Update README.md

README.md

@@ -10,11 +10,11 @@ tags:
   - pytorch
 ---
 
-# OpenCode-Nemotron-14B Overview
+# OpenCodeReasoning-Nemotron-14B Overview
 
 ## Description 
 
-OpenCode-Nemotron-14B is a large language model (LLM) which is a derivative of [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) (AKA the *reference model*). 
+OpenCodeReasoning-Nemotron-14B is a large language model (LLM) which is a derivative of [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) (AKA the *reference model*). 
 It is a reasoning model that is post trained for reasoning while code generation. The model supports a context length of 32K tokens.
 
 This model is ready for commercial use. 
@@ -29,7 +29,7 @@ Global<br>
 This model is intended for developers and researchers building LLMs. <br>
 
 ### Release Date:  <br>
-Huggingface [04/25/2025] via https://huggingface.co/nvidia/OpenCode-Nemotron-14B/ <br>
+Huggingface [04/25/2025] via https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-14B/ <br>
 
 
 ## References
@@ -70,13 +70,13 @@ Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated sys
 
 
 ## Training Dataset
-The training corpus for OpenCode-Nemotron-14B is [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) dataset, which is composed of competitive programming questions and DeepSeek-R1 generated responses.
+The training corpus for OpenCodeReasoning-Nemotron-14B is [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) dataset, which is composed of competitive programming questions and DeepSeek-R1 generated responses.
 * Data Collection Method: Hybrid: Automated, Human, Synthetic <br>
 * Data Labeling Method: Hybrid: Automated, Human, Synthetic <br>
 
 
 ## Evaluation Dataset
-We used the datasets listed in the next section to evaluate OpenCodeReasoning-32B. <br>
+We used the datasets listed in the next section to evaluate OpenCodeReasoning-Nemotron-14B. <br>
 * Data Collection Method: Hybrid: Automated, Human, Synthetic <br>
 * Data Labeling Method: Hybrid: Automated, Human, Synthetic <br>