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Model: thu-coai/ShieldLM-14B-qwen
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2026-07-04 12:42:39 +08:00
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---
license: mit
language:
- en
- zh
---
## Introduction
The ShieldLM model ([paper link](https://arxiv.org/abs/2402.16444)) initialized from [Qwen-14B-Chat](https://huggingface.co/Qwen/Qwen-14B-Chat). ShieldLM is a bilingual (Chinese and English) safety detector that mainly aims to help to detect safety issues in LLMs' generations. It aligns with general human safety standards, supports fine-grained customizable detection rules, and provides explanations for its decisions.
Refer to our [github repository](https://github.com/thu-coai/ShieldLM) for more detailed information.
## Usage
Please refer to our [github repository](https://github.com/thu-coai/ShieldLM) for the detailed usage instructions.
## Performance
ShieldLM demonstrates impressive detection performance across 4 ID and OOD test sets, compared to strong baselines such as GPT-4, Llama Guard and Perspective API.
Refer to [our paper](https://arxiv.org/abs/2402.16444) for more detailed evaluation results.

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{
"_name_or_path": "./ShieldLM-14B-qwen",
"architectures": [
"QWenLMHeadModel"
],
"attn_dropout_prob": 0.0,
"auto_map": {
"AutoConfig": "configuration_qwen.QWenConfig",
"AutoModelForCausalLM": "modeling_qwen.QWenLMHeadModel"
},
"bf16": true,
"emb_dropout_prob": 0.0,
"fp16": false,
"fp32": false,
"hidden_size": 5120,
"initializer_range": 0.02,
"intermediate_size": 27392,
"kv_channels": 128,
"layer_norm_epsilon": 1e-06,
"max_position_embeddings": 8192,
"model_type": "qwen",
"no_bias": true,
"num_attention_heads": 40,
"num_hidden_layers": 40,
"onnx_safe": null,
"rotary_emb_base": 10000,
"rotary_pct": 1.0,
"scale_attn_weights": true,
"seq_length": 2048,
"tie_word_embeddings": false,
"tokenizer_class": "QWenTokenizer",
"torch_dtype": "bfloat16",
"transformers_version": "4.36.2",
"use_cache": true,
"use_cache_kernel": false,
"use_cache_quantization": false,
"use_dynamic_ntk": true,
"use_flash_attn": true,
"use_logn_attn": true,
"vocab_size": 152064
}

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# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from transformers import PretrainedConfig
class QWenConfig(PretrainedConfig):
model_type = "qwen"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=151936,
hidden_size=4096,
num_hidden_layers=32,
num_attention_heads=32,
emb_dropout_prob=0.0,
attn_dropout_prob=0.0,
layer_norm_epsilon=1e-6,
initializer_range=0.02,
max_position_embeddings=8192,
scale_attn_weights=True,
use_cache=True,
bf16=False,
fp16=False,
fp32=False,
kv_channels=128,
rotary_pct=1.0,
rotary_emb_base=10000,
use_dynamic_ntk=True,
use_logn_attn=True,
use_flash_attn="auto",
intermediate_size=22016,
no_bias=True,
tie_word_embeddings=False,
use_cache_quantization=False,
use_cache_kernel=False,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.emb_dropout_prob = emb_dropout_prob
self.attn_dropout_prob = attn_dropout_prob
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.scale_attn_weights = scale_attn_weights
self.use_cache = use_cache
self.max_position_embeddings = max_position_embeddings
self.bf16 = bf16
self.fp16 = fp16
self.fp32 = fp32
self.kv_channels = kv_channels
self.rotary_pct = rotary_pct
self.rotary_emb_base = rotary_emb_base
self.use_dynamic_ntk = use_dynamic_ntk
self.use_logn_attn = use_logn_attn
self.use_flash_attn = use_flash_attn
self.no_bias = no_bias
self.use_cache_quantization=use_cache_quantization
self.use_cache_kernel=use_cache_kernel
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs
)

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from torch.utils import cpp_extension
import pathlib
import os
import subprocess
def _get_cuda_bare_metal_version(cuda_dir):
raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"],
universal_newlines=True)
output = raw_output.split()
release_idx = output.index("release") + 1
release = output[release_idx].split(".")
