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---
license: gpl-3.0
---
# TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space
> [Shaolei Zhang](https://zhangshaolei1998.github.io/), [Tian Yu](https://tianyu0313.github.io/), [Yang Feng](https://people.ucas.edu.cn/~yangfeng?language=en)*
Model for paper "[TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space](https://arxiv.org/pdf/2402.17811.pdf)".
**TruthX** is an inference-time method to elicit the truthfulness of LLMs by editing their internal representations in truthful space, thereby mitigating the hallucinations of LLMs. On the [TruthfulQA benchmark](https://paperswithcode.com/sota/question-answering-on-truthfulqa), TruthX yields an average **enhancement of 20% in truthfulness** across 13 advanced LLMs.
<div align="center">
<img src="./truthx_results.png" alt="img" width="100%" />
</div>
<p align="center">
TruthfulQA MC1 accuracy of TruthX across 13 advanced LLMs
</p>
This repo provides **Llama-2-7B-Chat-TruthX**, a Llama-2-7B-Chat model with baked-in TruthX model. You can directly download this baked-in model and use it like standard Llama, no additional operations are required.
## Quick Starts
Inference with Llama-2-7B-Chat-TruthX:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
llama2chat_with_truthx = "ICTNLP/Llama-2-7b-chat-TruthX"
tokenizer = AutoTokenizer.from_pretrained(llama2chat_with_truthx, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(llama2chat_with_truthx, trust_remote_code=True,torch_dtype=torch.float16).cuda()
question = "What are the benefits of eating an apple a day?"
encoded_inputs = tokenizer(question, return_tensors="pt")["input_ids"]
outputs = model.generate(encoded_inputs.cuda())[0, encoded_inputs.shape[-1] :]
outputs_text = tokenizer.decode(outputs, skip_special_tokens=True).strip()
print(outputs_text)
```
Please refer to [GitHub repo](https://github.com/ictnlp/TruthX) and [our paper](https://arxiv.org/pdf/2402.17811.pdf) for more details.
## Licence
Model weights and the inference code are released under The GNU General Public License v3.0 (GPLv3)
## Citation
If this repository is useful for you, please cite as:
```
@misc{zhang2024truthx,
title={TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space},
author={Shaolei Zhang and Tian Yu and Yang Feng},
year={2024},
eprint={2402.17811},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2402.17811}
}
```
If you have any questions, feel free to contact `zhangshaolei20z@ict.ac.cn`.

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{
"_name_or_path": "ICTNLP/Llama-2-7b-chat-TruthX",
"architectures": [
"LlamaForCausalLM"
],
"auto_map": {
"AutoConfig": "configuration_llama.LlamaConfig",
"AutoModelForCausalLM": "modeling_llama.LlamaForCausalLM",
"AutoModel": "modeling_llama.LlamaModel"
},
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 11008,
"max_position_embeddings": 4096,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 32,
"pretraining_tp": 1,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.32.0.dev0",
"use_cache": true,
"vocab_size": 32000,
"truthx_config": {
"path":"truthx_model.pt"
}
}

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{}

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# coding=utf-8
# 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.
""" LLaMA model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class LlamaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the LLaMA-7B.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`LlamaModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
Llama 2 up to 4096, CodeLlama up to 16384.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
pretraining_tp (`int`, *optional*, defaults to 1):
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
issue](https://github.com/pytorch/pytorch/issues/76232).
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
these scaling strategies behave:
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
experimental feature, subject to breaking API changes in future versions.
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import LlamaModel, LlamaConfig
>>> # Initializing a LLaMA llama-7b style configuration
>>> configuration = LlamaConfig()
>>> # Initializing a model from the llama-7b style configuration
>>> model = LlamaModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "llama"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self._rope_scaling_validation()
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def _rope_scaling_validation(self):
"""
Validate the `rope_scaling` configuration.
"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scaling_factor = self.rope_scaling.get("factor", None)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
)
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

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{
"bos_token_id": 1,
"do_sample": true,
"eos_token_id": 2,
"max_length": 4096,
"pad_token_id": 0,
"temperature": 0.6,
"top_p": 0.9,
"transformers_version": "4.32.0.dev0"
}

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"model.layers.8.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
"model.layers.8.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
"model.layers.8.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
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"model.layers.8.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
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}
}

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{
"bos_token": {
"content": "<s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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# coding=utf-8
# 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.
"""Tokenization classes for LLaMA."""
import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...convert_slow_tokenizer import import_protobuf
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
},
"tokenizer_file": {
"hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"hf-internal-testing/llama-tokenizer": 2048,
}
SPIECE_UNDERLINE = "▁"
B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
# fmt: off
DEFAULT_SYSTEM_PROMPT = """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.
If 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."""
# fmt: on
class LlamaTokenizer(PreTrainedTokenizer):
"""
Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
no padding token in the original model.
Args:
vocab_file (`str`):
Path to the vocabulary file.
unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
The end of sequence token.
pad_token (`str` or `tokenizers.AddedToken`, *optional*):
A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
attention mechanisms or loss computation.
sp_model_kwargs (`Dict[str, Any]`, `Optional`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
add_bos_token (`bool`, *optional*, defaults to `True`):
Whether or not to add an `bos_token` at the start of sequences.
add_eos_token (`bool`, *optional*, defaults to `False`):
Whether or not to add an `eos_token` at the end of sequences.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
extra spaces.
use_default_system_prompt (`bool`, *optional*, defaults to `False`):
Whether or not the default system prompt for Llama should be used.
spaces_between_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to add spaces between special tokens.
legacy (`bool`, *optional*):
Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622
and #25224 which includes fixes to properly handle tokens that appear after special tokens. A simple
example:
- `legacy=True`:
```python
>>> from transformers import T5Tokenizer
>>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=True)
>>> tokenizer.encode("Hello <extra_id_0>.")
