fix

Author: santiagxf

articles/ai-studio/how-to/deploy-models-deepseek.md

@@ -14,7 +14,7 @@ ms.custom: references_regions, generated
 zone_pivot_groups: azure-ai-model-catalog-samples-chat
 ---
 
-# How to use DeepSeek-R1
+# How to use DeepSeek-R1 reasoning model
 
 [!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
 
@@ -25,7 +25,7 @@ DeepSeek-R1 excels at reasoning tasks using a step-by-step training process, suc
 
 ::: zone pivot="programming-language-python"
 
-## DeepSeek-R1
+## DeepSeek-R1 (preview)
 
 DeepSeek-R1 builds on the progress of earlier reasoning-focused models that improved performance by extending Chain-of-Thought (CoT) reasoning. DeepSeek-R1 takes things further by combining reinforcement learning (RL) with fine-tuning on carefully chosen datasets. It evolved from an earlier version, DeepSeek-R1-Zero, which relied solely on RL and showed strong reasoning skills but had issues like hard-to-read outputs and language inconsistencies. To address these limitations, DeepSeek-R1 incorporates a small amount of cold-start data and follows a refined training pipeline that blends reasoning-oriented RL with supervised fine-tuning on curated datasets, resulting in a model that achieves state-of-the-art performance on reasoning benchmarks.
 
@@ -277,7 +277,7 @@ except HttpResponseError as ex:
 
 ::: zone pivot="programming-language-javascript"
 
-## DeepSeek-R1
+## DeepSeek-R1 (preview)
 
 DeepSeek-R1 builds on the progress of earlier reasoning-focused models that improved performance by extending Chain-of-Thought (CoT) reasoning. DeepSeek-R1 takes things further by combining reinforcement learning (RL) with fine-tuning on carefully chosen datasets. It evolved from an earlier version, DeepSeek-R1-Zero, which relied solely on RL and showed strong reasoning skills but had issues like hard-to-read outputs and language inconsistencies. To address these limitations, DeepSeek-R1 incorporates a small amount of cold-start data and follows a refined training pipeline that blends reasoning-oriented RL with supervised fine-tuning on curated datasets, resulting in a model that achieves state-of-the-art performance on reasoning benchmarks.
 
@@ -550,7 +550,7 @@ catch (error) {
 
 ::: zone pivot="programming-language-csharp"
 
-## DeepSeek-R1
+## DeepSeek-R1 (preview)
 
 DeepSeek-R1 builds on the progress of earlier reasoning-focused models that improved performance by extending Chain-of-Thought (CoT) reasoning. DeepSeek-R1 takes things further by combining reinforcement learning (RL) with fine-tuning on carefully chosen datasets. It evolved from an earlier version, DeepSeek-R1-Zero, which relied solely on RL and showed strong reasoning skills but had issues like hard-to-read outputs and language inconsistencies. To address these limitations, DeepSeek-R1 incorporates a small amount of cold-start data and follows a refined training pipeline that blends reasoning-oriented RL with supervised fine-tuning on curated datasets, resulting in a model that achieves state-of-the-art performance on reasoning benchmarks.
 
@@ -843,7 +843,7 @@ catch (RequestFailedException ex)
 
 ::: zone pivot="programming-language-rest"
 
-## DeepSeek-R1
+## DeepSeek-R1 (preview)
 
 DeepSeek-R1 builds on the progress of earlier reasoning-focused models that improved performance by extending Chain-of-Thought (CoT) reasoning. DeepSeek-R1 takes things further by combining reinforcement learning (RL) with fine-tuning on carefully chosen datasets. It evolved from an earlier version, DeepSeek-R1-Zero, which relied solely on RL and showed strong reasoning skills but had issues like hard-to-read outputs and language inconsistencies. To address these limitations, DeepSeek-R1 incorporates a small amount of cold-start data and follows a refined training pipeline that blends reasoning-oriented RL with supervised fine-tuning on curated datasets, resulting in a model that achieves state-of-the-art performance on reasoning benchmarks.