Updated ch07 (#213)

* Updated ch07

* fix links

* check links

ch07/03_model-evaluation/llm-instruction-eval-ollama.ipynb

@@ -178,6 +178,14 @@
     "- Next, run the following code cell to query the model"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "16642a48-1cab-40d2-af08-ab8c2fbf5876",
+   "metadata": {},
+   "source": [
+    "- First, let's try the API with a simple example to make sure it works as intended:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -246,14 +254,6 @@
     "print(result)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "16642a48-1cab-40d2-af08-ab8c2fbf5876",
-   "metadata": {},
-   "source": [
-    "- First, let's try the API with a simple example to make sure it works as intended:"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "162a4739-6f03-4092-a5c2-f57a0b6a4c4d",
@@ -571,7 +571,7 @@
    "id": "b071ce84-1866-427f-a272-b46700f364b2",
    "metadata": {},
    "source": [
-    "- Let's now apply this evaluation to the whole dataset and compute the average score of each model (this takes about 1 minute per model on a M3 MacBook Air laptop)\n",
+    "- Let's now apply this evaluation to the whole dataset and compute the average score of each model (this takes about 1 minute per model on an M3 MacBook Air laptop)\n",
     "- Note that ollama is not fully deterministic (as of this writing) so the numbers you are getting might slightly differ from the ones shown below"
    ]
   },
@@ -666,7 +666,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,