bare_metal_major = release[0]
bare_metal_minor = release[1][0]
return raw_output, bare_metal_major, bare_metal_minor
def _create_build_dir(buildpath):
try:
os.mkdir(buildpath)
except OSError:
if not os.path.isdir(buildpath):
print(f"Creation of the build directory {buildpath} failed")
# Check if cuda 11 is installed for compute capability 8.0
cc_flag = []
_, bare_metal_major, bare_metal_minor = _get_cuda_bare_metal_version(cpp_extension.CUDA_HOME)
if int(bare_metal_major) >= 11:
cc_flag.append('-gencode')
cc_flag.append('arch=compute_80,code=sm_80')
if int(bare_metal_minor) >= 7:
cc_flag.append('-gencode')
cc_flag.append('arch=compute_90,code=sm_90')
# Build path
srcpath = pathlib.Path(__file__).parent.absolute()
buildpath = srcpath / 'build'
_create_build_dir(buildpath)
def _cpp_extention_load_helper(name, sources, extra_cuda_flags):
return cpp_extension.load(
name=name,
sources=sources,
build_directory=buildpath,
extra_cflags=['-O3', ],
extra_cuda_cflags=['-O3',
'-gencode', 'arch=compute_70,code=sm_70',
'--use_fast_math'] + extra_cuda_flags + cc_flag,
verbose=1
)
extra_flags = []
cache_autogptq_cuda_256_sources = ["./cache_autogptq_cuda_256.cpp",
"./cache_autogptq_cuda_kernel_256.cu"]
cache_autogptq_cuda_256 = _cpp_extention_load_helper("cache_autogptq_cuda_256", cache_autogptq_cuda_256_sources, extra_flags)

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{
"chat_format": "chatml",
"do_sample": true,
"eos_token_id": 151643,
"max_new_tokens": 512,
"max_window_size": 6144,
"pad_token_id": 151643,
"top_k": 0,
"top_p": 0.5,
"transformers_version": "4.36.2"
}

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}

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# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Generation support."""
from typing import Tuple, List, Union, Iterable
import numpy as np
import torch
import torch.nn.functional as F
from transformers import PreTrainedTokenizer
from transformers import logging
from transformers.generation import LogitsProcessor
logger = logging.get_logger(__name__)
# Types.
HistoryType = List[Tuple[str, str]]
TokensType = List[int]
BatchTokensType = List[List[int]]
def pad_batch(batch: BatchTokensType, pad_id: int, seq_length: int) -> BatchTokensType:
for tokens in batch:
context_length = len(tokens)
if context_length < seq_length:
tokens.extend([pad_id] * (seq_length - context_length))
return batch
def get_ltor_masks_and_position_ids(
data,
eod_token,
reset_position_ids,
reset_attention_mask,
eod_mask_loss,
):
"""Build masks and position id for left to right model."""
# Extract batch size and sequence length.
micro_batch_size, seq_length = data.size()
# Attention mask (lower triangular).
if reset_attention_mask:
att_mask_batch = micro_batch_size
else:
att_mask_batch = 1
attention_mask = torch.tril(
torch.ones((att_mask_batch, seq_length, seq_length), device=data.device)
).view(att_mask_batch, 1, seq_length, seq_length)
# Loss mask.
loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
if eod_mask_loss:
loss_mask[data == eod_token] = 0.0
# Position ids.
position_ids = torch.arange(seq_length, dtype=torch.long, device=data.device)
position_ids = position_ids.unsqueeze(0).expand_as(data)
# We need to clone as the ids will be modifed based on batch index.
if reset_position_ids:
position_ids = position_ids.clone()
if reset_position_ids or reset_attention_mask:
# Loop through the batches:
for b in range(micro_batch_size):
# Find indecies where EOD token is.
eod_index = position_ids[b, data[b] == eod_token]
# Detach indecies from positions if going to modify positions.
if reset_position_ids:
eod_index = eod_index.clone()
# Loop through EOD indecies:
prev_index = 0
for j in range(eod_index.size()[0]):
i = eod_index[j]
# Mask attention loss.
if reset_attention_mask:
attention_mask[b, 0, (i + 1) :, : (i + 1)] = 0
# Reset positions.
if reset_position_ids:
position_ids[b, (i + 1) :] -= i + 1 - prev_index
prev_index = i + 1
# Convert attention mask to binary:
attention_mask = attention_mask < 0.5
return attention_mask, loss_mask, position_ids
def get_batch(context_tokens: torch.LongTensor, eod_id: int):
"""Generate batch from context tokens."""
