Upload finetuned dclm-german model

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tiktoken.py

@@ -0,0 +1,391 @@
+# Copyright 2024 MosaicML LLM Foundry authors
+# SPDX-License-Identifier: Apache-2.0
+
+from functools import lru_cache
+from typing import Any, Dict, List, Optional, Tuple
+
+from transformers import PreTrainedTokenizer
+
+__all__ = [
+    'TiktokenTokenizerWrapper',
+]
+
+DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible."""
+
+
+# Taken from
+# https://github.com/huggingface/transformers/blob/8aca43bdb3cb9a5020f6d57589d85679dc873b1c/src/transformers/models/gpt2/tokenization_gpt2.py#L62-L84
+@lru_cache()
+def bytes_to_unicode():
+    """Returns list of utf-8 byte and a mapping to unicode strings.
+
+    We specifically avoids mapping to whitespace/control characters the bpe code
+    barfs on.
+
+    The reversible bpe codes work on unicode strings. This means you need a
+    large # of unicode characters in your vocab if you want to avoid UNKs. When
+    you're at something like a 10B token dataset you end up needing around 5K
+    for decent coverage. This is a significant percentage of your normal, say,
+    32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and
+    unicode strings.
+    """
+    bs = (
+        list(range(ord('!'),
+                   ord('~') + 1)) + list(range(ord('¡'),
+                                               ord('¬') + 1)) +
+        list(range(ord('®'),
+                   ord('ÿ') + 1))
+    )
+    cs = bs[:]
+    n = 0
+    for b in range(2**8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2**8 + n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+class TiktokenTokenizerWrapper(PreTrainedTokenizer):
+    """A thin wrapper around tiktoken to make it compatible with Hugging Face.
+
+    tokenizers.
+
+    See HuggingFace for further documentation on general tokenizer methods.
+    """
+
+    model_input_names = ['input_ids', 'attention_mask']
+
+    def __init__(
+        self,
+        model_name: Optional[str] = None,
+        encoding_name: Optional[str] = None,
+        add_bos_token: bool = False,
+        add_eos_token: bool = False,
+        use_default_system_prompt: bool = False,
+        unk_token: Optional[str] = '<|endoftext|>',
+        eos_token: Optional[str] = '<|endoftext|>',
+        bos_token: Optional[str] = '<|endoftext|>',
+        pad_token: Optional[str] = None,
+        errors: str = 'replace',
+        **kwargs: Any,
+    ):
+        """Constructor creates a tiktoken tokenizer to use as the underlying.
+
+        tokenizer.
+
+        Args:
+            model_name (Optional[str], optional): The name of the model to load from tiktoken. Defaults to None.
+                Either model_name or encoding_name must be set, but not both.
+            encoding_name (Optional[str], optional): The name of the encoding to load from tiktoken. Defaults to None.
+                Either model_name or encoding_name must be set, but not both.
+            add_bos_token (bool, optional): Whether to add bos tokens. Defaults to False.
+            add_eos_token (bool, optional): Whether to add eos tokens. Defaults to False.
+            use_default_system_prompt (bool, optional): Use the default system prompt or not. Defaults to False.
+            unk_token (Optional[str], optional): The unk token. Defaults to '<|endoftext|>'.
+            eos_token (Optional[str], optional): The eos token. Defaults to '<|endoftext|>'.
+            bos_token (Optional[str], optional): The bos token. Defaults to '<|endoftext|>'.
+            pad_token (Optional[str], optional): The pad token. Defaults to None.
+            errors (str, optional): Paradigm to follow when decoding bytes to UTF-8. See
+                [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
+                Defaults to `"replace"`.
+        """
+        try:
+            import tiktoken
+        except:
+            raise ImportError(
+                'You need to install tiktoken to use TiktokenTokenizerWrapper.',
+            )
+
+        # Workaround to make tiktokenizer picklable.
