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GembyTiny Tokenizer — HuggingFace PreTrainedTokenizer compatible.
Works with pipeline(), Trainer, AutoTokenizer, push_to_hub().
For production: swap the character-level encoding with a SentencePiece model.
"""
import os
import json
from typing import Dict, List, Optional, Tuple
from transformers import PreTrainedTokenizer
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json"}
SPECIAL_TOKENS = {
"<|pad|>": 0,
"<|bos|>": 1,
"<|eos|>": 2,
"<|sep|>": 3,
"<|user|>": 4,
"<|assistant|>": 5,
"<|system|>": 6,
"<|unk|>": 7,
}
# Default chat template (Jinja2 format — used by apply_chat_template)
CHAT_TEMPLATE = (
"{% if messages[0]['role'] == 'system' %}"
"<|bos|><|system|>{{ messages[0]['content'] }}<|sep|>"
"{% set messages = messages[1:] %}"
"{% else %}"
"<|bos|><|system|>You are GembyTiny, a helpful AI assistant.<|sep|>"
"{% endif %}"
"{% for message in messages %}"
"{% if message['role'] == 'user' %}"
"<|user|>{{ message['content'] }}<|sep|>"
"{% elif message['role'] == 'assistant' %}"
"<|assistant|>{{ message['content'] }}<|sep|>"
"{% endif %}"
"{% endfor %}"
"{% if add_generation_prompt %}<|assistant|>{% endif %}"
)
class GembyTinyTokenizer(PreTrainedTokenizer):
"""
HuggingFace-compatible tokenizer for GembyTiny.
Special tokens:
<|pad|> pad token
<|bos|> beginning of sequence
<|eos|> end of sequence
<|sep|> turn separator
<|user|> user turn
<|assistant|> assistant turn
<|system|> system prompt
<|unk|> unknown token
"""
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file: Optional[str] = None,
pad_token: str = "<|pad|>",
bos_token: str = "<|bos|>",
eos_token: str = "<|eos|>",
unk_token: str = "<|unk|>",
sep_token: str = "<|sep|>",
**kwargs,
):
self.vocab_file = vocab_file
self._vocab: Dict[str, int] = {}
self._ids_to_tokens: Dict[int, str] = {}
if vocab_file and os.path.exists(vocab_file):
with open(vocab_file) as f:
self._vocab = json.load(f)
else:
self._build_char_vocab()
self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
kwargs.setdefault("chat_template", CHAT_TEMPLATE)
super().__init__(
pad_token=pad_token,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
**kwargs,
)
def _build_char_vocab(self):
"""Build a character-level vocabulary (bootstrap / dev)."""
self._vocab = dict(SPECIAL_TOKENS)
chars = (
list(" abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ")
+ list("0123456789")
+ list(".,!?;:'\"-()[]{}@#$%^&*+=/<>\\|`~\n\t")
)
offset = max(SPECIAL_TOKENS.values()) + 1
for i, ch in enumerate(chars):
self._vocab[ch] = offset + i
self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
@property
def vocab_size(self) -> int:
return 32000 # fixed vocab size matching model config
def get_vocab(self) -> Dict[str, int]:
return dict(self._vocab)
def _tokenize(self, text: str) -> List[str]:
"""Character-level tokenization. Replace with BPE/SPM in production."""
return list(text)
def _convert_token_to_id(self, token: str) -> int:
return self._vocab.get(token, self._vocab.get("<|unk|>", 7))
def _convert_id_to_token(self, index: int) -> str:
return self._ids_to_tokens.get(index, "<|unk|>")
def convert_tokens_to_string(self, tokens: List[str]) -> str:
return "".join(tokens)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
os.makedirs(save_directory, exist_ok=True)
vocab_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"],
)
with open(vocab_file, "w", encoding="utf-8") as f:
json.dump(self._vocab, f, ensure_ascii=False, indent=2)
return (vocab_file,)
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