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README.md

@@ -21094,32 +21094,31 @@ model-index:
       value: 78.51132446157838
 ---
 
-        # mlx-community/modernbert-embed-base-8bit
+# mlx-community/modernbert-embed-base-8bit
 
-        The Model [mlx-community/modernbert-embed-base-8bit](https://huggingface.co/mlx-community/modernbert-embed-base-8bit) was converted to MLX format from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) using mlx-lm version **0.0.3**.
+The Model [mlx-community/modernbert-embed-base-8bit](https://huggingface.co/mlx-community/modernbert-embed-base-8bit) was converted to MLX format from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) using mlx-lm version **0.0.3**.
 
-        ## Use with mlx
+## Use with mlx
 
-        ```bash
-        pip install mlx-embeddings
-        ```
+```bash
+pip install mlx-embeddings
+```
 
-        ```python
-        from mlx_embeddings import load, generate
-        import mlx.core as mx
+```python
+from mlx_embeddings import load, generate
+import mlx.core as mx
 
-        model, tokenizer = load('mlx-community/modernbert-embed-base-8bit')
+model, tokenizer = load("mlx-community/modernbert-embed-base-8bit")
 
-        # For text embeddings
-        output = generate(model, processor, texts=["I like grapes", "I like fruits"])
-        embeddings = output.text_embeds  # Normalized embeddings
+# For text embeddings
+output = generate(model, processor, texts=["I like grapes", "I like fruits"])
+embeddings = output.text_embeds  # Normalized embeddings
 
-        # Compute dot product between normalized embeddings
-        similarity_matrix = mx.matmul(embeddings, embeddings.T)
+# Compute dot product between normalized embeddings
+similarity_matrix = mx.matmul(embeddings, embeddings.T)
 
-        print("
-Similarity matrix between texts:")
-        print(similarity_matrix)
+print("Similarity matrix between texts:")
+print(similarity_matrix)
 
 
-        ```
+```