# Move to GPU.
tokens = context_tokens.contiguous().to(context_tokens.device)
# Get the attention mask and postition ids.
attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
tokens,
eod_id,
reset_position_ids=False,
reset_attention_mask=False,
eod_mask_loss=False,
)
return tokens, attention_mask, position_ids
def get_stop_words_ids(chat_format, tokenizer):
if chat_format == "raw":
stop_words_ids = [tokenizer.encode("Human:"), [tokenizer.eod_id]]
elif chat_format == "chatml":
stop_words_ids = [[tokenizer.im_end_id], [tokenizer.im_start_id]]
else:
raise NotImplementedError(f"Unknown chat format {chat_format!r}")
return stop_words_ids
def make_context(
tokenizer: PreTrainedTokenizer,
query: str,
history: List[Tuple[str, str]] = None,
system: str = "",
max_window_size: int = 6144,
chat_format: str = "chatml",
):
if history is None:
history = []
if chat_format == "chatml":
im_start, im_end = "<|im_start|>", "<|im_end|>"
im_start_tokens = [tokenizer.im_start_id]
im_end_tokens = [tokenizer.im_end_id]
nl_tokens = tokenizer.encode("\n")
def _tokenize_str(role, content):
return f"{role}\n{content}", tokenizer.encode(
role, allowed_special=set()
) + nl_tokens + tokenizer.encode(content, allowed_special=set())
system_text, system_tokens_part = _tokenize_str("system", system)
system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
raw_text = ""
context_tokens = []
for turn_query, turn_response in reversed(history):
query_text, query_tokens_part = _tokenize_str("user", turn_query)
query_tokens = im_start_tokens + query_tokens_part + im_end_tokens
response_text, response_tokens_part = _tokenize_str(
"assistant", turn_response
)
response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
prev_chat = (
f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
)
current_context_size = (
len(system_tokens) + len(next_context_tokens) + len(context_tokens)
)
if current_context_size < max_window_size:
context_tokens = next_context_tokens + context_tokens
raw_text = prev_chat + raw_text
else:
break
context_tokens = system_tokens + context_tokens
raw_text = f"{im_start}{system_text}{im_end}" + raw_text
context_tokens += (
nl_tokens
+ im_start_tokens
+ _tokenize_str("user", query)[1]
+ im_end_tokens
+ nl_tokens
+ im_start_tokens
+ tokenizer.encode("assistant")
+ nl_tokens
)
raw_text += f"\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n"
elif chat_format == "raw":
raw_text = query
context_tokens = tokenizer.encode(raw_text)
else:
raise NotImplementedError(f"Unknown chat format {chat_format!r}")
return raw_text, context_tokens
def _decode_default(
tokens: List[int],
*,
stop_words: List[str],
eod_words: List[str],
tokenizer: PreTrainedTokenizer,
raw_text_len: int,
verbose: bool = False,
return_end_reason: bool = False,
errors: str='replace',
):
trim_decode_tokens = tokenizer.decode(tokens, errors=errors)[raw_text_len:]
if verbose:
print("\nRaw Generate: ", trim_decode_tokens)
end_reason = f"Gen length {len(tokens)}"
for stop_word in stop_words:
trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
for eod_word in eod_words:
if eod_word in trim_decode_tokens:
end_reason = f"Gen {eod_word!r}"
trim_decode_tokens = trim_decode_tokens.split(eod_word)[0]
trim_decode_tokens = trim_decode_tokens.strip()
if verbose:
