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Co-authored-by: Jessica <[email protected]>
Co-authored-by: ldecarvalho-doc <[email protected]>

Author: 3 people

pages/managed-inference/reference-content/deepseek-r1-distill-llama-70b.mdx

@@ -36,15 +36,15 @@ deepseek/deepseek-r1-distill-llama-70b:bf16
 
 ## Model introduction
 
-Released January 21, 2025, Deepseek’s R1 Distilled Llama 70B is a distilled version of Llama model family based on Deepseek R1.
-DeepSeek R1 Distill Llama 70B is designed to improve performance of Llama models on reasoning use case such as mathematics and coding tasks.
+Released January 21, 2025, Deepseek’s R1 Distilled Llama 70B is a distilled version of the Llama model family based on Deepseek R1.
+DeepSeek R1 Distill Llama 70B is designed to improve the performance of Llama models on reasoning use case such as mathematics and coding tasks.
 
 ## Why is it useful?
 
 It is great to see Deepseek improving open(weight) models, and we are excited to fully support their mission with integration in the Scaleway ecosystem.
 
 - DeepSeek-R1-Distill-Llama was optimized to reach accuracy close to Deepseek-R1 in tasks like mathematics and coding, while keeping inference costs limited and tokens speed efficient. 
-- DeepSeek-R1-Distill-Llama supports a context window up to 56K tokens and tool calling, keeping interaction with other components possible.
+- DeepSeek-R1-Distill-Llama supports a context window of up to 56K tokens and tool calling, keeping interaction with other components possible.
 
 ## How to use it
 
@@ -71,9 +71,9 @@ Make sure to replace `<IAM API key>` and `<Deployment UUID>` with your actual [I
   This model is better used without `system prompt`, as suggested by the model provider.
 </Message>
 
-### Receiving Inference responses
+### Receiving inference responses
 
-Upon sending the HTTP request to the public or private endpoints exposed by the server, you will receive inference responses from the managed Managed Inference server. 
+Upon sending the HTTP request to the public or private endpoints exposed by the server, you will receive inference responses from the Managed Inference server. 
 Process the output data according to your application's needs. The response will contain the output generated by the LLM model based on the input provided in the request.
 
 <Message type="note">

pages/managed-inference/reference-content/deepseek-r1-distill-llama-8b.mdx

@@ -37,15 +37,15 @@ deepseek/deepseek-r1-distill-llama-8b:bf16
 
 ## Model introduction
 
-Released January 21, 2025, Deepseek’s R1 Distilled Llama 8B is a distilled version of Llama model family based on Deepseek R1.
-DeepSeek R1 Distill Llama 8B is designed to improve performance of Llama models on reasoning use case such as mathematics and coding tasks.
+Released January 21, 2025, Deepseek’s R1 Distilled Llama 8B is a distilled version of the Llama model family based on Deepseek R1.
+DeepSeek R1 Distill Llama 8B is designed to improve the performance of Llama models on reasoning use cases such as mathematics and coding tasks.
 
 ## Why is it useful?
 
 It is great to see Deepseek improving open(weight) models, and we are excited to fully support their mission with integration in the Scaleway ecosystem.
 
 - DeepSeek-R1-Distill-Llama was optimized to reach accuracy close to Deepseek-R1 in tasks like mathematics and coding, while keeping inference costs limited and tokens speed efficient. 
-- DeepSeek-R1-Distill-Llama supports a context window up to 131K tokens and tool calling, keeping interaction with other components possible.
+- DeepSeek-R1-Distill-Llama supports a context window of up to 131K tokens and tool calling, keeping interaction with other components possible.
 
 ## How to use it
 
@@ -72,7 +72,7 @@ Make sure to replace `<IAM API key>` and `<Deployment UUID>` with your actual [I
   This model is better used without `system prompt`, as suggested by the model provider.
 </Message>
 
-### Receiving Inference responses
+### Receiving inference responses
 
 Upon sending the HTTP request to the public or private endpoints exposed by the server, you will receive inference responses from the managed Managed Inference server. 
 Process the output data according to your application's needs. The response will contain the output generated by the LLM model based on the input provided in the request.