diff --git a/qdrant-landing/content/articles/fastembed.md b/qdrant-landing/content/articles/fastembed.md
index 945772a8b..8c45eb0cc 100644
--- a/qdrant-landing/content/articles/fastembed.md
+++ b/qdrant-landing/content/articles/fastembed.md
@@ -69,7 +69,7 @@ Note the use of prefixes “passage” and “query” to differentiate the type
 
 The use of text prefixes like “query” and “passage” isn’t merely syntactic sugar; it informs the algorithm on how to treat the text for embedding generation. A “query” prefix often triggers the model to generate embeddings that are optimized for similarity comparisons, while “passage” embeddings are fine-tuned for contextual understanding. If you omit the prefix, the default behavior is applied, although specifying it is recommended for more nuanced results.
 
-Next, we initialize the Embedding model with the default model: [BAAI/bge-small-en-v1.5](https://huggingface.co/baai/bge-small-en-v1.5) and specify a maximum token length of 512.
+Next, we initialize the Embedding model with the default model: [BAAI/bge-small-en-v1.5](https://huggingface.co/baai/bge-small-en-v1.5). 
 
 ```python
 embedding_model = DefaultEmbedding()
@@ -243,4 +243,4 @@ So, go ahead, take it for a test drive. We're excited to hear what you think!
 
 Lastly, If you find FastEmbed useful and want to keep up with what we're doing, giving our GitHub repo a star would mean a lot to us. Here's the link to [star the repository](https://github.com/qdrant/fastembed).
 
-If you ever have questions about FastEmbed, please ask them on the Qdrant Discord: [https://discord.gg/Qy6HCJK9Dc](https://discord.gg/Qy6HCJK9Dc)
+If you ever have questions about FastEmbed, please ask them on the Qdrant Discord: [https://discord.gg/Qy6HCJK9Dc](https://discord.gg/Qy6HCJK9Dc)
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