diff --git a/LLM-Detector-V1-4w/README.md b/LLM-Detector-V1-4w/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6d11b4c9e52483400eda176f7f8fc7b5c8cd1b62 --- /dev/null +++ b/LLM-Detector-V1-4w/README.md @@ -0,0 +1,66 @@ +--- +base_model: ../Baichuan2-7B-Chat +tags: +- llama-factory +- lora +- generated_from_trainer +model-index: +- name: hc3zh + results: [] +--- + + + +# hc3zh + +This model is a fine-tuned version of [../Baichuan2-7B-Chat](https://huggingface.co/../Baichuan2-7B-Chat) on the hc3zh dataset. +It achieves the following results on the evaluation set: +- Loss: 0.0150 + +## Model description + +More information needed + +## Intended uses & limitations + +More information needed + +## Training and evaluation data + +More information needed + +## Training procedure + +### Training hyperparameters + +The following hyperparameters were used during training: +- learning_rate: 5e-05 +- train_batch_size: 8 +- eval_batch_size: 8 +- seed: 42 +- gradient_accumulation_steps: 4 +- total_train_batch_size: 32 +- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 +- lr_scheduler_type: cosine +- num_epochs: 3.0 + +### Training results + +| Training Loss | Epoch | Step | Validation Loss | +|:-------------:|:-----:|:----:|:---------------:| +| 0.0199 | 0.42 | 500 | 0.0105 | +| 0.0011 | 0.85 | 1000 | 0.0118 | +| 0.0001 | 1.27 | 1500 | 0.0110 | +| 0.0143 | 1.7 | 2000 | 0.0135 | +| 0.0001 | 2.12 | 2500 | 0.0129 | +| 0.0001 | 2.55 | 3000 | 0.0145 | +| 0.002 | 2.97 | 3500 | 0.0150 | + + +### Framework versions + +- Transformers 4.32.1 +- Pytorch 2.1.0+cu121 +- Datasets 2.14.6 +- Tokenizers 0.13.2 diff --git a/LLM-Detector-V1-4w/adapter_config.json b/LLM-Detector-V1-4w/adapter_config.json new file mode 100644 index 0000000000000000000000000000000000000000..54acbac2c279e46331f75c340af98595a8683d48 --- /dev/null +++ b/LLM-Detector-V1-4w/adapter_config.json @@ -0,0 +1,22 @@ +{ + "alpha_pattern": {}, + "auto_mapping": null, + "base_model_name_or_path": "../Baichuan2-7B-Chat", + "bias": "none", + "fan_in_fan_out": false, + "inference_mode": true, + "init_lora_weights": true, + "layers_pattern": null, + "layers_to_transform": null, + "lora_alpha": 32.0, + "lora_dropout": 0.1, + "modules_to_save": null, + "peft_type": "LORA", + "r": 8, + "rank_pattern": {}, + "revision": null, + "target_modules": [ + "W_pack" + ], + "task_type": "CAUSAL_LM" +} \ No newline at end of file diff --git a/LLM-Detector-V1-4w/adapter_model.bin b/LLM-Detector-V1-4w/adapter_model.bin new file mode 100644 index 0000000000000000000000000000000000000000..73a4f4766fd048ad38964019279ee9b86a50644e --- /dev/null +++ b/LLM-Detector-V1-4w/adapter_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:43824a5d6a7ba3851d5cdec7eaebba477ebc4dc160eeeb85afd21cc987ec7440 +size 16800430 diff --git a/LLM-Detector-V1-4w/all_results.json b/LLM-Detector-V1-4w/all_results.json new file mode 100644 index 0000000000000000000000000000000000000000..1dd35543727978998f481967dd84990c16c23c6a --- /dev/null +++ b/LLM-Detector-V1-4w/all_results.json @@ -0,0 +1,11 @@ +{ + "epoch": 3.0, + "eval_loss": 0.014986271038651466, + "eval_runtime": 87.9616, + "eval_samples_per_second": 22.544, + "eval_steps_per_second": 2.819, + "train_loss": 0.06714861565509712, + "train_runtime": 17560.0547, + "train_samples_per_second": 6.434, + "train_steps_per_second": 0.201 +} \ No newline at end of file diff --git a/LLM-Detector-V1-4w/checkpoint-1000/README.md b/LLM-Detector-V1-4w/checkpoint-1000/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a04450aa7d792898a89dd2c6093050ffd3808789 --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-1000/README.md @@ -0,0 +1,219 @@ +--- +library_name: peft +base_model: ../Baichuan2-7B-Chat +--- + +# Model Card for Model ID + + + + + +## Model Details + +### Model Description + + + + + +- **Developed by:** [More Information Needed] +- **Shared by [optional]:** [More Information Needed] +- **Model type:** [More Information Needed] +- **Language(s) (NLP):** [More Information Needed] +- **License:** [More Information Needed] +- **Finetuned from model [optional]:** [More Information Needed] + +### Model Sources [optional] + + + +- **Repository:** [More Information Needed] +- **Paper [optional]:** [More Information Needed] +- **Demo [optional]:** [More Information Needed] + +## Uses + + + +### Direct Use + + + +[More Information Needed] + +### Downstream Use [optional] + + + +[More Information Needed] + +### Out-of-Scope Use + + + +[More Information Needed] + +## Bias, Risks, and Limitations + + + +[More Information Needed] + +### Recommendations + + + +Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. + +## How to Get Started with the Model + +Use the code below to get started with the model. + +[More Information Needed] + +## Training Details + +### Training Data + + + +[More Information Needed] + +### Training Procedure + + + +#### Preprocessing [optional] + +[More Information Needed] + + +#### Training Hyperparameters + +- **Training regime:** [More Information Needed] + +#### Speeds, Sizes, Times [optional] + + + +[More Information Needed] + +## Evaluation + + + +### Testing Data, Factors & Metrics + +#### Testing Data + + + +[More Information Needed] + +#### Factors + + + +[More Information Needed] + +#### Metrics + + + +[More Information Needed] + +### Results + +[More Information Needed] + +#### Summary + + + +## Model Examination [optional] + + + +[More Information Needed] + +## Environmental Impact + + + +Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). + +- **Hardware