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README.md ADDED
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+ ---
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+ library_name: peft
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+ tags:
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+ - alignment-handbook
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+ - generated_from_trainer
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+ # base_model: /ML-A100/team/mm/zhangge/iterativeDPO/data/model/full/neo_7B_sft_v0_1_plus-dpo-iter1-beta0_3
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+ # datasets:
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+ # - /ML-A100/team/mm/zhangge/iterativeDPO/data/dataset/generate/neo_7B_sft_v0_1_plus-dpo-iter1-beta0_3-generate-chosen-rejected-reward
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+ model-index:
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+ - name: neo_7B_sft_v0_1_plus-dpo-iter2-beta0_05
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+ results: []
12
+ ---
13
+
14
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
15
+ should probably proofread and complete it, then remove this comment. -->
16
+
17
+ # neo_7B_sft_v0_1_plus-dpo-iter2-beta0_05
18
+
19
+ This model is a fine-tuned version of [/ML-A100/team/mm/zhangge/iterativeDPO/data/model/full/neo_7B_sft_v0_1_plus-dpo-iter1-beta0_3](https://huggingface.co//ML-A100/team/mm/zhangge/iterativeDPO/data/model/full/neo_7B_sft_v0_1_plus-dpo-iter1-beta0_3) on the /ML-A100/team/mm/zhangge/iterativeDPO/data/dataset/generate/neo_7B_sft_v0_1_plus-dpo-iter1-beta0_3-generate-chosen-rejected-reward dataset.
20
+
21
+ ## Model description
22
+
23
+ More information needed
24
+
25
+ ## Intended uses & limitations
26
+
27
+ More information needed
28
+
29
+ ## Training and evaluation data
30
+
31
+ More information needed
32
+
33
+ ## Training procedure
34
+
35
+ ### Training hyperparameters
36
+
37
+ The following hyperparameters were used during training:
38
+ - learning_rate: 5e-06
39
+ - train_batch_size: 3
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+ - eval_batch_size: 8
41
+ - seed: 42
42
+ - distributed_type: multi-GPU
43
+ - num_devices: 128
44
+ - total_train_batch_size: 384
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+ - total_eval_batch_size: 1024
46
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
47
+ - lr_scheduler_type: cosine
48
+ - lr_scheduler_warmup_ratio: 0.1
49
+ - num_epochs: 1
50
+
51
+ ### Training results
52
+
53
+
54
+
55
+ ### Framework versions
56
+
57
+ - PEFT 0.7.1
58
+ - Transformers 4.39.0.dev0
59
+ - Pytorch 2.3.0+cu121
60
+ - Datasets 2.14.6
61
+ - Tokenizers 0.15.2
adapter_config.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "alpha_pattern": {},
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+ "auto_mapping": null,
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+ "base_model_name_or_path": "/ML-A100/team/mm/zhangge/iterativeDPO/data/model/full/neo_7B_sft_v0_1_plus-dpo-iter1-beta0_3",
5
+ "bias": "none",
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+ "fan_in_fan_out": false,
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+ "inference_mode": true,
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+ "init_lora_weights": true,
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+ "layers_pattern": null,
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+ "layers_to_transform": null,
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+ "loftq_config": {},
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+ "lora_alpha": 128,
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+ "lora_dropout": 0.05,
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+ "megatron_config": null,
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+ "megatron_core": "megatron.core",
16
+ "modules_to_save": null,
17
+ "peft_type": "LORA",
18
+ "r": 128,
19
+ "rank_pattern": {},
20
+ "revision": null,
21
+ "target_modules": [
22
+ "down_proj",
23
+ "gate_proj",
24
+ "v_proj",
25
+ "k_proj",
26
+ "o_proj",
27
+ "q_proj",
28
+ "up_proj"
29
+ ],
30
+ "task_type": "CAUSAL_LM"
31
+ }
added_tokens.json ADDED
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+ {
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+ "<|CLS|>": 64000,
3
+ "<|EOD|>": 64002,
4
+ "<|MASK|>": 64003,
5
+ "<|PAD|>": 64004,
6
+ "<|SEP|>": 64001
