diff --git a/docs/getting_started.md b/docs/getting_started.md
index 2df0f08..476bb56 100644
--- a/docs/getting_started.md
+++ b/docs/getting_started.md
@@ -14,7 +14,7 @@ cd hfppl
 pip install .
 ```
 
-You can then run an example. The first time you run it, the example may ask to downlaod model weights from the HuggingFace model repository. 
+You can then run an example. The first time you run it, the example may ask to downlaod model weights from the HuggingFace model repository.
 
 ```
 python examples/hard_constraints.py
@@ -39,34 +39,34 @@ 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)
-        
+
         # The forbidden letter
         self.forbidden_tokens = [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(), 
+        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)
 
         # Check for EOS or end of sentence
-        if token.token_id == self.lm.tokenizer.eos_token_id or str(token) in ['.', '!', '?']:
+        if token.token_id == self.context.lm.tokenizer.eos_token_id 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()
@@ -94,7 +94,7 @@ model = MyModel(lm, "The weather today is expected to be", "e")
 particles = asyncio.run(smc_steer(model, 5, 3)) # number of particles N, and beam factor K
 ```
 
-Each returned particle is an instance of the `MyModel` class that has been `step`-ped to completion. 
+Each returned particle is an instance of the `MyModel` class that has been `step`-ped to completion.
 The generated strings can be printed along with the particle weights:
 
 ```python