Image resizing

Author: rasbt

ch04/08_deltanet/README.md

@@ -4,7 +4,7 @@ Recently, [Qwen3-Next](https://qwen.ai/blog?id=4074cca80393150c248e508aa62983f9c
 
 Both Qwen3-Next and Kimi Linear use a 3:1 ratio, meaning for every three transformer blocks employing the linear Gated DeltaNet variant, there’s one block that uses full attention, as shown in the figure below.
 
-<img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gated_deltanet/01.webp" alt="Qwen3-Next versus Kimi Linear" style="zoom:47%;" />
+<img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gated_deltanet/01.webp" alt="Qwen3-Next versus Kimi Linear">
 
 
 
@@ -125,7 +125,7 @@ The delta rule part refers to computing the difference (delta, Δ) between new a
 
 Gated DeltaNet has a gate similar to the gate in gated attention discussed earlier, except that it uses a SiLU instead of logistic sigmoid activation, as illustrated below. (The SiLU choice is likely to improve gradient flow and stability over the standard sigmoid.)
 
-<img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gated_deltanet/02.webp" alt="Gated DeltaNet" style="zoom:47%;" />
+<img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gated_deltanet/02.webp" alt="Gated DeltaNet" width=500px>
 
 However, as shown in the figure above, the "gated" in the Gated DeltaNet also refers to several additional gates:
 
@@ -271,7 +271,7 @@ context = context.reshape(b, num_tokens, self.d_out)
 
 
 
-<img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gated_deltanet/03.webp" alt="Quadratic attention" style="zoom:67%;" />
+<img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gated_deltanet/03.webp" alt="Quadratic attention" width=500px />
 
 In Gated DeltaNet, there's no  *n*-by-*n* attention matrix. Instead, the model processes tokens one by one. It keeps a running memory (a state) that gets updated as each new token comes in. This is what's implemented as, where `S` is the state that gets updated recurrently for each time step *t*.
 
@@ -353,4 +353,4 @@ uv run plot_memory_estimates_gated_deltanet.py \
 
 Note that the above computes the `head_dim` as `emb_dim / n_heads`. I.e., 2048 / 16  = 128.
 
-<img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gated_deltanet/plot.webp" alt="Gated DeltaNet scaling" style="zoom:47%;" />
+<img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gated_deltanet/plot.webp" alt="Gated DeltaNet scaling" width=500px>