fix token count

lecture.md

@@ -101,7 +101,7 @@ Next, let's load this [tokenization webapp](https://tiktokenizer.vercel.app). Wh
 
 So for example, the string "Tokenization" encoded into the tokens 30642 followed by the token 1634. The token " is" (note that these is three characters, including the space in the front, this is important!) is index 318. Be careful with whitespace because it is absolutely present in the string and must be tokenized along with all the other characters, but is usually omitted in visualization for clarity. You can toggle on and off its visualization at the bottom of the app. In the same way, the token " at" is 379, " the" is 262, etc.
 
-Next, we have a simple example of some arithmetic. Here, we see that numbers may be inconsistently decomposed by the tokenizer. For example, the number 127 is a single token of three characters, but the number 677 because two tokens: the token " 6" (again, note the space in the front!) and the token "77". We rely on the large language model to make sense of this arbitrariness. It has to learn inside its parameters and during training that these two tokens (" 6" and "77" actually combine to create the number 677). In the same way, we see that if the LLM wanted to predict that the result of this sum is the number 804, it would have to output that in two time steps: first it has to emit the token " 8", and then the token "04". Note that all of these splits look completely arbitrary. In the example right below, we see that 1275 is "12" followed by "75", 6773 is actually three tokens " 6", "77", "3", and 8041 is " 8", "041".
+Next, we have a simple example of some arithmetic. Here, we see that numbers may be inconsistently decomposed by the tokenizer. For example, the number 127 is a single token of three characters, but the number 677 because two tokens: the token " 6" (again, note the space in the front!) and the token "77". We rely on the large language model to make sense of this arbitrariness. It has to learn inside its parameters and during training that these two tokens (" 6" and "77" actually combine to create the number 677). In the same way, we see that if the LLM wanted to predict that the result of this sum is the number 804, it would have to output that in two time steps: first it has to emit the token " 8", and then the token "04". Note that all of these splits look completely arbitrary. In the example right below, we see that 1275 is "12" followed by "75", 6773 is actually two tokens " 6", "773", and 8041 is " 8", "041".
 
 (to be continued...)
 (TODO: may continue this unless we figure out how to generate it automatically from the video :))