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@@ -136,13 +136,13 @@ Network Architecture: Qwen-7B-Instruct
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  **Input Type(s):** Text <br>
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  **Input Format(s):** String <br>
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  **Input Parameters:** One-Dimensional (1D) <br>
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- **Other Properties Related to Input:** Context length up to 32,768 tokens <br>
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  ## Output: <br>
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  **Output Type(s):** Text <br>
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  **Output Format:** String <br>
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  **Output Parameters:** One-Dimensional (1D) <br>
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- **Other Properties Related to Output:** Context length up to 32,768 tokens <br>
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  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>
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@@ -168,17 +168,17 @@ The training corpus for OpenCodeReasoning-Nemotron-7B-v1.1 is [OpenCodeReasoning
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  Data Collection Method: Hybrid: Automated, Human, Synthetic <br>
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  Labeling Method: Hybrid: Automated, Human, Synthetic <br>
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- Properties: 736k samples from OpenCodeReasoning (https://huggingface.co/datasets/nvidia/OpenCodeReasoning)
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  ## Evaluation Dataset:
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- We used the datasets listed in the next section to evaluate OpenCodeReasoning-Nemotron-7B. <br>
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  **Data Collection Method: Hybrid: Automated, Human, Synthetic <br>**
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  **Labeling Method: Hybrid: Automated, Human, Synthetic <br>**
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  ### License/Terms of Use: <br>
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- GOVERNING TERMS: Use of this model is governed by [Apache 2.0](https://huggingface.co/nvidia/OpenCode-Nemotron-2-7B/blob/main/LICENSE).
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  ### Deployment Geography:
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  Global<br>
 
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  **Input Type(s):** Text <br>
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  **Input Format(s):** String <br>
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  **Input Parameters:** One-Dimensional (1D) <br>
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+ **Other Properties Related to Input:** Context length up to 65,536 tokens <br>
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  ## Output: <br>
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  **Output Type(s):** Text <br>
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  **Output Format:** String <br>
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  **Output Parameters:** One-Dimensional (1D) <br>
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+ **Other Properties Related to Output:** Context length up to 65,536 tokens <br>
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  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>
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  Data Collection Method: Hybrid: Automated, Human, Synthetic <br>
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  Labeling Method: Hybrid: Automated, Human, Synthetic <br>
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+ Properties: 1.165M samples from OpenCodeReasoning (https://huggingface.co/datasets/nvidia/OpenCodeReasoning)
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  ## Evaluation Dataset:
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+ We used the datasets listed in the next section to evaluate OpenCodeReasoning-Nemotron-7B-v1.1. <br>
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  **Data Collection Method: Hybrid: Automated, Human, Synthetic <br>**
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  **Labeling Method: Hybrid: Automated, Human, Synthetic <br>**
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  ### License/Terms of Use: <br>
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+ GOVERNING TERMS: Use of this model is governed by [Apache 2.0](https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-7B-v1.1/blob/main/LICENSE).
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  ### Deployment Geography:
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  Global<br>