diff --git a/model/supervised_finetuning/utils.py b/model/supervised_finetuning/utils.py
index f7a0ab15..c3b8264f 100644
--- a/model/supervised_finetuning/utils.py
+++ b/model/supervised_finetuning/utils.py
@@ -1,5 +1,6 @@
 # from functools import partial
 from pathlib import Path
+from typing import NamedTuple
 
 import evaluate
 
@@ -16,19 +17,43 @@ from sklearn.model_selection import train_test_split
 from torch.utils.data import ConcatDataset, Subset
 
 
-def get_tokenizer(conf):
-    tokenizer = transformers.AutoTokenizer.from_pretrained(conf.model_name, cache_dir=conf.cache_dir)
+class SpecialTokens(NamedTuple):
+    pad_token: str = ""
+    eos_token: str = ""
+    sep_token: str = ""
 
-    if "galactica" in conf.model_name:
-        tokenizer.add_special_tokens({"pad_token": "<pad>", "eos_token": "</s>"})
-    elif "GPT-JT" in conf.model_name:
-        tokenizer.add_special_tokens({"pad_token": tokenizer.eos_token, "sep_token": "<|extratoken_100|>"})
-    elif "codegen" in conf.model_name:
-        tokenizer.add_special_tokens({"pad_token": "<|endoftext|>", "sep_token": "<|endoftext|>"})
-    elif "pythia" in conf.model_name:
-        tokenizer.add_special_tokens(
-            {"pad_token": "<|padding|>", "sep_token": "<|endoftext|>", "eos_token": "<|endoftext|>"}
-        )
+
+class TokenizerConfig(NamedTuple):
+    special_tokens: SpecialTokens = {}
+
+
+TOKENIZER_CONFIGS = {
+    "galactica": TokenizerConfig(special_tokens=SpecialTokens("<pad>", "</s>")),
+    "GPT-JT": TokenizerConfig(special_tokens=SpecialTokens(sep_token="<|extratoken_100|>")),
+    "codegen": TokenizerConfig(special_tokens=SpecialTokens("<|endoftext|>", sep_token="<|endoftext|>")),
+    "pythia": TokenizerConfig(special_tokens=SpecialTokens("<|padding|>", "<|endoftext|>", "<|endoftext|>")),
+}
+
+
+def match_tokenizer_name(model_name: str) -> TokenizerConfig:
+    """Match a partial model name to a tokenizer configuration"""
+    tokenizer_config_matches = [config for name, config in TOKENIZER_CONFIGS.items() if name in model_name]
+    if not tokenizer_config_matches:
+        raise ValueError(f"Cannot find any tokeniser configuration to match {model_name=}")
+    elif 1 < len(tokenizer_config_matches):
+        raise ValueError(f"Found multiple tokeniser configuration matches for {model_name=}")
+    else:
+        return tokenizer_config_matches[0]
+
+
+def get_tokenizer(conf) -> transformers.AutoTokenizer:
+    tokenizer = transformers.AutoTokenizer.from_pretrained(conf.model_name, cache_dir=conf.cache_dir)
+    tokenizer_config = match_tokenizer_name(conf.model_name)
+
+    if tokenizer_config.special_tokens:
+        if "GPT-JT" in conf.model_name:
+            tokenizer_config.special_tokens.pad_token = tokenizer.eos_token
+        tokenizer.add_special_tokens(tokenizer_config.special_tokens)
 
     additional_special_tokens = (
         []