Type:** [More Information Needed] +- **Hours used:** [More Information Needed] +- **Cloud Provider:** [More Information Needed] +- **Compute Region:** [More Information Needed] +- **Carbon Emitted:** [More Information Needed] + +## Technical Specifications [optional] + +### Model Architecture and Objective + +[More Information Needed] + +### Compute Infrastructure + +[More Information Needed] + +#### Hardware + +[More Information Needed] + +#### Software + +[More Information Needed] + +## Citation [optional] + + + +**BibTeX:** + +[More Information Needed] + +**APA:** + +[More Information Needed] + +## Glossary [optional] + + + +[More Information Needed] + +## More Information [optional] + +[More Information Needed] + +## Model Card Authors [optional] + +[More Information Needed] + +## Model Card Contact + +[More Information Needed] + + +## Training procedure + + +The following `bitsandbytes` quantization config was used during training: +- quant_method: QuantizationMethod.BITS_AND_BYTES +- load_in_8bit: False +- load_in_4bit: True +- llm_int8_threshold: 6.0 +- llm_int8_skip_modules: None +- llm_int8_enable_fp32_cpu_offload: False +- llm_int8_has_fp16_weight: False +- bnb_4bit_quant_type: nf4 +- bnb_4bit_use_double_quant: True +- bnb_4bit_compute_dtype: float16 + +### Framework versions + + +- PEFT 0.6.0 diff --git a/LLM-Detector-V1-4w/checkpoint-1000/adapter_config.json b/LLM-Detector-V1-4w/checkpoint-1000/adapter_config.json new file mode 100644 index 0000000000000000000000000000000000000000..54acbac2c279e46331f75c340af98595a8683d48 --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-1000/adapter_config.json @@ -0,0 +1,22 @@ +{ + "alpha_pattern": {}, + "auto_mapping": null, + "base_model_name_or_path": "../Baichuan2-7B-Chat", + "bias": "none", + "fan_in_fan_out": false, + "inference_mode": true, + "init_lora_weights": true, + "layers_pattern": null, + "layers_to_transform": null, + "lora_alpha": 32.0, + "lora_dropout": 0.1, + "modules_to_save": null, + "peft_type": "LORA", + "r": 8, + "rank_pattern": {}, + "revision": null, + "target_modules": [ + "W_pack" + ], + "task_type": "CAUSAL_LM" +} \ No newline at end of file diff --git a/LLM-Detector-V1-4w/checkpoint-1000/adapter_model.bin b/LLM-Detector-V1-4w/checkpoint-1000/adapter_model.bin new file mode 100644 index 0000000000000000000000000000000000000000..48e31281cb0edcb978597b91fe27fbb7af76c293 --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-1000/adapter_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fe40450b2fbd3f10782fef8e66d9acb4e5cac016892e806376a3af80925fad96 +size 16800430 diff --git a/LLM-Detector-V1-4w/checkpoint-1000/optimizer.pt b/LLM-Detector-V1-4w/checkpoint-1000/optimizer.pt new file mode 100644 index 0000000000000000000000000000000000000000..01dea075500b0cf47cdfe72f930ddf0cab0b595d --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-1000/optimizer.pt @@ -0,0 +1,3 @@ +version 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1064 diff --git a/LLM-Detector-V1-4w/checkpoint-1000/special_tokens_map.json b/LLM-Detector-V1-4w/checkpoint-1000/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..5819ea25d7b1b6340063a0629c2143c44b0452da --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-1000/special_tokens_map.json @@ -0,0 +1,30 @@ +{ + "bos_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "pad_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "unk_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/LLM-Detector-V1-4w/checkpoint-1000/tokenization_baichuan.py b/LLM-Detector-V1-4w/checkpoint-1000/tokenization_baichuan.py new file mode 100644 index 0000000000000000000000000000000000000000..256544b45542d2f5dcd12a65e2f0ddaeeb9def25 --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-1000/tokenization_baichuan.py @@ -0,0 +1,251 @@ +# Copyright 2023 Baichuan Inc. All Rights Reserved. + +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from shutil import copyfile +from typing import Any, Dict, List, Optional, Tuple + +import sentencepiece as spm + +from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} + +PRETRAINED_VOCAB_FILES_MAP = { + "vocab_file": {}, + "tokenizer_file": {}, +} +PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {} + + +class BaichuanTokenizer(PreTrainedTokenizer): + """ + Construct a Baichuan tokenizer. Based on byte-level Byte-Pair-Encoding. + + Args: + vocab_file (`str`): + Path to the vocabulary file. + """ + + vocab_files_names = VOCAB_FILES_NAMES + pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP + max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES + model_input_names = ["input_ids", "attention_mask"] + + def __init__( + self, + vocab_file, + unk_token="", + bos_token="", + eos_token="", + pad_token=None, + sp_model_kwargs: Optional[Dict[str, Any]] = None, + add_bos_token=True, + add_eos_token=False, + clean_up_tokenization_spaces=False, + **kwargs, + ): + self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs + bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token + eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token + unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token + pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token + super().__init__( + bos_token=bos_token, + eos_token=eos_token, + unk_token=unk_token, + pad_token=pad_token, + add_bos_token=add_bos_token, + add_eos_token=add_eos_token, + sp_model_kwargs=self.sp_model_kwargs, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + self.vocab_file = vocab_file + self.add_bos_token = add_bos_token + self.add_eos_token = add_eos_token + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(vocab_file) + + def __getstate__(self): + state = self.