print("\nEnd Reason:", end_reason)
print("\nGenerate: ", trim_decode_tokens)
if return_end_reason:
return trim_decode_tokens, end_reason
else:
return trim_decode_tokens
def _decode_chatml(
tokens: List[int],
*,
stop_words: List[str],
eod_token_ids: List[int],
tokenizer: PreTrainedTokenizer,
raw_text_len: int,
context_length: int,
verbose: bool = False,
return_end_reason: bool = False,
errors: str='replace'
):
end_reason = f"Gen length {len(tokens)}"
eod_token_idx = context_length
for eod_token_idx in range(context_length, len(tokens)):
if tokens[eod_token_idx] in eod_token_ids:
end_reason = f"Gen {tokenizer.decode([tokens[eod_token_idx]])!r}"
break
trim_decode_tokens = tokenizer.decode(tokens[:eod_token_idx], errors=errors)[raw_text_len:]
if verbose:
print("\nRaw Generate w/o EOD:", tokenizer.decode(tokens, errors=errors)[raw_text_len:])
print("\nRaw Generate:", trim_decode_tokens)
print("\nEnd Reason:", end_reason)
for stop_word in stop_words:
trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
trim_decode_tokens = trim_decode_tokens.strip()
if verbose:
print("\nGenerate:", trim_decode_tokens)
if return_end_reason:
return trim_decode_tokens, end_reason
else:
return trim_decode_tokens
def decode_tokens(
tokens: Union[torch.LongTensor, TokensType],
tokenizer: PreTrainedTokenizer,
raw_text_len: int,
context_length: int,
chat_format: str,
verbose: bool = False,
return_end_reason: bool = False,
errors: str="replace",
) -> str:
if torch.is_tensor(tokens):
tokens = tokens.cpu().numpy().tolist()
if chat_format == "chatml":
return _decode_chatml(
tokens,
stop_words=[],
eod_token_ids=[tokenizer.im_start_id, tokenizer.im_end_id],
tokenizer=tokenizer,
raw_text_len=raw_text_len,
context_length=context_length,
verbose=verbose,
return_end_reason=return_end_reason,
errors=errors,
)
elif chat_format == "raw":
return _decode_default(
tokens,
stop_words=["<|endoftext|>"],
eod_words=["<|endoftext|>"],
tokenizer=tokenizer,
raw_text_len=raw_text_len,
verbose=verbose,
return_end_reason=return_end_reason,
errors=errors,
)
else:
raise NotImplementedError(f"Unknown chat format {chat_format!r}")
class StopWordsLogitsProcessor(LogitsProcessor):
"""
:class:`transformers.LogitsProcessor` that enforces that when specified sequences appear, stop geration.
Args:
stop_words_ids (:obj:`List[List[int]]`):
List of list of token ids of stop ids. In order to get the tokens of the words
that should not appear in the generated text, use :obj:`tokenizer(bad_word,
add_prefix_space=True).input_ids`.
eos_token_id (:obj:`int`):
The id of the `end-of-sequence` token.
"""
def __init__(self, stop_words_ids: Iterable[Iterable[int]], eos_token_id: int):
if not isinstance(stop_words_ids, List) or len(stop_words_ids) == 0:
raise ValueError(
f"`stop_words_ids` has to be a non-emtpy list, but is {stop_words_ids}."
)
if any(not isinstance(bad_word_ids, list) for bad_word_ids in stop_words_ids):
raise ValueError(
f"`stop_words_ids` has to be a list of lists, but is {stop_words_ids}."
)
if any(
any(
(not isinstance(token_id, (int, np.integer)) or token_id < 0)
for token_id in stop_word_ids
)
for stop_word_ids in stop_words_ids
):
raise ValueError(
f"Each list in `stop_words_ids` has to be a list of positive integers, but is {stop_words_ids}."