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+ }
all_results.json ADDED
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+ {
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+ "epoch": 1.0,
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+ "eval_logits/chosen": -3.900073289871216,
4
+ "eval_logits/rejected": -3.9142141342163086,
5
+ "eval_logps/chosen": -293.32354736328125,
6
+ "eval_logps/rejected": -187.59616088867188,
7
+ "eval_loss": 0.6149587035179138,
8
+ "eval_rewards/accuracies": 0.6875,
9
+ "eval_rewards/chosen": -0.1575067937374115,
10
+ "eval_rewards/diff": -2.2334296703338623,
11
+ "eval_rewards/diff_abs": 2.236445188522339,
12
+ "eval_rewards/rejected": -0.33032703399658203,
13
+ "eval_rewards/student_margin": 0.17282025516033173,
14
+ "eval_rewards/teacher_margin": 2.40625,
15
+ "eval_runtime": 11.4547,
16
+ "eval_samples": 1470,
17
+ "eval_samples_per_second": 128.332,
18
+ "eval_steps_per_second": 0.175,
19
+ "train_loss": 0.6488963676806219,
20
+ "train_runtime": 2900.3403,
21
+ "train_samples": 147002,
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+ "train_samples_per_second": 50.684,
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+ "train_steps_per_second": 0.132
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+ }
config.json ADDED
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+ {
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+ "_name_or_path": "/ML-A100/team/mm/zhangge/iterativeDPO/data/model/full/neo_7B_sft_v0_1_plus-dpo-iter1-beta0_3",
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+ "architectures": [
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+ "LlamaForCausalLM"
5
+ ],
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+ "attention_bias": false,
7
+ "attention_dropout": 0.0,
8
+ "bos_token_id": 1,
9
+ "eos_token_id": 2,
10
+ "hidden_act": "silu",
11
+ "hidden_size": 3072,
12
+ "initializer_range": 0.02,
13
+ "intermediate_size": 24576,
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+ "max_position_embeddings": 8192,
15
+ "model_type": "llama",
16
+ "num_attention_heads": 16,
17
+ "num_hidden_layers": 28,
18
+ "num_key_value_heads": 16,
19
+ "pretraining_tp": 1,
20
+ "rms_norm_eps": 1e-05,
21
+ "rope_scaling": null,
22
+ "rope_theta": 10000.0,
23
+ "tie_word_embeddings": false,
24
+ "torch_dtype": "bfloat16",
25
+ "transformers_version": "4.39.0.dev0",
26
+ "use_cache": true,
27
+ "vocab_size": 64256
28
+ }
eval_results.json ADDED
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+ {
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+ "epoch": 1.0,
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+ "eval_logits/chosen": -3.900073289871216,
4
+ "eval_logits/rejected": -3.9142141342163086,
5
+ "eval_logps/chosen": -293.32354736328125,
6
+ "eval_logps/rejected": -187.59616088867188,
7
+ "eval_loss": 0.6149587035179138,
8
+ "eval_rewards/accuracies": 0.6875,
9
+ "eval_rewards/chosen": -0.1575067937374115,
10
+ "eval_rewards/diff": -2.2334296703338623,
11
+ "eval_rewards/diff_abs": 2.236445188522339,
12
+ "eval_rewards/rejected": -0.33032703399658203,
13
+ "eval_rewards/student_margin": 0.17282025516033173,
14
+ "eval_rewards/teacher_margin": 2.40625,
15
+ "eval_runtime": 11.4547,
16
+ "eval_samples": 1470,
17
+ "eval_samples_per_second": 128.332,
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+ "eval_steps_per_second": 0.175
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+ }
special_tokens_map.json ADDED
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+ {
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+ "additional_special_tokens": [
3
+ "<|CLS|>",
4
+ "<|SEP|>",
5
+ "<|EOD|>",
6
+ "<|MASK|>",
7
+ "<|PAD|>"
8
+ ],
9
+ "bos_token": {
10
+ "content": "<s>",
11
+ "lstrip": false,
12
+ "normalized": true,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "eos_token": {
17
+ "content": "</s>",
18
+ "lstrip": false,
19
+ "normalized": true,
20
+ "rstrip": false,
21
+ "single_word": true
22
+ },
23
+ "pad_token": {
24
+ "content": "<unk>",
25
+ "lstrip": false,
26
+ "normalized": true,
27
+ "rstrip": false,
28
+ "single_word": true
29
+ },
30
+ "unk_token": {
31
+ "content": "<unk>",
32
+ "lstrip": false,
33
+ "normalized": true,
34
+ "rstrip": false,
35
+ "single_word": true
36
+ }
37
+ }
tokenization_hkgpt.py ADDED
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1
+ # Copyright 2024 HKGAI Inc. All Rights Reserved.