__dict__.copy() + state["sp_model"] = None + return state + + def __setstate__(self, d): + self.__dict__ = d + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(self.vocab_file) + + @property + def vocab_size(self): + """Returns vocab size""" + return self.sp_model.get_piece_size() + + def get_vocab(self): + """Returns vocab as a dict""" + vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} + vocab.update(self.added_tokens_encoder) + return vocab + + def _tokenize(self, text): + """Returns a tokenized string.""" + return self.sp_model.encode(text, out_type=str) + + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + return self.sp_model.piece_to_id(token) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + token = self.sp_model.IdToPiece(index) + return token + + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (string) in a single string.""" + current_sub_tokens = [] + out_string = "" + prev_is_special = False + for i, token in enumerate(tokens): + # make sure that special tokens are not decoded using sentencepiece model + if token in self.all_special_tokens: + if not prev_is_special and i != 0: + out_string += " " + out_string += self.sp_model.decode(current_sub_tokens) + token + prev_is_special = True + current_sub_tokens = [] + else: + current_sub_tokens.append(token) + prev_is_special = False + out_string += self.sp_model.decode(current_sub_tokens) + return out_string + + def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]: + """ + Save the vocabulary and special tokens file to a directory. + + Args: + save_directory (`str`): + The directory in which to save the vocabulary. + + Returns: + `Tuple(str)`: Paths to the files saved. + """ + if not os.path.isdir(save_directory): + logger.error(f"Vocabulary path ({save_directory}) should be a directory") + return + out_vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + + if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): + copyfile(self.vocab_file, out_vocab_file) + elif not os.path.isfile(self.vocab_file): + with open(out_vocab_file, "wb") as fi: + content_spiece_model = self.sp_model.serialized_model_proto() + fi.write(content_spiece_model) + + return (out_vocab_file,) + + def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): + 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]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + 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]: + """ + Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT + sequence pair mask has the following format: + + ``` + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence | + ``` + + if token_ids_1 is None, only returns the first portion of the mask (0s). + + Args: + token_ids_0 (`List[int]`): + List of ids. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). + """ + 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 = [0] * len(bos_token_id + token_ids_0 + eos_token_id) + + if token_ids_1 is not None: + output += [1] * len(bos_token_id + token_ids_1 + eos_token_id) + + return output diff --git a/LLM-Detector-V1-4w/checkpoint-1000/tokenizer.model b/LLM-Detector-V1-4w/checkpoint-1000/tokenizer.model new file mode 100644 index 0000000000000000000000000000000000000000..b3902c4521d7f34868ac76dd16150ff5ca41b000 --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-1000/tokenizer.model @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:79452955be6b419a65984273a9f08af86042e1c2a75ee3ba989cbf620a133cc2 +size 2001107 diff --git a/LLM-Detector-V1-4w/checkpoint-1000/tokenizer_config.json b/LLM-Detector-V1-4w/checkpoint-1000/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..d14486d6a5be1135bdda779a8ffcde1b77155302 --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-1000/tokenizer_config.json @@ -0,0 +1,49 @@ +{ + "add_bos_token": false, + "add_eos_token": false, + "auto_map": { + "AutoTokenizer": [ + "tokenization_baichuan.BaichuanTokenizer", + null + ] + }, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "clean_up_tokenization_spaces": false, + "eos_token": { + 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b/LLM-Detector-V1-4w/checkpoint-1000/training_args.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c31eb820fabf5021fa0eda935da3d201c65c7331d3ce4ce4ad4631151a6068e9 +size 4664 diff --git a/LLM-Detector-V1-4w/checkpoint-2000/README.md b/LLM-Detector-V1-4w/checkpoint-2000/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a04450aa7d792898a89dd2c6093050ffd3808789 --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-2000/README.md @@ -0,0 +1,219 @@ +--- +library_name: peft +base_model: ../Baichuan2-7B-Chat +--- + +# Model Card for Model ID + + + + + +## Model Details + +### Model Description + + + + + +- **Developed by:** [More Information Needed] +- **Shared by [optional]:** [More Information Needed] +- **Model type:** [More Information Needed] +- **Language(s) (NLP):** [More Information Needed] +- **License:** [More Information Needed] +- **Finetuned from model [optional]:** [More Information Needed] + +### Model Sources [optional] + + + +- **Repository:** [More Information Needed] +- **Paper [optional]:** [More Information Needed] +- **Demo [optional]:** [More Information Needed] + +## Uses + + + +### Direct Use + + + +[More Information Needed] + +### Downstream Use [optional] + + + +[More Information Needed] + +### Out-of-Scope Use + + + +[More Information Needed] + +## Bias, Risks, and