diff --git a/test/external/external_llm_eval.py b/test/external/external_llm_eval.py
index 619cd631f6..ed617cfae4 100644
--- a/test/external/external_llm_eval.py
+++ b/test/external/external_llm_eval.py
@@ -10,7 +10,7 @@ if __name__ == "__main__":
 
   model, kv = Transformer.from_gguf(Tensor.from_url(models["1B"]), max_context=4096)
 
-  tok = SimpleTokenizer(kv["tokenizer.ggml.tokens"])
+  tok = SimpleTokenizer.from_gguf_kv(kv)
   bos_id: int = kv['tokenizer.ggml.bos_token_id']
   eos_id: int = kv['tokenizer.ggml.eos_token_id']
 
diff --git a/test/external/external_test_simple_tokenizer.py b/test/external/external_test_simple_tokenizer.py
index b3e33e0d4d..9c3ca8f420 100644
--- a/test/external/external_test_simple_tokenizer.py
+++ b/test/external/external_test_simple_tokenizer.py
@@ -1,17 +1,19 @@
 from transformers import AutoTokenizer
 from datasets import load_dataset
-from tinygrad.apps.llm import SimpleTokenizer
-from tinygrad.helpers import tqdm, getenv
+from tinygrad.apps.llm import SimpleTokenizer, gpt2_decode_vocab, get_llama_re
+from tinygrad.helpers import tqdm, getenv, partition
 
 # use ALLOW_FAILED=-1 to go over the entire dataset without printing.
 if __name__ == "__main__":
   base_tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B-Instruct")
-  vocab_words = [ word for word, _ in sorted(base_tokenizer.get_vocab().items(), key=lambda t: t[1]) ]
+  special_tokens, normal_tokens = partition(((t, tid) for t, tid in base_tokenizer.vocab.items()),
+    lambda e: e[1] in base_tokenizer.all_special_ids)
   inv_vocab = { tid: word for word, tid in base_tokenizer.get_vocab().items() }
-  simple_tokenizer = SimpleTokenizer(vocab_words)
+  simple_tokenizer = SimpleTokenizer(get_llama_re(), gpt2_decode_vocab(dict(normal_tokens)), dict(special_tokens))
 
   color_codes = [ 91, 92, 94, 93, 95 ]
-  def color_tokens(tids): return "".join(f"\033[{color_codes[i%len(color_codes)]}m{inv_vocab[t]}" for i, t in enumerate(tids)) + "\033[0m"
+  def color_tokens(tids):
+    return "".join(f"\033[{color_codes[i%len(color_codes)]}m{base_tokenizer.decode([t])}" for i, t in enumerate(tids)) + "\033[0m"
 