2
+
3
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
4
+ #
5
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
6
+ # and OPT implementations in this library. It has been modified from its
7
+ # original forms to accommodate minor architectural differences compared
8
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
9
+ #
10
+ # Licensed under the Apache License, Version 2.0 (the "License");
11
+ # you may not use this file except in compliance with the License.
12
+ # You may obtain a copy of the License at
13
+ #
14
+ # http://www.apache.org/licenses/LICENSE-2.0
15
+ #
16
+ # Unless required by applicable law or agreed to in writing, software
17
+ # distributed under the License is distributed on an "AS IS" BASIS,
18
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
19
+ # See the License for the specific language governing permissions and
20
+ # limitations under the License.
21
+
22
+ import os
23
+ from shutil import copyfile
24
+ from typing import Any, Dict, List, Optional, Tuple
25
+
26
+ import sentencepiece as spm
27
+
28
+ from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
29
+ from transformers.utils import logging
30
+
31
+
32
+ logger = logging.get_logger(__name__)
33
+
34
+ VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
35
+
36
+ PRETRAINED_VOCAB_FILES_MAP = {
37
+ "vocab_file": {},
38
+ "tokenizer_file": {},
39
+ }
40
+ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
41
+
42
+
43
+ class HKGPTTokenizer(PreTrainedTokenizer):
44
+ """
45
+ Construct a HKGPT tokenizer. Based on byte-level Byte-Pair-Encoding.
46
+
47
+ Args:
48
+ vocab_file (`str`):
49
+ Path to the vocabulary file.
50
+ """
51
+
52
+ vocab_files_names = VOCAB_FILES_NAMES
53
+ pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
54
+ max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
55
+ model_input_names = ["input_ids", "attention_mask"]
56
+
57
+ def __init__(
58
+ self,
59
+ vocab_file,
60
+ unk_token="<unk>",
61
+ bos_token="<s>",
62
+ eos_token="</s>",
63
+ pad_token=None,
64
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
65
+ add_bos_token=True,
66
+ add_eos_token=False,
67
+ clean_up_tokenization_spaces=False,
68
+ **kwargs,
69
+ ):
70
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
71
+ bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
72
+ eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
73
+ unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
74
+ pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
75
+
76
+ self.vocab_file = vocab_file
77
+ self.add_bos_token = add_bos_token
78
+ self.add_eos_token = add_eos_token
79
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
80
+ self.sp_model.Load(vocab_file)
81
+
82
+ super().__init__(
83
+ bos_token=bos_token,
84
+ eos_token=eos_token,
85
+ unk_token=unk_token,
86
+ pad_token=pad_token,
87
+ add_bos_token=add_bos_token,
88
+ add_eos_token=add_eos_token,
89
+ sp_model_kwargs=self.sp_model_kwargs,
90
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
91
+ **kwargs,
92
+ )
93
+
94
+ def __getstate__(self):
95
+ state = self.__dict__.copy()
96
+ state["sp_model"] = None
97
+ return state
98
+
99
+ def __setstate__(self, d):
100
+ self.__dict__ = d
101
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
102
+ self.sp_model.Load(self.vocab_file)
103
+
104
+ @property
105
+ def vocab_size(self):
106
+ """Returns vocab size"""
107
+ return self.sp_model.get_piece_size()
108
+
109
+ def get_vocab(self):
110
+ """Returns vocab as a dict"""
111
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
112
+ vocab.update(self.added_tokens_encoder)
113
+ return vocab
114
+
115
+ def _tokenize(self, text):
116
+ """Returns a tokenized string."""