Limitations + + + +[More Information Needed] + +### Recommendations + + + +Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. + +## How to Get Started with the Model + +Use the code below to get started with the model. + +[More Information Needed] + +## Training Details + +### Training Data + + + +[More Information Needed] + +### Training Procedure + + + +#### Preprocessing [optional] + +[More Information Needed] + + +#### Training Hyperparameters + +- **Training regime:** [More Information Needed] + +#### Speeds, Sizes, Times [optional] + + + +[More Information Needed] + +## Evaluation + + + +### Testing Data, Factors & Metrics + +#### Testing Data + + + +[More Information Needed] + +#### Factors + + + +[More Information Needed] + +#### Metrics + + + +[More Information Needed] + +### Results + +[More Information Needed] + +#### Summary + + + +## Model Examination [optional] + + + +[More Information Needed] + +## Environmental Impact + + + +Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). + +- **Hardware Type:** [More Information Needed] +- **Hours used:** [More Information Needed] +- **Cloud Provider:** [More Information Needed] +- **Compute Region:** [More Information Needed] +- **Carbon Emitted:** [More Information Needed] + +## Technical Specifications [optional] + +### Model Architecture and Objective + +[More Information Needed] + +### Compute Infrastructure + +[More Information Needed] + +#### Hardware + +[More Information Needed] + +#### Software + +[More Information Needed] + +## Citation [optional] + + + +**BibTeX:** + +[More Information Needed] + +**APA:** + +[More Information Needed] + +## Glossary [optional] + + + +[More Information Needed] + +## More Information [optional] + +[More Information Needed] + +## Model Card Authors [optional] + +[More Information Needed] + +## Model Card Contact + +[More Information Needed] + + +## Training procedure + + +The following `bitsandbytes` quantization config was used during training: +- quant_method: QuantizationMethod.BITS_AND_BYTES +- load_in_8bit: False +- load_in_4bit: True +- llm_int8_threshold: 6.0 +- llm_int8_skip_modules: None +- llm_int8_enable_fp32_cpu_offload: False +- llm_int8_has_fp16_weight: False +- bnb_4bit_quant_type: nf4 +- bnb_4bit_use_double_quant: True +- bnb_4bit_compute_dtype: float16 + +### Framework versions + + +- PEFT 0.6.0 diff --git a/LLM-Detector-V1-4w/checkpoint-2000/adapter_config.json b/LLM-Detector-V1-4w/checkpoint-2000/adapter_config.json new file mode 100644 index 0000000000000000000000000000000000000000..54acbac2c279e46331f75c340af98595a8683d48 --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-2000/adapter_config.json @@ -0,0 +1,22 @@ +{ + "alpha_pattern": {}, + "auto_mapping": null, + "base_model_name_or_path": "../Baichuan2-7B-Chat", + "bias": "none", + "fan_in_fan_out": false, + "inference_mode": true, + "init_lora_weights": true, + "layers_pattern": null, + "layers_to_transform": null, + "lora_alpha": 32.0, + 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1064 diff --git a/LLM-Detector-V1-4w/checkpoint-2000/special_tokens_map.json b/LLM-Detector-V1-4w/checkpoint-2000/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..5819ea25d7b1b6340063a0629c2143c44b0452da --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-2000/special_tokens_map.json @@ -0,0 +1,30 @@ +{ + "bos_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "pad_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "unk_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/LLM-Detector-V1-4w/checkpoint-2000/tokenization_baichuan.py b/LLM-Detector-V1-4w/checkpoint-2000/tokenization_baichuan.py new file mode 100644 index 0000000000000000000000000000000000000000..256544b45542d2f5dcd12a65e2f0ddaeeb9def25 --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-2000/tokenization_baichuan.py @@ -0,0 +1,251 @@ +# Copyright 2023 Baichuan Inc. All Rights Reserved. + +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from shutil import copyfile +from typing import Any, Dict, List, Optional, Tuple + +import sentencepiece as spm + +from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} + +PRETRAINED_VOCAB_FILES_MAP = { + "vocab_file": {}, + "tokenizer_file": {}, +} +PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {} + + +class BaichuanTokenizer(PreTrainedTokenizer): + """ + Construct a Baichuan tokenizer. Based on byte-level Byte-Pair-Encoding. + + Args: + vocab_file (`str`): + Path to the vocabulary file. + """ + + vocab_files_names = VOCAB_FILES_NAMES + pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP + max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES + model_input_names = ["input_ids", "attention_mask"] + + def __init__( + self, + vocab_file, + unk_token="", + bos_token="", + eos_token="", + pad_token=None, + sp_model_kwargs: Optional[Dict[str, Any]] = None, + add_bos_token=True, + add_eos_token=False, + clean_up_tokenization_spaces=False, + **kwargs, + ): + self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs + bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token + eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token + unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token + pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token + super().__init__( + bos_token=bos_token, + eos_token=eos_token, + unk_token=unk_token, + pad_token=pad_token, + add_bos_token=add_bos_token, + add_eos_token=add_eos_token, + sp_model_kwargs=self.sp_model_kwargs, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + self.vocab_file = vocab_file + self.add_bos_token = add_bos_token + self.add_eos_token = add_eos_token + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(vocab_file) + + def __getstate__(self): + state = self.