)
self.stop_words_ids = list(
filter(
lambda bad_token_seq: bad_token_seq != [eos_token_id], stop_words_ids
)
)
self.eos_token_id = eos_token_id
for stop_token_seq in self.stop_words_ids:
assert (
len(stop_token_seq) > 0
), "Stop words token sequences {} cannot have an empty list".format(
stop_words_ids
)
def __call__(
self, input_ids: torch.LongTensor, scores: torch.FloatTensor
) -> torch.FloatTensor:
stopped_samples = self._calc_stopped_samples(input_ids)
for i, should_stop in enumerate(stopped_samples):
if should_stop:
scores[i, self.eos_token_id] = float(2**15)
return scores
def _tokens_match(self, prev_tokens: torch.LongTensor, tokens: List[int]) -> bool:
if len(tokens) == 0:
# if bad word tokens is just one token always ban it
return True
elif len(tokens) > len(prev_tokens):
# if bad word tokens are longer then prev input_ids they can't be equal
return False
elif prev_tokens[-len(tokens) :].tolist() == tokens:
# if tokens match
return True
else:
return False
def _calc_stopped_samples(self, prev_input_ids: Iterable[int]) -> Iterable[int]:
stopped_samples = []
for prev_input_ids_slice in prev_input_ids:
match = False
for stop_token_seq in self.stop_words_ids:
if self._tokens_match(prev_input_ids_slice, stop_token_seq):
# if tokens do not match continue
match = True
break
stopped_samples.append(match)
return stopped_samples
def top_k_logits(logits, top_k=0, top_p=0.0, filter_value=-float("Inf")):
"""This function has been mostly taken from huggingface conversational
ai code at
https://medium.com/huggingface/how-to-build-a-state-of-the-art-
conversational-ai-with-transfer-learning-2d818ac26313"""
if top_k > 0:
# Remove all tokens with a probability less than the
# last token of the top-k
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = filter_value
if top_p > 0.0:
# Cconvert to 1D
sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probs > top_p
# Shift the indices to the right to keep also the first token
# above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
for i in range(sorted_indices.size(0)):
indices_to_remove = sorted_indices[i][sorted_indices_to_remove[i]]
logits[i][indices_to_remove] = filter_value
return logits
def switch(val1, val2, boolean):
boolean = boolean.type_as(val1)
return (1 - boolean) * val1 + boolean * val2

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tokenization_qwen.py Normal file
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# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Tokenization classes for QWen."""
import base64
import logging
import os
import unicodedata
from typing import Collection, Dict, List, Set, Tuple, Union
import tiktoken
from transformers import PreTrainedTokenizer, AddedToken
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
ENDOFTEXT = "<|endoftext|>"
IMSTART = "<|im_start|>"
IMEND = "<|im_end|>"
# as the default behavior is changed to allow special tokens in
# regular texts, the surface forms of special tokens need to be
# as different as possible to minimize the impact
EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
SPECIAL_TOKENS = (
ENDOFTEXT,
IMSTART,
IMEND,
) + EXTRAS
def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
with open(tiktoken_bpe_file, "rb") as f:
contents = f.read()
return {
base64.b64decode(token): int(rank)
for token, rank in (line.split() for line in contents.splitlines() if line)
}
class QWenTokenizer(PreTrainedTokenizer):
"""QWen tokenizer."""