117
+ return self.sp_model.encode(text, out_type=str)
118
+
119
+ def _convert_token_to_id(self, token):
120
+ """Converts a token (str) in an id using the vocab."""
121
+ return self.sp_model.piece_to_id(token)
122
+
123
+ def _convert_id_to_token(self, index):
124
+ """Converts an index (integer) in a token (str) using the vocab."""
125
+ token = self.sp_model.IdToPiece(index)
126
+ return token
127
+
128
+ def convert_tokens_to_string(self, tokens):
129
+ """Converts a sequence of tokens (string) in a single string."""
130
+ current_sub_tokens = []
131
+ out_string = ""
132
+ prev_is_special = False
133
+ for i, token in enumerate(tokens):
134
+ # make sure that special tokens are not decoded using sentencepiece model
135
+ if token in self.all_special_tokens:
136
+ if not prev_is_special and i != 0:
137
+ out_string += " "
138
+ out_string += self.sp_model.decode(current_sub_tokens) + token
139
+ prev_is_special = True
140
+ current_sub_tokens = []
141
+ else:
142
+ current_sub_tokens.append(token)
143
+ prev_is_special = False
144
+ out_string += self.sp_model.decode(current_sub_tokens)
145
+ return out_string
146
+
147
+ def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
148
+ """
149
+ Save the vocabulary and special tokens file to a directory.
150
+
151
+ Args:
152
+ save_directory (`str`):
153
+ The directory in which to save the vocabulary.
154
+
155
+ Returns:
156
+ `Tuple(str)`: Paths to the files saved.
157
+ """
158
+ if not os.path.isdir(save_directory):
159
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
160
+ return
161
+ out_vocab_file = os.path.join(
162
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
163
+ )
164
+
165
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
166
+ copyfile(self.vocab_file, out_vocab_file)
167
+ elif not os.path.isfile(self.vocab_file):
168
+ with open(out_vocab_file, "wb") as fi:
169
+ content_spiece_model = self.sp_model.serialized_model_proto()
170
+ fi.write(content_spiece_model)
171
+
172
+ return (out_vocab_file,)
173
+
174
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
175
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
176
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
177
+
178
+ output = bos_token_id + token_ids_0 + eos_token_id
179
+
180
+ if token_ids_1 is not None:
181
+ output = output + bos_token_id + token_ids_1 + eos_token_id
182
+
183
+ return output
184
+
185
+ def get_special_tokens_mask(
186
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
187
+ ) -> List[int]:
188
+ """
189
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
190
+ special tokens using the tokenizer `prepare_for_model` method.
191
+
192
+ Args:
193
+ token_ids_0 (`List[int]`):
194
+ List of IDs.
195
+ token_ids_1 (`List[int]`, *optional*):
196
+ Optional second list of IDs for sequence pairs.
197
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
198
+ Whether or not the token list is already formatted with special tokens for the model.
199
+
200
+ Returns:
201
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
202
+ """
203
+ if already_has_special_tokens:
204
+ return super().get_special_tokens_mask(
205
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
206
+ )
207
+
208
+ bos_token_id = [1] if self.add_bos_token else []
209
+ eos_token_id = [1] if self.add_eos_token else []
210
+
211
+ if token_ids_1 is None:
212
+ return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
213
+ return (
214
+ bos_token_id
215
+ + ([0] * len(token_ids_0))
216
+ + eos_token_id
217
+ + bos_token_id
218
+ + ([0] * len(token_ids_1))
219
+ + eos_token_id
220
+ )
221
+
222
+ def create_token_type_ids_from_sequences(
223
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
224
+ ) -> List[int]:
225
+ """
226
+ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
227
+ sequence pair mask has the following format:
228
+
229
+ ```
230
+ 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
231
+ | first sequence | second sequence |
232
+ ```
233
+
234
+ if token_ids_1 is None, only returns the first portion of the mask (0s).