__dict__.copy() + state["sp_model"] = None + return state + + def __setstate__(self, d): + self.__dict__ = d + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(self.vocab_file) + + @property + def vocab_size(self): + """Returns vocab size""" + return self.sp_model.get_piece_size() + + def get_vocab(self): + """Returns vocab as a dict""" + vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} + vocab.update(self.added_tokens_encoder) + return vocab + + def _tokenize(self, text): + """Returns a tokenized string.""" + return self.sp_model.encode(text, out_type=str) + + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + return self.sp_model.piece_to_id(token) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + token = self.sp_model.IdToPiece(index) + return token + + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (string) in a single string.""" + current_sub_tokens = [] + out_string = "" + prev_is_special = False + for i, token in enumerate(tokens): + # make sure that special tokens are not decoded using sentencepiece model + if token in self.all_special_tokens: + if not prev_is_special and i != 0: + out_string += " " + out_string += self.sp_model.decode(current_sub_tokens) + token + prev_is_special = True + current_sub_tokens = [] + else: + current_sub_tokens.append(token) + prev_is_special = False + out_string += self.sp_model.decode(current_sub_tokens) + return out_string + + def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]: + """ + Save the vocabulary and special tokens file to a directory. + + Args: + save_directory (`str`): + The directory in which to save the vocabulary. + + Returns: + `Tuple(str)`: Paths to the files saved. + """ + if not os.path.isdir(save_directory): + logger.error(f"Vocabulary path ({save_directory}) should be a directory") + return + out_vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + + if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): + copyfile(self.vocab_file, out_vocab_file) + elif not os.path.isfile(self.vocab_file): + with open(out_vocab_file, "wb") as fi: + content_spiece_model = self.sp_model.serialized_model_proto() + fi.write(content_spiece_model) + + return (out_vocab_file,) + + def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): + 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]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + 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]: + """ + Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT + sequence pair mask has the following format: + + ``` + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence | + ``` + + if token_ids_1 is None, only returns the first portion of the mask (0s). + + Args: + token_ids_0 (`List[int]`): + List of ids. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). + """ + 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 = [0] * len(bos_token_id + token_ids_0 + eos_token_id) + + if token_ids_1 is not None: + output += [1] * len(bos_token_id + token_ids_1 + eos_token_id) + + return output diff --git a/LLM-Detector-V1-4w/checkpoint-2000/tokenizer.model b/LLM-Detector-V1-4w/checkpoint-2000/tokenizer.model new file mode 100644 index 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+library_name: peft +base_model: ../Baichuan2-7B-Chat +--- + +# Model Card for Model ID + + + + + +## Model Details + +### Model Description + + + + + +- **Developed by:** [More Information Needed] +- **Shared by [optional]:** [More Information Needed] +- **Model type:** [More Information Needed] +- **Language(s) (NLP):** [More Information Needed] +- **License:** [More Information Needed] +- **Finetuned from model [optional]:** [More Information Needed] + +### Model Sources [optional] + + + +- **Repository:** [More Information Needed] +- **Paper [optional]:** [More Information Needed] +- **Demo [optional]:** [More Information Needed] + +## Uses + + + +### Direct Use + + + +[More Information Needed] + +### Downstream Use [optional] + + + +[More Information Needed] + +### Out-of-Scope Use + + + +[More Information Needed] + +## Bias, Risks, and Limitations + + + +[More Information Needed] + +### Recommendations + + + +Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. + +## How to Get Started with the Model + +Use the code below to get started with the model. + +[More Information Needed] + +## Training Details + +### Training Data + + + +[More Information Needed] + +### Training Procedure + + + +#### Preprocessing [optional] + +[More Information Needed] + + +#### Training Hyperparameters + +- **Training regime:** [More Information Needed] + +#### Speeds, Sizes, Times [optional] + + + +[More Information Needed] + +## Evaluation + + + +### Testing Data, Factors & Metrics + +#### Testing Data + + + +[More Information Needed] + +#### Factors + + + +[More Information Needed] + +#### Metrics + + + +[More Information Needed] + +### Results + +[More Information Needed] + +#### Summary + + + +## Model Examination [optional] + + + +[More Information Needed] + +## Environmental Impact + + + +Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). + +- **Hardware Type:** [More Information Needed] +- **Hours used:** [More Information Needed] +- **Cloud Provider:** [More Information Needed] +- **Compute Region:** [More Information Needed] +- **Carbon Emitted:** [More Information Needed] + +## Technical