fix outdated link and shape typo in main demo (#251)

* fix dead link

* fix shape type

demos/Main_Demo.ipynb

@@ -537,7 +537,7 @@
                 "The IOI task is the task of identifying that a sentence like \"After John and Mary went to the store, Mary gave a bottle of milk to\" continues with \" John\" rather than \" Mary\" (ie, finding the indirect object), and Redwood Research have [an excellent paper studying the underlying circuit in GPT-2 Small](https://arxiv.org/abs/2211.00593).\n",
                 "\n",
                 "**[Activation patching](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J#z=qeWBvs-R-taFfcCq-S_hgMqx)** is a technique from [Kevin Meng and David Bau's excellent ROME paper](https://rome.baulab.info/). The goal is to identify which model activations are important for completing a task. We do this by setting up a **clean prompt** and a **corrupted prompt** and a **metric** for performance on the task. We then pick a specific model activation, run the model on the corrupted prompt, but then *intervene* on that activation and patch in its value when run on the clean prompt. We then apply the metric, and see how much this patch has recovered the clean performance. \n",
-                "(See [a more detailed demonstration of activation patching here](https://colab.research.google.com/github/neelnanda-io/Easy-Transformer/blob/main/Exploratory_Analysis_Demo.ipynb#scrollTo=5nUXG6zqmd0f))"
+                "(See [a more detailed demonstration of activation patching here](https://colab.research.google.com/github.com/neelnanda-io/TransformerLens/blob/main/demos/Exploratory_Analysis_Demo.ipynb))"
             ]
         },
         {
@@ -1167,7 +1167,7 @@
                 "* The weights (W_K, W_Q, W_V) mapping the residual stream to queries, keys and values are 3 separate matrices, rather than big concatenated one.\n",
                 "* The weight matrices (W_K, W_Q, W_V, W_O) and activations (keys, queries, values, z (values mixed by attention pattern)) have separate head_index and d_head axes, rather than flattening them into one big axis.\n",
                 "    * The activations all have shape `[batch, position, head_index, d_head]`\n",
-                "    * W_K, W_Q, W_V have shape `[head_index, d_head, d_model]` and W_O has shape `[head_index, d_model, d_head]`\n",
+                "    * W_K, W_Q, W_V have shape `[head_index, d_model, d_head]` and W_O has shape `[head_index, d_head, d_model]`\n",
                 "\n",
                 "The actual code is a bit of a mess, as there's a variety of Boolean flags to make it consistent with the various different model families in TransformerLens - to understand it and the internal structure, I instead recommend reading the code in [CleanTransformerDemo](https://colab.research.google.com/github/neelnanda-io/TransformerLens/blob/clean-transformer-demo/Clean_Transformer_Demo.ipynb)"
             ]