Docs: Mark recommended sections

README.md

@@ -51,9 +51,9 @@ With compact __custom pre-trained transformer models__, this can run anywhere fr
 
 | Model                                    | Parameters | Languages |                                 Architecture |
 | :--------------------------------------- | ---------: | --------: | -------------------------------------------: |
-| [`uform-vl-english-large`][model-e-l]    |       365M |         1 | 6 text layers, ViT-L/14, 6 multimodal layers |
+| [`uform-vl-english-large`][model-e-l] 🆕  |       365M |         1 | 6 text layers, ViT-L/14, 6 multimodal layers |
 | [`uform-vl-english`][model-e]            |       143M |         1 | 2 text layers, ViT-B/16, 2 multimodal layers |
-| [`uform-vl-english-small`][model-e-s]    |        79M |         1 | 2 text layers, ViT-S/16, 2 multimodal layers |
+| [`uform-vl-english-small`][model-e-s] 🆕  |        79M |         1 | 2 text layers, ViT-S/16, 2 multimodal layers |
 | [`uform-vl-multilingual-v2`][model-m-v2] |       206M |        21 | 8 text layers, ViT-B/16, 4 multimodal layers |
 | [`uform-vl-multilingual`][model-m]       |       206M |        12 | 8 text layers, ViT-B/16, 4 multimodal layers |
 
@@ -67,7 +67,7 @@ With compact __custom pre-trained transformer models__, this can run anywhere fr
 
 | Model                              | Parameters |                     Purpose |           Architecture |
 | :--------------------------------- | ---------: | --------------------------: | ---------------------: |
-| [`uform-gen2-dpo`][model-g2]       |       1.2B | Chat, Image Captioning, VQA | qwen1.5-0.5B, ViT-H/14 |
+| [`uform-gen2-dpo`][model-g2] 🆕     |       1.2B | Chat, Image Captioning, VQA | qwen1.5-0.5B, ViT-H/14 |
 | [`uform-gen2-qwen-500m`][model-g2] |       1.2B | Chat, Image Captioning, VQA | qwen1.5-0.5B, ViT-H/14 |
 | [`uform-gen`][model-g1]            |       1.5B |       Image Captioning, VQA |   llama-1.3B, ViT-B/16 |
 
@@ -105,21 +105,28 @@ text_data = processor.preprocess_text(text)
 
 image_features, image_embedding = model.encode_image(image_data, return_features=True)
 text_features, text_embedding = model.encode_text(text_data, return_features=True)
+```
+
+To search for similar items, the embeddings can be compared using cosine similarity.
+The resulting value will fall within the range of `-1` to `1`, where `1` indicates a high likelihood of a match.
+PyTorch provides a built-in function for calculating cosine similarity, while for ONNX, you can use NumPy.
+
+```python
+import torch.nn.functional as F
 
-# For PyTorch
 similarity = F.cosine_similarity(image_embedding, text_embedding)
+```
+
+ONNX has no such function, but you can calculate the cosine similarity using [SimSIMD](https://github.com/ashvardanian/simsimd) or manually, with NumPy:
 
-# For ONNX
+```python
 import numpy as np
 
 image_embedding = image_embedding / np.linalg.norm(image_embedding, keepdims=True, axis=1)
 text_embedding = text_embedding / np.linalg.norm(text_embedding, keepdims=True, axis=1)
 similarity = (image_embedding * text_embedding).sum(axis=1)
 ```
 
-To search for similar items, the embeddings can be compared using cosine similarity.
-The resulting value will fall within the range of `-1` to `1`, where `1` indicates a high likelihood of a match.
-
 ### Reranking
 
 Once the list of nearest neighbors (best matches) is obtained, the joint multimodal embeddings, created from both text and image features, can be used to better rerank (reorder) the list.
@@ -161,7 +168,7 @@ tiny_embedding: np.ndarray = large_embedding[:, :64]
 ```
 
 Both approaches are natively supported by the [USearch][github-usearch] vector-search engine and the [SimSIMD][github-simsimd] numerics libraries.
-When dealing with small collections (up to millions of entries) and looking for low-latency cosine distance calculations, you can [achieve 5x-2500x performance improvement over Torch, NumPy, SciPy, and vanilla Python using SimSIMD][report-simsimd].
+When dealing with small collections (up to millions of entries) and looking for low-latency cosine distance calculations, you can [achieve 5x-2500x performance improvement][report-simsimd] over Torch, NumPy, SciPy, and vanilla Python using SimSIMD.
 
 ```python
 from simsimd import cosine, hamming
@@ -172,7 +179,7 @@ distance: float = cosine(i8_embedding, i8_embedding) # 133x SciPy performance on
 distance: float = hamming(b1_embedding, b1_embedding) # 17x SciPy performance on Apple M2 CPU
 ```
 
-Similarly, when dealing with large collections (up to billions of entries per server) and looking for high-throughput search, you can [achieve 100x performance improvement over FAISS and other vector-search solutions using USearch][report-usearch].
+Similarly, when dealing with large collections (up to billions of entries per server) and looking for high-throughput search, you can [achieve 100x performance improvement][report-usearch] over FAISS and other vector-search solutions using USearch.
 Here are a couple of examples:
 
 ```python