Specifications [optional] + +### Model Architecture and Objective + +[More Information Needed] + +### Compute Infrastructure + +[More Information Needed] + +#### Hardware + +[More Information Needed] + +#### Software + +[More Information Needed] + +## Citation [optional] + + + +**BibTeX:** + +[More Information Needed] + +**APA:** + +[More Information Needed] + +## Glossary [optional] + + + +[More Information Needed] + +## More Information [optional] + +[More Information Needed] + +## Model Card Authors [optional] + +[More Information Needed] + +## Model Card Contact + +[More Information Needed] + + +## Training procedure + + +The following `bitsandbytes` quantization config was used during training: +- quant_method: QuantizationMethod.BITS_AND_BYTES +- load_in_8bit: False +- load_in_4bit: True +- llm_int8_threshold: 6.0 +- llm_int8_skip_modules: None +- llm_int8_enable_fp32_cpu_offload: False +- llm_int8_has_fp16_weight: False +- bnb_4bit_quant_type: nf4 +- bnb_4bit_use_double_quant: True +- bnb_4bit_compute_dtype: float16 + +### Framework versions + + +- PEFT 0.6.0 diff --git a/LLM-Detector-V1-4w/checkpoint-3000/adapter_config.json b/LLM-Detector-V1-4w/checkpoint-3000/adapter_config.json new file mode 100644 index 0000000000000000000000000000000000000000..54acbac2c279e46331f75c340af98595a8683d48 --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-3000/adapter_config.json @@ -0,0 +1,22 @@ +{ + "alpha_pattern": {}, + "auto_mapping": null, + "base_model_name_or_path": "../Baichuan2-7B-Chat", + "bias": "none", + "fan_in_fan_out": false, + "inference_mode": true, + "init_lora_weights": true, + "layers_pattern": null, + "layers_to_transform": null, + "lora_alpha": 32.0, + "lora_dropout": 0.1, + "modules_to_save": null, + "peft_type": "LORA", + "r": 8, + "rank_pattern": {}, + "revision": null, + "target_modules": [ + "W_pack" + ], + "task_type": "CAUSAL_LM" +} \ No newline at end of file diff --git a/LLM-Detector-V1-4w/checkpoint-3000/adapter_model.bin b/LLM-Detector-V1-4w/checkpoint-3000/adapter_model.bin new file mode 100644 index 0000000000000000000000000000000000000000..88c1068195e1badd2d1c588064a8825f786847ef --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-3000/adapter_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:95683a3f84e8898c5638dc27af4722d83e15011e94d2d5b3dc5e5df5fb5f2957 +size 16800430 diff --git a/LLM-Detector-V1-4w/checkpoint-3000/optimizer.pt b/LLM-Detector-V1-4w/checkpoint-3000/optimizer.pt new file mode 100644 index 0000000000000000000000000000000000000000..8184f12059703df68f525ee7ee2d707fdba3a06d --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-3000/optimizer.pt @@ -0,0 +1,3 @@ +version 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1064 diff --git a/LLM-Detector-V1-4w/checkpoint-3000/special_tokens_map.json b/LLM-Detector-V1-4w/checkpoint-3000/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..5819ea25d7b1b6340063a0629c2143c44b0452da --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-3000/special_tokens_map.json @@ -0,0 +1,30 @@ +{ + "bos_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "pad_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "unk_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/LLM-Detector-V1-4w/checkpoint-3000/tokenization_baichuan.py b/LLM-Detector-V1-4w/checkpoint-3000/tokenization_baichuan.py new file mode 100644 index 0000000000000000000000000000000000000000..256544b45542d2f5dcd12a65e2f0ddaeeb9def25 --- /dev/null +++ b/LLM-Detector-V1-4w/checkpoint-3000/tokenization_baichuan.py @@ -0,0 +1,251 @@ +# Copyright 2023 Baichuan Inc. All Rights Reserved. + +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from shutil import copyfile +from typing import Any, Dict, List, Optional, Tuple + +import sentencepiece as spm + +from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} + +PRETRAINED_VOCAB_FILES_MAP = { + "vocab_file": {}, + "tokenizer_file": {}, +} +PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {} + + +class BaichuanTokenizer(PreTrainedTokenizer): + """ + Construct a Baichuan tokenizer. Based on byte-level Byte-Pair-Encoding. + + Args: + vocab_file (`str`): + Path to the vocabulary file. + """ + + vocab_files_names = VOCAB_FILES_NAMES + pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP + max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES + model_input_names = ["input_ids", "attention_mask"] + + def __init__( + self, + vocab_file, + unk_token="", + bos_token="", + eos_token="", + pad_token=None, + sp_model_kwargs: Optional[Dict[str, Any]] = None, + add_bos_token=True, + add_eos_token=False, + clean_up_tokenization_spaces=False, + **kwargs, + ): + self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs + bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token + eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token + unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token + pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token + super().__init__( + bos_token=bos_token, + eos_token=eos_token, + unk_token=unk_token, + pad_token=pad_token, + add_bos_token=add_bos_token, + add_eos_token=add_eos_token, + sp_model_kwargs=self.sp_model_kwargs, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + self.vocab_file = vocab_file + self.add_bos_token = add_bos_token + self.add_eos_token = add_eos_token + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(vocab_file) + + def __getstate__(self): + state = self.__dict__.copy() + state["sp_model"] = None + return state + + def __setstate__(self, d): + self.