diff --git a/outlines/models/transformers.py b/outlines/models/transformers.py
index 6b443a8fa..80ee813b9 100644
--- a/outlines/models/transformers.py
+++ b/outlines/models/transformers.py
@@ -4,28 +4,54 @@
 import torch
 from transformers.file_utils import SPIECE_UNDERLINE
 
-try:
-    from transformers.models.llama.tokenization_llama import LlamaTokenizer
-except ImportError:
+from outlines.models.tokenizer import Tokenizer
 
-    class LlamaTokenizer:  # type: ignore
-        pass
+if TYPE_CHECKING:
+    from transformers import PreTrainedModel, PreTrainedTokenizer
 
+__all__ = ["transformers"]
 
-try:
-    from transformers.models.llama.tokenization_llama_fast import LlamaTokenizerFast
-except ImportError:
 
-    class LlamaTokenizerFast:  # type: ignore
-        pass
+def get_llama_tokenizer_types():
+    """Get all the Llama tokenizer types/classes that need work-arounds.
 
+    When they can't be imported, a dummy class is created.
 
-from outlines.models.tokenizer import Tokenizer
+    """
+    try:
+        from transformers.models.llama import LlamaTokenizer
+    except ImportError:
 
-if TYPE_CHECKING:
-    from transformers import PreTrainedModel, PreTrainedTokenizer
+        class LlamaTokenizer:  # type: ignore
+            pass
 
-__all__ = ["transformers"]
+    try:
+        from transformers.models.llama import LlamaTokenizerFast
+    except ImportError:
+
+        class LlamaTokenizerFast:  # type: ignore
+            pass
+
+    try:
+        from transformers.models.code_llama import CodeLlamaTokenizer
+    except ImportError:
+
+        class CodeLlamaTokenizer:  # type: ignore
+            pass
+
+    try:
+        from transformers.models.code_llama import CodeLlamaTokenizerFast
+    except ImportError:
+
+        class CodeLlamaTokenizerFast:  # type: ignore
+            pass
+
+    return (
+        LlamaTokenizer,
+        LlamaTokenizerFast,
+        CodeLlamaTokenizer,
+        CodeLlamaTokenizerFast,
+    )
 
 
 class Transformers:
@@ -83,9 +109,7 @@ def __init__(self, model_name: str, **kwargs):
             self.pad_token = self.tokenizer.pad_token
 
         self.vocabulary = self.tokenizer.get_vocab()
-        self.is_sentencepiece = isinstance(
-            self.tokenizer, (LlamaTokenizerFast, LlamaTokenizer)
-        )
+        self.is_llama = isinstance(self.tokenizer, get_llama_tokenizer_types())
 
     def encode(
         self, prompt: Union[str, List[str]], **kwargs
@@ -102,9 +126,9 @@ def decode(self, token_ids: torch.LongTensor) -> List[str]:
     def convert_token_to_string(self, token: str) -> str:
         string = self.tokenizer.convert_tokens_to_string([token])
 
-        if self.is_sentencepiece:
-            # A hack to handle missing spaces from SentencePiece tokenizers
-            if token.startswith(SPIECE_UNDERLINE):
+        if self.is_llama:
+            # A hack to handle missing spaces to HF's Llama tokenizers
+            if token.startswith(SPIECE_UNDERLINE) or token == "<0x20>":
                 return " " + string
 
         return string
diff --git a/tests/models/test_transformers.py b/tests/models/test_transformers.py
index f44aca4c7..a7e9f9842 100644
--- a/tests/models/test_transformers.py
+++ b/tests/models/test_transformers.py
@@ -43,9 +43,13 @@ def test_llama_tokenizer():
 
     # Broken
     assert tokenizer.tokenizer.convert_tokens_to_string(["▁baz"]) == "baz"
+    assert tokenizer.tokenizer.convert_tokens_to_string(["<0x20>"]) == ""
+    assert tokenizer.tokenizer.convert_tokens_to_string(["▁▁▁"]) == "  "
 
     # Not broken
     assert tokenizer.convert_token_to_string("▁baz") == " baz"
+    assert tokenizer.convert_token_to_string("<0x20>") == " "
+    assert tokenizer.convert_token_to_string("▁▁▁") == "   "
 
 
 def test_model():