+        # https://github.com/huggingface/datasets/issues/5536#issuecomment-1682309347
+        # There is an open PR from HF to add this to tiktoken: https://github.com/openai/tiktoken/pull/181
+        import copyreg
+        import functools
+
+        from tiktoken import Encoding  # type: ignore (thirdParty)
+
+        def pickle_Encoding(enc: Encoding):
+            return (
+                functools.partial(
+                    Encoding,
+                    enc.name,
+                    pat_str=enc._pat_str,
+                    mergeable_ranks=enc._mergeable_ranks,
+                    special_tokens=enc._special_tokens,
+                ),
+                (),
+            )
+
+        copyreg.pickle(Encoding, pickle_Encoding)
+
+        if model_name is not None and encoding_name is not None:
+            raise ValueError(
+                'You need to specify either model_name or encoding_name, not both.',
+            )
+
+        self.model_name = model_name
+        self.encoding_name = encoding_name
+
+        if self.model_name is not None:
+            self.encoding = tiktoken.encoding_for_model(  # type: ignore (thirdParty)
+                self.model_name)
+        elif self.encoding_name is not None:
+            self.encoding = tiktoken.get_encoding(  # type: ignore (thirdParty)
+                self.encoding_name)
+        else:
+            raise ValueError(
+                'You need to specify either model_name or encoding_name.',
+            )
+
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.use_default_system_prompt = use_default_system_prompt
+
+        self.byte_encoder = bytes_to_unicode()
+        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+        self.errors = errors
+
+        self.decoder: Dict[int, str] = {}
+        for i in range(self.encoding.n_vocab):
+            try:
+                self.encoding.decode_single_token_bytes(i)
+            except KeyError:
+                continue
+            # Taken from
+            # https://gist.github.com/xenova/a452a6474428de0182b17605a98631ee
+            decoding = ''.join([
+                bytes_to_unicode()[ord(char)] for char in
+                self.encoding.decode_single_token_bytes(i).decode('latin-1')
+            ])
+            self.decoder[i] = decoding
+
+        self.encoder: Dict[str, int] = {}
+        for i in range(self.encoding.n_vocab):
+            if i in self.decoder:
+                self.encoder[self.decoder[i]] = i
+
+        super().__init__(
+            model_name=model_name,
+            encoding_name=encoding_name,
+            add_bos_token=add_bos_token,
+            add_eos_token=add_eos_token,
+            use_default_system_prompt=use_default_system_prompt,
+            unk_token=unk_token,
+            eos_token=eos_token,
+            bos_token=bos_token,
+            pad_token=pad_token,
+            errors=errors,
+            **kwargs,
+        )
+
+    @property
+    def vocab_size(self) -> int:
+        """Returns vocab size."""
+        return self.encoding.n_vocab
+
+    @property
+    def is_fast(self) -> bool:
+        return False
+
+    @property
+    def default_chat_template(self):
+        """Chat ML Template for User/Assistant.
+
+        Pinning default Chat ML template in case defaults change.
+        """
+        template = (
+            "{% if messages[0]['role'] == 'system' %}"
+            '{% set loop_messages = messages[1:] %}'
+            "{% set system_message = messages[0]['content'] %}"
+            "{% elif USE_DEFAULT_PROMPT == true and not 'system' in messages[0]['role'] %}"
+            '{% set loop_messages = messages %}'
+            "{% set system_message = 'DEFAULT_SYSTEM_PROMPT' %}"
+            '{% else %}'
+            '{% set loop_messages = messages %}'
+            '{% set system_message = false %}'
+            '{% endif %}'
+            '{% for message in loop_messages %}'
+            '{% if loop.index0 == 0 %}'
+            '{% if system_message != false %}'
+            "{{ '<|im_start|>system\n' + system_message.strip() + '<|im_end|>\n'}}"
+            '{% endif %}'
+            "{{ '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' }}"
+            '{% else %}'
+            "{{ '\n' + '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' }}"
+            '{% endif %}'
+            '{% if (add_generation_prompt == true and loop.last) %}'
+            "{{ '\n' + '<|im_start|>' + 'assistant' + '\n' }}"
+            '{% endif %}'
+            '{% endfor %}'
+        )
+        template = template.replace(
+            'USE_DEFAULT_PROMPT',
+            'true' if self.use_default_system_prompt else 'false',
+        )
+        template = template.replace(
+            'DEFAULT_SYSTEM_PROMPT',
+            DEFAULT_SYSTEM_PROMPT,
+        )
+        return template
+
+    def get_vocab(self) -> Dict[str, int]:
+        """Returns vocab as a dict."""
+        # As far as I can tell, we don't require get_vocab to completely work,
+        # but when using additional_special_tokens, Hugging Face determines the next
+        # token index to add with len(self.get_vocab()) so we need the _size_ of this dictionary to be correct.
+        vocab_clone = self.encoder.copy()
+        extra_id_index = 0
+        candidate_extra_id = f'<extra_id_{extra_id_index}>'
+        indices_to_fill_in = (
+            set(range(self.vocab_size)) - set(vocab_clone.values())
+        )
+
+        # Add enough indices to make get_vocab() the right length
+        for index_to_add in indices_to_fill_in:
+            # Make sure we don't overwrite a token that already exists
+            while candidate_extra_id in vocab_clone:
+                extra_id_index += 1
+                candidate_extra_id = f'<extra_id_{extra_id_index}>'
+
+            # Get an index to add and add the item
+            vocab_clone[candidate_extra_id] = index_to_add
+
+        return dict(vocab_clone, **self.added_tokens_encoder)
+
+    def _tokenize(self, text: str) -> List[str]:
+        """Returns a tokenized string."""
