Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 base_model:
-- Qwen/Qwen2.5-7B-Instruct
+- Qwen/Qwen2.5-32B-Instruct
 datasets:
 - nvidia/OpenCodeReasoning
 language:
@@ -13,10 +13,10 @@ tags:
 pipeline_tag: text-generation
 ---
 
-# OpenCodeReasoning-Nemotron-7B-v1.1 Overview
+# OpenCodeReasoning-Nemotron-32B-v1.1 Overview
 
 ## Description: <br>
-OpenCodeReasoning-Nemotron-7B-v1.1 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 64k tokens. <br>
+OpenCodeReasoning-Nemotron-32B-v1.1 is a large language model (LLM) which is a derivative of Qwen2.5-32B-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 64k tokens. <br>
 
 This model is ready for commercial/non-commercial use. <br>
 
@@ -73,7 +73,7 @@ To run inference on coding problems:
 import transformers
 import torch
 
-model_id = "nvidia/OpenCodeReasoning-Nemotron-7B-v1.1"
+model_id = "nvidia/OpenCodeReasoning-Nemotron-32B-v1.1"
 
 pipeline = transformers.pipeline(
     "text-generation",
@@ -128,10 +128,10 @@ If you find the data useful, please cite:
 
 ## Model Architecture: <br>
 Architecture Type: Dense decoder-only Transformer model
-Network Architecture: Qwen-7B-Instruct 
+Network Architecture: Qwen-32B-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>**
+**This model was developed based on Qwen2.5-32B-Instruct and has 32B model parameters. <br>**
+**OpenCodeReasoning-Nemotron-32B was developed based on Qwen2.5-32B-Instruct and has 32B model parameters. <br>**
 
 ## Input: <br>
 **Input Type(s):** Text <br>
@@ -165,21 +165,21 @@ OpenCodeReasoning-Nemotron-32B-v1.1<br>
 
 ## Training Dataset:
 
-The training corpus for OpenCodeReasoning-Nemotron-7B-v1.1 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-32B-v1.1 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>
 Labeling Method: Hybrid: Automated, Human, Synthetic <br>
 Properties: 1.165M 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-v1.1. <br>
+We used the datasets listed in the next section to evaluate OpenCodeReasoning-Nemotron-32B-v1.1. <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](https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-7B-v1.1/blob/main/LICENSE).
+GOVERNING TERMS: Use of this model is governed by [Apache 2.0](https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-32B-v1.1/blob/main/LICENSE).
 
 ### Deployment Geography:
 Global<br>
@@ -188,7 +188,7 @@ Global<br>
 This model is intended for developers and researchers building LLMs. <br>
 
 ### Release Date:  <br>
-Huggingface [06/20/2025] via https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-7B-v1.1/ <br> 
+Huggingface [06/20/2025] via https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-32B-v1.1/ <br> 
 
 ## Reference(s):
 [2504.01943] OpenCodeReasoning: Advancing Data Distillation for Competitive Coding