vocab_files_names = VOCAB_FILES_NAMES
def __init__(
self,
vocab_file,
errors="replace",
**kwargs,
):
super().__init__(**kwargs)
self.errors = errors # how to handle errors in decoding
self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: dict[bytes, int]
self.special_tokens = {
token: index
for index, token in enumerate(
SPECIAL_TOKENS, start=len(self.mergeable_ranks)
)
}
enc = tiktoken.Encoding(
"Qwen",
pat_str=PAT_STR,
mergeable_ranks=self.mergeable_ranks,
special_tokens=self.special_tokens,
)
assert (
len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
self.decoder = {
v: k for k, v in self.mergeable_ranks.items()
} # type: dict[int, bytes|str]
self.decoder.update({v: k for k, v in self.special_tokens.items()})
self.tokenizer = enc # type: tiktoken.Encoding
self.eod_id = self.tokenizer.eot_token
self.im_start_id = self.special_tokens[IMSTART]
self.im_end_id = self.special_tokens[IMEND]
def __getstate__(self):
# for pickle lovers
state = self.__dict__.copy()
del state['tokenizer']
return state
def __setstate__(self, state):
# tokenizer is not python native; don't pass it; rebuild it
self.__dict__.update(state)
enc = tiktoken.Encoding(
"Qwen",
pat_str=PAT_STR,
mergeable_ranks=self.mergeable_ranks,
special_tokens=self.special_tokens,
)
self.tokenizer = enc
def __len__(self) -> int:
return self.tokenizer.n_vocab
def get_vocab(self) -> Dict[bytes, int]:
return self.mergeable_ranks
def convert_tokens_to_ids(
self, tokens: Union[bytes, str, List[Union[bytes, str]]]
) -> List[int]:
ids = []
if isinstance(tokens, (str, bytes)):
if tokens in self.special_tokens:
return self.special_tokens[tokens]
else:
return self.mergeable_ranks.get(tokens)
for token in tokens:
if token in self.special_tokens:
ids.append(self.special_tokens[token])
else:
ids.append(self.mergeable_ranks.get(token))
return ids
def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
if not special_tokens and new_tokens:
raise ValueError('Adding regular tokens is not supported')
for token in new_tokens:
surface_form = token.content if isinstance(token, AddedToken) else token
if surface_form not in SPECIAL_TOKENS:
raise ValueError('Adding unknown special tokens is not supported')
return 0
def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
"""
Save only the vocabulary of the tokenizer (vocabulary).
Returns:
`Tuple(str)`: Paths to the files saved.
"""
file_path = os.path.join(save_directory, "qwen.tiktoken")
with open(file_path, "w", encoding="utf8") as w:
for k, v in self.mergeable_ranks.items():
line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
w.write(line)
return (file_path,)
def tokenize(
self,
text: str,
allowed_special: Union[Set, str] = "all",
disallowed_special: Union[Collection, str] = (),
**kwargs,
) -> List[Union[bytes, str]]:
"""
Converts a string in a sequence of tokens.
Args:
text (`str`):
The sequence to be encoded.
allowed_special (`Literal["all"]` or `set`):
The surface forms of the tokens to be encoded as special tokens in regular texts.
Default to "all".
disallowed_special (`Literal["all"]` or `Collection`):
The surface forms of the tokens that should not be in regular texts and trigger errors.
Default to an empty tuple.
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific encode method.
Returns:
`List[bytes|str]`: The list of tokens.
"""
tokens = []
text = unicodedata.normalize("NFC", text)
# this implementation takes a detour: text -> token id -> token surface forms
for t in self.tokenizer.encode(
text, allowed_special=allowed_special, disallowed_special=disallowed_special
):
tokens.append(self.decoder[t])
return tokens
def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
"""
Converts a sequence of tokens in a single string.
"""
text = ""
temp = b""
for t in tokens:
if isinstance(t, str):
if temp:
text += temp.decode("utf-8", errors=self.errors)
temp = b""
text += t
elif isinstance(t, bytes):
temp += t
else:
raise TypeError("token should only be of type types or str")
if temp:
text += temp.decode("utf-8", errors=self.errors)
return text
@property
def vocab_size(self):
return self.tokenizer.n_vocab
def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
"""Converts an id to a token, special tokens included"""
if index in self.decoder:
return self.decoder[index]
raise ValueError("unknown ids")
def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
"""Converts a token to an id using the vocab, special tokens included"""
if token in self.special_tokens:
return self.special_tokens[token]
if token in self.mergeable_ranks:
return self.mergeable_ranks[token]
raise ValueError("unknown token")
def _tokenize(self, text: str, **kwargs):
"""
Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
Do NOT take care of added tokens.
"""
raise NotImplementedError
def _decode(
self,
token_ids: Union[int, List[int]],
skip_special_tokens: bool = False,
errors: str = None,
**kwargs,
) -> str:
if isinstance(token_ids, int):
token_ids = [token_ids]
if skip_special_tokens:
token_ids = [i for i in token_ids if i < self.eod_id]
return self.tokenizer.decode(token_ids, errors=errors or self.errors)

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tokenizer_config.json Normal file
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{
"model_max_length": 8192,
"tokenizer_class": "QWenTokenizer",
"auto_map": {
"AutoTokenizer": [
"tokenization_qwen.QWenTokenizer",
null
]
}
}