   ds = load_dataset("OpenAssistant/oasst1")
   allow_failed = getenv("ALLOW_FAILED", 10)
diff --git a/test/unit/test_llm_tokenizer.py b/test/unit/test_llm_tokenizer.py
new file mode 100644
index 0000000000..fca60bd7bb
--- /dev/null
+++ b/test/unit/test_llm_tokenizer.py
@@ -0,0 +1,57 @@
+import unittest, base64, functools
+from tinygrad.apps.llm import SimpleTokenizer, get_llama_re
+from tinygrad.helpers import fetch
+
+class TestLLMTokenizer(unittest.TestCase):
+  @functools.cached_property
+  def basic_tok(self): return SimpleTokenizer(".*", { b"a": 0, b"b": 1, b"c": 2, b"ab": 3, b"bc": 4 }, { "<x>": 5, "<y>": 6, "<z>": 7 })
+
+  @functools.cached_property
+  def llama_tok(self):
+    # from https://github.com/tinygrad/tinygrad/blob/e0106b6b257ebc003eb3694144e3e198f7d8cc37/examples/llama3.py#L14
+    model_file = fetch("https://huggingface.co/bofenghuang/Meta-Llama-3-8B/resolve/main/original/tokenizer.model")
+    with open(model_file, "rt") as fd:
+      str_vocab = [ line.split(maxsplit=1) for line in fd.read().splitlines() if line ]
+      normal_tokens = { base64.b64decode(stok): int(srank) for stok, srank in str_vocab }
+
+    special_tokens = [
+      "<|begin_of_text|>",
+      "<|end_of_text|>",
+      "<|reserved_special_token_0|>",
+      "<|reserved_special_token_1|>",
+      "<|reserved_special_token_2|>",
+      "<|reserved_special_token_3|>",
+      "<|start_header_id|>",
+      "<|end_header_id|>",
+      "<|reserved_special_token_4|>",
+      "<|eot_id|>",
+    ] + [ f"<|reserved_special_token_{i}|>" for i in range(5, 256 - 5) ]
+    return SimpleTokenizer(get_llama_re(), normal_tokens, { token: len(normal_tokens) + i for i, token in enumerate(special_tokens) })
+
+  def _test_coding(self, tok: SimpleTokenizer, text: str, expected_tokens: list[int]):
+    self.assertEqual(tok.encode(text), expected_tokens)
+    self.assertEqual(tok.decode(expected_tokens), text)
+
+  def test_abc(self): self._test_coding(self.basic_tok, "abc", [ 3, 2 ])
+  def test_abbc(self): self._test_coding(self.basic_tok, "abbc", [ 3, 4 ])
+  def test_aabbbcc(self): self._test_coding(self.basic_tok, "aabbbcc", [ 0, 3, 1, 4, 2 ])
+  def test_specials1(self): self._test_coding(self.basic_tok, "a<x>a<y>a<z>a", [ 0, 5, 0, 6, 0, 7, 0 ])
+  def test_specials2(self): self._test_coding(self.basic_tok, "<x>a<y>a<z>", [ 5, 0, 6, 0, 7 ])
+  def test_invalid_token(self):
+    with self.assertRaises(RuntimeError): self._test_coding(self.basic_tok, "L", [])
+
+  def test_no_specials(self): self._test_coding(SimpleTokenizer(".*", { bytes([i]): i for i in range(256) }, {}), "abc", [97, 98, 99])
+
+  # NOTE: the correct tokenization for this can only be found by looking up the text chunk in the vocab, not by applying merges
+  def test_llama_early_tokenize(self): self._test_coding(self.llama_tok, " например", [ 111797 ])
+
+  def test_llama_basic(self): self._test_coding(self.llama_tok, "hello world", [ 15339, 1917 ])
+  def test_llama_control_char(self): self._test_coding(self.llama_tok, " \x850", [ 220, 116360, 15 ])
+  def test_llama_bytes(self): self._test_coding(self.llama_tok, " \xec\x8b\xa4\xed", [ 1717, 105, 116174, 82638, 2483 ])
+  def test_llama_special1(self): self._test_coding(self.llama_tok, "hello <|end_of_text|>", [ 15339, 220, 128001 ])
+  def test_llama_special2(self): self._test_coding(self.llama_tok, "<|start_header_id|>user<|end_header_id|>\n\n", [ 128006, 882, 128007, 271 ])
+  def test_llama_repeat(self): self._test_coding(self.llama_tok, "00000000000000000", [ 931, 931, 931, 931, 931, 410 ])
+  def test_llama_pat(self): self._test_coding(self.llama_tok, "today\n  \n", [ 31213, 14211 ])
+
+if __name__ == '__main__':
+  unittest.main()
diff --git a/tinygrad/apps/llm.py b/tinygrad/apps/llm.py
index baffe85aeb..4f80d4bf2d 100644
--- a/tinygrad/apps/llm.py
+++ b/tinygrad/apps/llm.py
@@ -1,33 +1,57 @@
 from __future__ import annotations
-import sys, argparse
-from tinygrad import Tensor, nn, UOp, TinyJit, getenv
+import sys, argparse, typing, re, itertools, unicodedata
+from tinygrad import Tensor, nn, UOp, TinyJit, getenv, helpers
+
+def gpt2_decode_vocab(voc: dict[str, int]): # https://github.com/openai/gpt-2/blob/9b63575ef42771a015060c964af2c3da4cf7c8ab/src/encoder.py#L9
+  c2b = { chr(cp): cp for cp in itertools.chain(range(ord("!"), ord("~")+1), range(ord("¡"), ord("¬")+1), range(ord("®"), ord("ÿ")+1)) }
+  c2b.update({ chr(256+off): cp for off, cp in enumerate(cp for cp in range(256) if chr(cp) not in c2b) })
+  return { bytes(c2b[c] for c in tok): tid for tok, tid in voc.items() }
+
+def get_llama_re():
+  def ucat_range(pre: str): return "".join(re.escape(chr(cp)) for cp in range(sys.maxunicode + 1) if unicodedata.category(chr(cp)).startswith(pre))
+  r_ws, r_p_N, r_p_L = r"\t\n\x0b\x0c\r\x85" + ucat_range("Z"), ucat_range("N"), ucat_range("L")
+  # https://github.com/ggml-org/llama.cpp/blob/94933c8c2eeaa9a7983e3f6c08af76bd86724094/src/llama-vocab.cpp#L286
+  return "(?i:'s|'t|'re|'ve|'m|'ll|'d)|" + \
+    f"[^\\r\\n{r_p_N}{r_p_L}]?[{r_p_L}]+|[{r_p_N}]{{1,3}}| ?[^{r_ws}{r_p_N}{r_p_L}]+[\\r\\n]*|[{r_ws}]*[\\r\\n]+|[{r_ws}]+(?![^{r_ws}])|[{r_ws}]+"
 