235
+
236
+ Args:
237
+ token_ids_0 (`List[int]`):
238
+ List of ids.
239
+ token_ids_1 (`List[int]`, *optional*):
240
+ Optional second list of IDs for sequence pairs.
241
+
242
+ Returns:
243
+ `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
244
+ """
245
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
246
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
247
+
248
+ output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
249
+
250
+ if token_ids_1 is not None:
251
+ output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
252
+
253
+ return output
tokenizer_config.json ADDED
@@ -0,0 +1,95 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_bos_token": false,
3
+ "add_eos_token": false,
4
+ "added_tokens_decoder": {
5
+ "0": {
6
+ "content": "<unk>",
7
+ "lstrip": false,
8
+ "normalized": true,
9
+ "rstrip": false,
10
+ "single_word": true,
11
+ "special": true
12
+ },
13
+ "1": {
14
+ "content": "<s>",
15
+ "lstrip": false,
16
+ "normalized": true,
17
+ "rstrip": false,
18
+ "single_word": false,
19
+ "special": true
20
+ },
21
+ "2": {
22
+ "content": "</s>",
23
+ "lstrip": false,
24
+ "normalized": true,
25
+ "rstrip": false,
26
+ "single_word": true,
27
+ "special": true
28
+ },
29
+ "64000": {
30
+ "content": "<|CLS|>",
31
+ "lstrip": false,
32
+ "normalized": false,
33
+ "rstrip": false,
34
+ "single_word": false,
35
+ "special": true
36
+ },
37
+ "64001": {
38
+ "content": "<|SEP|>",
39
+ "lstrip": false,
40
+ "normalized": false,
41
+ "rstrip": false,
42
+ "single_word": false,
43
+ "special": true
44
+ },
45
+ "64002": {
46
+ "content": "<|EOD|>",
47
+ "lstrip": false,
48
+ "normalized": false,
49
+ "rstrip": false,
50
+ "single_word": false,
51
+ "special": true
52
+ },
53
+ "64003": {
54
+ "content": "<|MASK|>",
55
+ "lstrip": false,
56
+ "normalized": false,
57
+ "rstrip": false,
58
+ "single_word": false,
59
+ "special": true
60
+ },
61
+ "64004": {
62
+ "content": "<|PAD|>",
63
+ "lstrip": false,
64
+ "normalized": false,
65
+ "rstrip": false,
66
+ "single_word": false,
67
+ "special": true
68
+ }
69
+ },
70
+ "additional_special_tokens": [
71
+ "<|CLS|>",
72
+ "<|SEP|>",
73
+ "<|EOD|>",
74
+ "<|MASK|>",
75
+ "<|PAD|>"
76
+ ],
77
+ "auto_map": {
78
+ "AutoTokenizer": [
79
+ "tokenization_hkgpt.HKGPTTokenizer",
80
+ null
81
+ ]
82
+ },
83
+ "bos_token": "<s>",
84
+ "chat_template": "{% set system_message = 'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\\n\\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don\\'t know the answer to a question, please don\\'t share false information.' %}{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if loop.index0 == 0 and system_message is defined %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '<s>' + '[INST] ' + content + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ content + '</s>' }}{% endif %}{% endfor %}",
85
+ "clean_up_tokenization_spaces": false,
86
+ "eos_token": "</s>",
87
+ "model_max_length": 4096,
88
+ "pad_token": "<unk>",
89
+ "padding_side": "right",
90
+ "sp_model_kwargs": {},
91
+ "split_special_tokens": false,
92
+ "tokenizer_class": "HKGPTTokenizer",
93
+ "unk_token": "<unk>",
94
+ "use_fast": false
95
+ }