__dict__ = d + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(self.vocab_file) + + @property + def vocab_size(self): + """Returns vocab size""" + return self.sp_model.get_piece_size() + + def get_vocab(self): + """Returns vocab as a dict""" + vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} + vocab.update(self.added_tokens_encoder) + return vocab + + def _tokenize(self, text): + """Returns a tokenized string.""" + return self.sp_model.encode(text, out_type=str) + + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + return self.sp_model.piece_to_id(token) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + token = self.sp_model.IdToPiece(index) + return token + + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (string) in a single string.""" + current_sub_tokens = [] + out_string = "" + prev_is_special = False + for i, token in enumerate(tokens): + # make sure that special tokens are not decoded using sentencepiece model + if token in self.all_special_tokens: + if not prev_is_special and i != 0: + out_string += " " + out_string += self.sp_model.decode(current_sub_tokens) + token + prev_is_special = True + current_sub_tokens = [] + else: + current_sub_tokens.append(token) + prev_is_special = False + out_string += self.sp_model.decode(current_sub_tokens) + return out_string + + def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]: + """ + Save the vocabulary and special tokens file to a directory. + + Args: + save_directory (`str`): + The directory in which to save the vocabulary. + + Returns: + `Tuple(str)`: Paths to the files saved. + """ + if not os.path.isdir(save_directory): + logger.error(f"Vocabulary path ({save_directory}) should be a directory") + return + out_vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + + if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): + copyfile(self.vocab_file, out_vocab_file) + elif not os.path.isfile(self.vocab_file): + with open(out_vocab_file, "wb") as fi: + content_spiece_model = self.sp_model.serialized_model_proto() + fi.write(content_spiece_model) + + return (out_vocab_file,) + + def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): + 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]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + 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]: + """ + Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT + sequence pair mask has the following format: + + ``` + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence | + ``` + + if token_ids_1 is None, only returns the first portion of the mask (0s). + + Args: + token_ids_0 (`List[int]`): + List of ids. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). + """ + 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 = [0] * len(bos_token_id + token_ids_0 + eos_token_id) + + if token_ids_1 is not None: + output += [1] * len(bos_token_id + token_ids_1 + eos_token_id) + + return output diff --git a/LLM-Detector-V1-4w/checkpoint-3000/tokenizer.model b/LLM-Detector-V1-4w/checkpoint-3000/tokenizer.model new file mode 100644 index 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+version https://git-lfs.github.com/spec/v1 +oid sha256:c31eb820fabf5021fa0eda935da3d201c65c7331d3ce4ce4ad4631151a6068e9 +size 4664 diff --git a/LLM-Detector-V1-4w/eval_results.json b/LLM-Detector-V1-4w/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..1cd58a4a1f8aa93461714bdbab830cf7e18af255 --- /dev/null +++ b/LLM-Detector-V1-4w/eval_results.json @@ -0,0 +1,7 @@ +{ + "epoch": 3.0, + "eval_loss": 0.014986271038651466, + "eval_runtime": 87.9616, + "eval_samples_per_second": 22.544, + "eval_steps_per_second": 2.819 +} \ No newline at end of file diff --git a/LLM-Detector-V1-4w/special_tokens_map.json b/LLM-Detector-V1-4w/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..5819ea25d7b1b6340063a0629c2143c44b0452da --- /dev/null +++ b/LLM-Detector-V1-4w/special_tokens_map.json @@ -0,0 +1,30 @@ +{ + "bos_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "pad_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "unk_token": { + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/LLM-Detector-V1-4w/tokenization_baichuan.py b/LLM-Detector-V1-4w/tokenization_baichuan.py new file mode 100644 index 0000000000000000000000000000000000000000..256544b45542d2f5dcd12a65e2f0ddaeeb9def25 --- /dev/null +++ b/LLM-Detector-V1-4w/tokenization_baichuan.py @@ -0,0 +1,251 @@ +# Copyright 2023 Baichuan Inc. All Rights Reserved. + +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from shutil import copyfile +from typing import Any, Dict, List, Optional, Tuple + +import sentencepiece as spm + +from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} + +PRETRAINED_VOCAB_FILES_MAP = { + "vocab_file": {}, + "tokenizer_file": {}, +} +PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {} + + +class BaichuanTokenizer(PreTrainedTokenizer): + """ + Construct a Baichuan tokenizer. Based on byte-level Byte-Pair-Encoding. + + Args: + vocab_file (`str`): + Path to the vocabulary file. + """ + + vocab_files_names = VOCAB_FILES_NAMES + pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP + max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES + model_input_names = ["input_ids", "attention_mask"] + + def __init__( + self, + vocab_file, + unk_token="", + bos_token="", + eos_token="", + pad_token=None, + sp_model_kwargs: Optional[Dict[str, Any]] = None, + add_bos_token=True, + add_eos_token=False, + clean_up_tokenization_spaces=False, + **kwargs, + ): + self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs + bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token + eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token + unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token + pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token + super().