Fixed size 32B -> 14B

fetched the wrong model card - 32B is still being quantized

README.md

@@ -1,6 +1,6 @@
 ---
 base_model:
-- nvidia/OpenCodeReasoning-Nemotron-32B
+- nvidia/OpenCodeReasoning-Nemotron-14B
 datasets:
 - nvidia/OpenCodeReasoning
 language:
@@ -17,13 +17,14 @@ AWQ quantization: done by stelterlab in INT4 GEMM with AutoAWQ by casper-hansen
 
 Original Weights by Qwen AI. Original Model Card follows:
 
-# OpenCodeReasoning-Nemotron-32B Overview
+# OpenCodeReasoning-Nemotron-14B Overview
 
 ## Description: <br>
-OpenCodeReasoning-Nemotron-32B 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 32K tokens. <br>
+OpenCodeReasoning-Nemotron-14B is a large language model (LLM) which is a derivative of Qwen2.5-14B-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 32K tokens. <br>
 
 This model is ready for commercial/non-commercial use. <br>
 
+
 ![Evaluation Results](./results.png)
 
 
@@ -75,7 +76,7 @@ To run inference on coding problems:
 import transformers
 import torch
 
-model_id = "nvidia/OpenCodeReasoning-Nemotron-32B"
+model_id = "nvidia/OpenCodeReasoning-Nemotron-14B"
 
 pipeline = transformers.pipeline(
     "text-generation",
@@ -112,6 +113,7 @@ print(outputs[0]["generated_text"][-1]['content'])
 
 
 
+
 ## Citation
 
 If you find the data useful, please cite:
@@ -131,10 +133,10 @@ If you find the data useful, please cite:
 
 ## Model Architecture: <br>
 Architecture Type: Dense decoder-only Transformer model
-Network Architecture: Qwen-32B-Instruct 
+Network Architecture: Qwen-14B-Instruct 
 <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>**
+**This model was developed based on Qwen2.5-14B-Instruct and has 14B model parameters. <br>**
+**OpenCodeReasoning-Nemotron-14B was developed based on Qwen2.5-14B-Instruct and has 14B model parameters. <br>**
 
 ## Input: <br>
 **Input Type(s):** Text <br>
@@ -169,19 +171,21 @@ OpenCodeReasoning-Nemotron-32B-IOI<br>
 
 ## Training Dataset:
 
-The training corpus for OpenCodeReasoning-Nemotron-32B 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>
 Labeling Method: Hybrid: Automated, Human, Synthetic <br>
 Properties: 736k samples from OpenCodeReasoning (https://huggingface.co/datasets/nvidia/OpenCodeReasoning)
 
 ## Evaluation Dataset:
-We used the datasets listed in the next section to evaluate OpenCodeReasoning-Nemotron-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>**
 **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/OpenCode-Nemotron-2-7B/blob/main/LICENSE).
+GOVERNING TERMS: Use of this model is governed by [Apache 2.0](https://huggingface.co/nvidia/OpenCode-Nemotron-2-14B/blob/main/LICENSE).
 
 ### Deployment Geography:
 Global<br>
@@ -190,7 +194,7 @@ 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/OpenCodeReasoning-Nemotron-32B/ <br> 
+Huggingface [04/25/2025] via https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-7B/ <br> 
 
 ## Reference(s):
 [2504.01943] OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
@@ -203,4 +207,4 @@ Huggingface [04/25/2025] via https://huggingface.co/nvidia/OpenCodeReasoning-Nem
 ## 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.  
 
-Please report security vulnerabilities or NVIDIA AI Concerns here.
+Please report security vulnerabilities or NVIDIA AI Concerns here.