Update ts example; use camelCase in python

1_python/1_llm-prediction/completion.md

@@ -43,7 +43,7 @@ Once you have a loaded model, you can generate completions by passing a string t
       language: python
       code: |
         # The `chat` object is created in the previous step.
-        prediction_stream = model.complete_stream("My name is", config={"max_tokens": 100})
+        prediction_stream = model.complete_stream("My name is", config={"maxTokens": 100})
 
         for fragment in prediction_stream:
             print(fragment.content, end="", flush=True)
@@ -53,7 +53,7 @@ Once you have a loaded model, you can generate completions by passing a string t
       language: python
       code: |
         # The `chat` object is created in the previous step.
-        result = model.complete("My name is", config={"max_tokens": 100})
+        result = model.complete("My name is", config={"maxTokens": 100})
 
         print(result)
 ```

2_typescript/2_llm-prediction/completion.md

@@ -32,7 +32,9 @@ Once you have a loaded model, you can generate completions by passing a string t
     Streaming:
       language: typescript
       code: |
-        const completion = model.complete("My name is");
+        const completion = model.complete("My name is", {
+          maxTokens: 100,
+        });
 
         for await (const { content } of completion) {
           process.stdout.write(content);
@@ -43,7 +45,9 @@ Once you have a loaded model, you can generate completions by passing a string t
     "Non-streaming":
       language: typescript
       code: |
-        const completion = await model.complete("My name is");
+        const completion = await model.complete("My name is", {
+          maxTokens: 100,
+        });
 
         console.info(completion.content);
 ```