 class SimpleTokenizer:
-  def __init__(self, vocab: list[str]):
-    self.vocab: list[str] = vocab
-    self.biggest_token: int = max(map(len, vocab))
-    self.token_to_id: dict[str, int] = {tok: i for i, tok in enumerate(vocab)}
-    self.replace_space = "Ġ"
-    self.replace_newline = "Ċ"
+  def __init__(self, pat: str, normal_tokens: dict[bytes, int], special_tokens: dict[str, int]):
+    self._normal_tokens, self._special_tokens, self._pat = normal_tokens, special_tokens, re.compile(pat)
+    self._tok2str = { tid: tok.encode() for tok, tid in special_tokens.items() } | { tid: tok for tok, tid in normal_tokens.items()  }
+    self._special_re = re.compile("|".join(re.escape(tok) for tok in self._special_tokens.keys()) if special_tokens else r"(?!)")
 
-  def encode(self, text:str) -> list[int]:
-    s = text.replace(" ", self.replace_space).replace("\n", self.replace_newline)
-    out: list[int] = []
-    i = 0
-    while i < len(s):
-      j = min(i+self.biggest_token, len(s))
-      while i < j and (tid:=self.token_to_id.get(s[i:j])) is None: j -= 1
-      if tid is None: raise RuntimeError(f"token not found in {s}")
-      assert tid is not None, f"token not found in {s}"
-      out.append(tid)
-      i = j
-    return out
+  @staticmethod
+  def from_gguf_kv(kv: dict):
+    # https://github.com/ggml-org/llama.cpp/blob/94933c8c2eeaa9a7983e3f6c08af76bd86724094/src/llama-vocab.cpp#L1818-L1820
+    if kv["tokenizer.ggml.pre"] not in ("llama3","llama-v3","llama-bpe"): raise ValueError(f"Invalid tokenizer preset '{kv['tokenizer.ggml.pre']}'")
+    vocab: typing.Iterable[tuple[str, int]] = ((tok, idx) for idx, tok in enumerate(kv["tokenizer.ggml.tokens"]))
+    normal_tokens, special_tokens = helpers.partition(vocab, lambda e: kv["tokenizer.ggml.token_type"][e[1]] == 1)
+    return SimpleTokenizer(get_llama_re(), gpt2_decode_vocab(dict(normal_tokens)), dict(special_tokens))
 
-  def decode(self, ids: list[int]) -> str:
-    return ''.join(self.vocab[tid] for tid in ids).replace(self.replace_space, " ").replace(self.replace_newline, "\n")
+  def encode(self, text: str):
+    tokens: list[int] = []
+    pos = 0
+    for match in self._special_re.finditer(text):
+      tokens.extend(self._encode_sentence(text[pos:match.start(0)]) + [self._special_tokens[text[match.start(0):match.end(0)]]])
+      pos = match.end(0)
+    return tokens + self._encode_sentence(text[pos:])
 
-  def role(self, role:str):
-    return [t for x in ["<|start_header_id|>", role, "<|end_header_id|>\n\n"] for t in self.encode(x)]  # llama style
+  def decode(self, ids: list[int]) -> str: return b''.join(self._tok2str[tid] for tid in ids).decode()
+  def role(self, role:str): return self.encode("<|start_header_id|>" + role + "<|end_header_id|>\n\n")
+
+  def _encode_sentence(self, chunk: str): return [ tok for word in self._pat.findall(chunk) for tok in self._encode_word(word.encode()) ]
+  def _encode_word(self, word: bytes):
+    if (early_token:=self._normal_tokens.get(word)) is not None: return [early_token]
+    parts = [word[i:i+1] for i in range(len(word))]
+    while True:
+      min_tid, min_idx = 2**32, -1
+      for idx, (p1, p2) in enumerate(zip(parts[:-1], parts[1:])):
+        tid = self._normal_tokens.get(p1 + p2, min_tid)
+        if tid < min_tid: min_tid, min_idx = tid, idx
+      if min_idx == -1: break
+      parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx+1]] + parts[min_idx+2:]
+    try: return [ self._normal_tokens[p] for p in parts ]
+    except KeyError: raise RuntimeError("token not found")
 
 def apply_rope(x:Tensor, start_pos:int|UOp, base:int=10000):
   B, H, T, Hd = x.shape
@@ -165,7 +189,7 @@ if __name__ == "__main__":
   model, kv = Transformer.from_gguf(Tensor.from_url(models[args.size]), args.max_context)
 
   # extract some metadata
-  tok = SimpleTokenizer(kv["tokenizer.ggml.tokens"])
+  tok = SimpleTokenizer.from_gguf_kv(kv)
   bos_id: int = kv['tokenizer.ggml.bos_token_id']
   eos_id: int = kv['tokenizer.ggml.eos_token_id']