+        if not isinstance(text, str):
+            raise ValueError(
+                f'Expected a string input to _tokenize but got {type(text)}.',
+            )
+
+        tokens = [
+            self.decoder[t]
+            for t in self.encoding.encode(text, allowed_special='all')
+        ]
+
+        return tokens
+
+    def _convert_token_to_id(self, token: str) -> Optional[int]:
+        """Converts a token (str) in an id using the vocab."""
+        return self.encoder.get(token, self.encoder.get(self.unk_token))
+
+    def _convert_id_to_token(self, index: int) -> Optional[str]:
+        """Converts an index (integer) in a token (str) using the vocab."""
+        # For tokens in either the gap in ids in the tokenizer, or beyond the range of the tokenizer,
+        # we return empty string. This matches the behavior of Hugging Face fast tokenizers,
+        # but not slow tokenizers.
+        return self.decoder.get(index, '')
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        """Converts a sequence of tokens (string) in a single string."""
+        text = ''.join(tokens)
+        text = bytearray([self.byte_decoder[c] for c in text
+                         ],).decode('utf-8', errors=self.errors)
+        return text
+
+    def build_inputs_with_special_tokens(
+        self,
+        token_ids_0: List[int],
+        token_ids_1: Optional[List[int]] = None,
+    ) -> List[int]:
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+        output = bos_token_id + token_ids_0 + eos_token_id
+
+        if token_ids_1 is not None:
+            output = output + bos_token_id + token_ids_1 + eos_token_id
+
+        return output
+
+    def get_special_tokens_mask(
+        self,
+        token_ids_0: List[int],
+        token_ids_1: Optional[List[int]] = None,
+        already_has_special_tokens: bool = False,
+    ) -> List[int]:
+        """Retrieves sequence ids from a token list that has no special tokens.
+
+        Function copied from
+        https://github.com/huggingface/transformers/blob/e3a4bd2bee212a2d0fd9f03b27fe7bfc1debe42d/src/transformers/models/gpt2/tokenization_gpt2.py#L265-L295
+
+        added. This method is called when adding special tokens using the
+        tokenizer `prepare_for_model` or `encode_plus` methods.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0,
+                token_ids_1=token_ids_1,
+                already_has_special_tokens=True,
+            )
+
+        bos_token_id = [1] if self.add_bos_token else []
+        eos_token_id = [1] if self.add_eos_token else []
+
+        if token_ids_1 is None:
+            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
+        return (
+            bos_token_id + ([0] * len(token_ids_0)) + eos_token_id +
+            bos_token_id + ([0] * len(token_ids_1)) + eos_token_id
+        )
+
+    def create_token_type_ids_from_sequences(
+        self,
+        token_ids_0: List[int],
+        token_ids_1: Optional[List[int]] = None,
+    ) -> List[int]:
+        sep = [self.sep_token_id]
+
+        if token_ids_1 is None:
+            return len(token_ids_0 + sep) * [0]
+        return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
+
+    def save_vocabulary(
+        self,
+        save_directory: str,
+        filename_prefix: Optional[str] = None,
+    ) -> Tuple[str]:
+
+        # ignore the below type to keep the original signature
+        # we are knowingly breaking the signature here, although not 100% certain
+        # it doesn't have side effects
+        # There is some code in huggingface that calls this function to get the vocab files,
+        # but it doesn't seem to access them (or at least checks for their existence
+        # before accessing them)
+        return (None, None)  # type: ignore
+
+    def sanitize_special_tokens(self) -> int:
+        """Make sure that all the special tokens attributes of the tokenizer.
+
+        (`tokenizer.mask_token`, `tokenizer.cls_token`, etc.) are in the
+        vocabulary.
+
+        Add the missing ones to the vocabulary if needed.
+
+        Return:
+            `int`: The number of tokens added in the vocabulary during the operation.
+        """
+        actual_new_tokens = []
+        for token in self.all_special_tokens_extended:
+            encoded = self.encoding.encode(token, allowed_special='all')
+            if len(encoded) > 1:
+                actual_new_tokens.append(token)
+
+        return self.add_tokens(actual_new_tokens, special_tokens=True)
+
+
+TiktokenTokenizerWrapper.register_for_auto_class()

tokenizer_config.json

@@ -0,0 +1,34 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "100257": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoTokenizer": [
+      "tiktoken.TiktokenTokenizerWrapper",
+      null
+    ]
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "encoding_name": null,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "model_name": "gpt-4",
+  "pad_token": "<|endoftext|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "TiktokenTokenizerWrapper",
+  "unk_token": "<|endoftext|>",
+  "use_default_system_prompt": false
+}