPT-OSS 120B\n* **Data Modality:** Text\n* **Text Training Data Size:** ~700000 samples\n* **Data Collection Method by dataset:** Hybrid: Automated, Synthetic\n* **Labeling Method by dataset:** Not Applicable\n* **Properties (Quantity, Dataset Descriptions, Sensor(s)):** ~700,000 samples. Text modality (source code). Content includes open-source CUDA and general programming code collected from permissive-licensed repositories, as well as machine-generated synthetic CUDA code produced by OSS models. Primarily English-language code with CUDA-specific constructs and APIs. No sensor data involved.\n\n### Testing Dataset\n* **Source:** NVIDIA Internal Data\n* **Data Collection Method by dataset:** Automated\n* **Labeling Method by dataset:** Not Applicable\n* **Properties (Quantity, Dataset Descriptions, Sensor(s)):** 2,156 samples. Text modality (source code). Content consists of internal proprietary CUDA and HPC library code (e.g., cuDNN, cuda-hpc) parsed from internal GitLab repositories. Code is CUDA-specific with domain-specific APIs and patterns. No sensor data involved.\n\n\n### Evaluation Dataset\n* **Source:** Subset of [bigcode/the-stack-v2](https://huggingface.co/datasets/bigcode/the-stack-v2)\n* **Data Collection Method by dataset:** Automated\n* **Labeling Method by dataset:** Not Applicable\n* **Properties (Quantity, Dataset Descriptions, Sensor(s)):** ~33,000 samples. Each sample corresponds to a single source code file. Text modality (source code). Content includes open-source code collected from permissive-licensed repositories. CUDA and general programming code in English. No sensor data involved.\n\n\n## Inference\n**Acceleration Engine:** vLLM\n\n**Test Hardware:**\n* H100\n* DGX Spark\n\n## Ethical Considerations\nNVIDIA 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.\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail).","displayName","CUDA Autocomplete",["D",1780818815236],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/blueprint/models/cuda-autocomplete","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms",{"_235":236,"_237":238,"_239":240,"_241":20,"_242":243},"isMultinodeEnabled","isMultiArchitecture","bias","# Bias Subcard\n## Participation considerations from adversely impacted groups protected classes in model design and testing:\nNot Applicable\n\n## Measures taken to mitigate against unwanted bias:\nNot Applicable","explainability","# Explainability Subcard\n## intended_domain\nCode generation in VSCode / Cursor.\n\n## Model Type\nTransformer\n\n## Intended Users\nThis model is designed for software developers, CUDA programmers, and AI enthusiasts who want to accelerate their coding workflow with intelligent code completion. Primary users include:\n- **CUDA Developers**: Engineers building GPU-accelerated applications, high-performance computing (HPC) solutions, or scientific computing applications who need assistance with CUDA-specific syntax and patterns.\n- **Software Engineers**: General programmers working in languages like Python, C++, and Java who want AI-powered code suggestions to boost productivity.\n- **Students and Learners**: Individuals learning CUDA programming or general software development who benefit from contextual code completion as a learning aid.\n- **AI/ML Engineers**: Developers building AI applications who need efficient coding assistance within the VSCode/Cursor development environment.\n\n## Output\nTypes: Several lines of code. Formats: Python, C++, Java, etc.\n\n## Describe how the model works:\nThe model accepts a block of code around the cursor (as left and right context) and predicts the sequence of code that is the best continuation of the code.\n\n\n## Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:\nNot Applicable\n\n## Technical Limitations & Mitigation:\nThe model is currently trained with a large distribution of C++, Python, and CUDA code. As the representation of other languages is limited, we recommend fine-tuning the model on more diverse languages and domains.\n\n## Verified to have met prescribed NVIDIA quality standards:\nYes\n\n## Performance Metrics:\nAcceptance rate of the code completion. Offline metrics include LLM-as-a-judge metrics and ROUGE-L score.\n\n## Potential Known Risks:\nThis model may occasionally generate incorrect responses or produce repetitive code. To mitigate this, mechanisms are in place to reduce repetition, and model parameters such as temperature and max_tokens have been carefully configured to minimize these risks.\n\n## Licensing:\nUse of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\nAdditional Information.For Qwen2.5-Coder-7B, [Apache License, Version 2.0](https://huggingface.co/Qwen/Qwen2.5-Coder-7B/blob/main/LICENSE).","privacy","# Privacy Subcard\n## Generatable or reverse engineerable personal data?\nNo\n\n## Personal data used to create this model?\nNo\n\n## How often is dataset reviewed?\nDataset is initially reviewed upon addition, and subsequent reviews are conducted as needed or upon request for changes.\n\n## Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model?\nNo\n\n## Is there provenance for all datasets used in training?\nYes\n\n## Does data labeling (annotation, metadata) comply with privacy laws?\nYes\n\n## Is data compliant with data subject requests for data correction or removal, if such a request was made?\nYes\n\n## Applicable Privacy Policy\nhttps://www.nvidia.com/en-us/about-nvidia/privacy-policy/","safetyAndSecurity","# Safety & Security Subcard\n## Model Application Field(s):\nCode generation in VSCode / Cursor.\n\n## Describe the life critical impact (if present).\nNot Applicable\n\n## Use Case Restrictions:\nUse of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\nAdditional Information.For Qwen2.5-Coder-7B, [Apache License, Version 2.0](https://huggingface.co/Qwen/Qwen2.5-Coder-7B/blob/main/LICENSE).\n\n## Model and dataset restrictions:\nThe Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.","featureLabels",[],"displayLabels",[196,197,198,199,200,201,202,203,204],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_159":232,"_206":233,"_207":234,"_209":210},{"_159":230,"_206":230,"_207":231,"_209":210},{"_159":226,"_206":227,"_207":228,"_209":210},{"_159":223,"_206":224,"_207":225,"_209":210},{"_159":221,"_206":221,"_207":222,"_209":210},{"_159":219,"_206":219,"_207":220,"_209":210},{"_159":217,"_206":217,"_207":218,"_209":210},{"_159":213,"_206":214,"_207":215,"_209":210},{"_159":205,"_206":205,"_207":208,"_209":210},"PyTorch","value","filter",{"_211":212,"_206":205},"category","label","field","framework","NVIDIA AI","soln_nvidia_ai",{"_211":216,"_206":214},"technology","NSPECT-KKK3-TWRN",{"_211":210,"_206":217},"NLP",{"_211":210,"_206":219},"False",{"_211":210,"_206":221},"DL","soln_dl",{"_211":216,"_206":224},"CUDA","pltfm_cuda",{"_211":229,"_206":227},"platform","Code Generation",{"_211":210,"_206":230},"AI","soln_ai",{"_211":216,"_206":233},"governingTerms","

Governing Terms:

Use of this model is governed by theNVIDIA Open Model License Agreement.

Additional Information.For Qwen2.5-Coder-7B,Apache License, Version 2.0.

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Governing Terms:

Use of this model is governed by theNVIDIA Open Model License Agreement.

Additional Information.For Qwen2.5-Coder-7B,Apache License, Version 2.0.

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N/A

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