Update 'Getting Started' doc to use same example as README

Author: alex-lew

docs/getting_started.md

@@ -33,48 +33,39 @@ To do so, we write subclass the [`Model`](hfppl.modeling.Model) class:
 ```python
 # examples/no_e.py
 
-from hfppl import Model, LMContext, TokenCategorical, CachedCausalLM
+from hfppl import Model, LMContext, CachedCausalLM
 
 # A LLaMPPL model subclasses the Model class
 class MyModel(Model):
 
     # The __init__ method is used to process arguments
     # and initialize instance variables.
     def __init__(self, lm, prompt, forbidden_letter):
-
-        # Always call the superclass's __init__.
         super().__init__()
 
         # A stateful context object for the LLM, initialized with the prompt
         self.context = LMContext(lm, prompt)
-
+        self.eos_token = lm.tokenizer.eos_token_id
+        
         # The forbidden letter
-        self.forbidden_tokens = [i for (i, v) in enumerate(lm.vocab)
-                                   if forbidden_letter in v]
-
+        self.forbidden_tokens = set(i for (i, v) in enumerate(lm.vocab)
+                                      if forbidden_letter in v)
+    
     # The step method is used to perform a single 'step' of generation.
     # This might be a single token, a single phrase, or any other division.
     # Here, we generate one token at a time.
     async def step(self):
-        # Sample a token from the LLM -- automatically extends `self.context`.
-        # We use `await` so that LLaMPPL can automatically batch language model calls.
-        token = await self.sample(self.context.next_token(),
-                                  proposal=self.proposal())
-
-        # Condition on the token not having the forbidden letter
-        self.condition(token.token_id not in self.forbidden_tokens)
+        # Condition on the next token *not* being a forbidden token.
+        await self.observe(self.context.mask_dist(self.forbidden_tokens), False)
+        
+        # Sample the next token from the LLM -- automatically extends `self.context`.
+        token = await self.sample(self.context.next_token())
 
         # Check for EOS or end of sentence
-        if token.token_id == self.context.lm.tokenizer.eos_token_id or str(token) in ['.', '!', '?']:
+        if token.token_id == self.eos_token or str(token) in ['.', '!', '?']:
             # Finish generation
             self.finish()
 
-    # Helper method to define a custom proposal
-    def proposal(self):
-        logits = self.context.next_token_logprobs.copy()
-        logits[self.forbidden_tokens] = -float('inf')
-        return TokenCategorical(self.context.lm, logits)
-
     # To improve performance, a hint that `self.forbidden_tokens` is immutable
     def immutable_properties(self):
         return set(['forbidden_tokens'])