__init__( + bos_token=bos_token, + eos_token=eos_token, + unk_token=unk_token, + pad_token=pad_token, + add_bos_token=add_bos_token, + add_eos_token=add_eos_token, + sp_model_kwargs=self.sp_model_kwargs, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + self.vocab_file = vocab_file + self.add_bos_token = add_bos_token + self.add_eos_token = add_eos_token + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(vocab_file) + + def __getstate__(self): + state = self.__dict__.copy() + state["sp_model"] = None + return state + + def __setstate__(self, d): + self.__dict__ = d + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(self.vocab_file) + + @property + def vocab_size(self): + """Returns vocab size""" + return self.sp_model.get_piece_size() + + def get_vocab(self): + """Returns vocab as a dict""" + vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} + vocab.update(self.added_tokens_encoder) + return vocab + + def _tokenize(self, text): + """Returns a tokenized string.""" + return self.sp_model.encode(text, out_type=str) + + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + return self.sp_model.piece_to_id(token) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + token = self.sp_model.IdToPiece(index) + return token + + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (string) in a single string.""" + current_sub_tokens = [] + out_string = "" + prev_is_special = False + for i, token in enumerate(tokens): + # make sure that special tokens are not decoded using sentencepiece model + if token in self.all_special_tokens: + if not prev_is_special and i != 0: + out_string += " " + out_string += self.sp_model.decode(current_sub_tokens) + token + prev_is_special = True + current_sub_tokens = [] + else: + current_sub_tokens.append(token) + prev_is_special = False + out_string += self.sp_model.decode(current_sub_tokens) + return out_string + + def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]: + """ + Save the vocabulary and special tokens file to a directory. + + Args: + save_directory (`str`): + The directory in which to save the vocabulary. + + Returns: + `Tuple(str)`: Paths to the files saved. + """ + if not os.path.isdir(save_directory): + logger.error(f"Vocabulary path ({save_directory}) should be a directory") + return + out_vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + + if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): + copyfile(self.vocab_file, out_vocab_file) + elif not os.path.isfile(self.vocab_file): + with open(out_vocab_file, "wb") as fi: + content_spiece_model = self.sp_model.serialized_model_proto() + fi.write(content_spiece_model) + + return (out_vocab_file,) + + def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): + 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]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + 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]: + """ + Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT + sequence pair mask has the following format: + + ``` + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence | + ``` + + if token_ids_1 is None, only returns the first portion of the mask (0s). + + Args: + token_ids_0 (`List[int]`): + List of ids. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). + """ + 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 = [0] * len(bos_token_id + token_ids_0 + eos_token_id) + + if token_ids_1 is not None: + output += [1] * len(bos_token_id + token_ids_1 + eos_token_id) + + return output diff --git a/LLM-Detector-V1-4w/tokenizer.model b/LLM-Detector-V1-4w/tokenizer.model new file mode 100644 index 0000000000000000000000000000000000000000..b3902c4521d7f34868ac76dd16150ff5ca41b000 --- /dev/null +++ b/LLM-Detector-V1-4w/tokenizer.model @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:79452955be6b419a65984273a9f08af86042e1c2a75ee3ba989cbf620a133cc2 +size 2001107 diff --git a/LLM-Detector-V1-4w/tokenizer_config.json b/LLM-Detector-V1-4w/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..d14486d6a5be1135bdda779a8ffcde1b77155302 --- /dev/null +++ b/LLM-Detector-V1-4w/tokenizer_config.json @@ -0,0 +1,49 @@ +{ + "add_bos_token": false, + "add_eos_token": false, + "auto_map": { + "AutoTokenizer": [ + "tokenization_baichuan.BaichuanTokenizer", + null + ] + }, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "clean_up_tokenization_spaces": false, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": true + }, + "model_max_length": 4096, + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": true + }, + "padding_side": "right", + "sp_model_kwargs": {}, + "split_special_tokens": false, + "tokenizer_class": "BaichuanTokenizer", + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": true + }, + "use_fast": false +} diff --git a/LLM-Detector-V1-4w/train_results.json b/LLM-Detector-V1-4w/train_results.json new file mode 100644 index 0000000000000000000000000000000000000000..189495d4cc94b12504663e66226f82663fb61226 --- /dev/null +++ b/LLM-Detector-V1-4w/train_results.json @@ -0,0 +1,7 @@ +{ + "epoch": 3.0, + "train_loss": 0.06714861565509712, + "train_runtime": 17560.0547, + "train_samples_per_second": 6.434, + "train_steps_per_second": 0.201 +} \ No newline at end of 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