[8774, 32099, 3, 5, 1]
```
- `legacy=False`:
```python
>>> from transformers import T5Tokenizer
>>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=False)
>>> tokenizer.encode("Hello <extra_id_0>.") # the extra space `[3]` is no longer here
[8774, 32099, 5, 1]
```
Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details.
"""
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="<unk>",
bos_token="<s>",
eos_token="</s>",
pad_token=None,
sp_model_kwargs: Optional[Dict[str, Any]] = None,
add_bos_token=True,
add_eos_token=False,
clean_up_tokenization_spaces=False,
use_default_system_prompt=False,
spaces_between_special_tokens=False,
legacy=None,
**kwargs,
):
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
if legacy is None:
logger.warning_once(
f"You are using the default legacy behaviour of the {self.__class__}. This is"
" expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
" If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it"
" means, and thoroughly read the reason why this was added as explained in"
" https://github.com/huggingface/transformers/pull/24565"
)
legacy = True
self.legacy = legacy
self.vocab_file = vocab_file
self.add_bos_token = add_bos_token
self.add_eos_token = add_eos_token
self.use_default_system_prompt = use_default_system_prompt
self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False))
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,
use_default_system_prompt=use_default_system_prompt,
spaces_between_special_tokens=spaces_between_special_tokens,
legacy=legacy,
**kwargs,
)
@property
def unk_token_length(self):
return len(self.sp_model.encode(str(self.unk_token)))
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor
def get_spm_processor(self, from_slow=False):
tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
if self.legacy or from_slow: # no dependency on protobuf
tokenizer.Load(self.vocab_file)
return tokenizer
with open(self.vocab_file, "rb") as f:
sp_model = f.read()
model_pb2 = import_protobuf(f"The new behaviour of {self.__class__.__name__} (with `self.legacy = False`)")
model = model_pb2.ModelProto.FromString(sp_model)
normalizer_spec = model_pb2.NormalizerSpec()
normalizer_spec.add_dummy_prefix = False
model.normalizer_spec.MergeFrom(normalizer_spec)
sp_model = model.SerializeToString()
tokenizer.LoadFromSerializedProto(sp_model)
return tokenizer
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
state["sp_model_proto"] = self.sp_model.serialized_model_proto()
return state
def __setstate__(self, d):
self.__dict__ = d
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
@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
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize
def tokenize(self, text: "TextInput", add_special_tokens=False, **kwargs) -> List[str]:
"""
Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the
first token is special.
"""
if self.legacy or len(text) == 0:
return super().tokenize(text, **kwargs)
tokens = super().tokenize(SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, " "), **kwargs)
if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
tokens = tokens[1:]
return tokens
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize
def _tokenize(self, text, **kwargs):
"""
Returns a tokenized string.
We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
`['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the
`unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
`self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
"""
tokens = self.sp_model.encode(text, out_type=str)
if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")):
return tokens
# 1. Encode string + prefix ex: "<unk> Hey"
tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
# 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
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."""
# since we manually add the prefix space, we have to remove it when decoding
if tokens[0].startswith(SPIECE_UNDERLINE):
tokens[0] = tokens[0][1:]
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 and self.legacy:
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
@property
def default_chat_template(self):
"""
LLaMA uses [INST] and [/INST] to indicate user messages, and <<SYS>> and <</SYS>> to indicate system messages.
Assistant messages do not have special tokens, because LLaMA chat models are generally trained with strict
user/assistant/user/assistant message ordering, and so assistant messages can be identified from the ordering
rather than needing special tokens. The system message is partly 'embedded' in the first user message, which
results in an unusual token ordering when it is present. This template should definitely be changed if you wish
to fine-tune a model with more flexible role ordering!
The output should look something like:
<bos>[INST] B_SYS SystemPrompt E_SYS Prompt [/INST] Answer <eos><bos>[INST] Prompt [/INST] Answer <eos>
<bos>[INST] Prompt [/INST]
The reference for this chat template is [this code
snippet](https://github.com/facebookresearch/llama/blob/556949fdfb72da27c2f4a40b7f0e4cf0b8153a28/llama/generation.py#L320-L362)
in the original repository.
"""
logger.warning_once(
"\nNo chat template is defined for this tokenizer - using the default template "
f"for the {self.__class__.__name__} class. If the default is not appropriate for "
"your model, please set `tokenizer.chat_template` to an appropriate template. "
"See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n"
)
template = (
"{% if messages[0]['role'] == 'system' %}"
"{% set loop_messages = messages[1:] %}" # Extract system message if it's present
"{% set system_message = messages[0]['content'] %}"
"{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['content'] %}"
"{% set loop_messages = messages %}" # Or use the default system message if the flag is set
"{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}"
"{% else %}"
"{% set loop_messages = messages %}"
"{% set system_message = false %}"
"{% endif %}"
"{% for message in loop_messages %}" # Loop over all non-system messages
"{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}"
"{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}"
"{% endif %}"
"{% if loop.index0 == 0 and system_message != false %}" # Embed system message in first message
"{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}"
"{% else %}"
"{% set content = message['content'] %}"
"{% endif %}"
"{% if message['role'] == 'user' %}" # After all of that, handle messages/roles in a fairly normal way
"{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}"
"{% elif message['role'] == 'system' %}"
"{{ '<<SYS>>\\n' + content.strip() + '\\n<</SYS>>\\n\\n' }}"
"{% elif message['role'] == 'assistant' %}"
"{{ ' ' + content.strip() + ' ' + eos_token }}"
"{% endif %}"
"{% endfor %}"
)
template = template.replace("USE_DEFAULT_PROMPT", "true" if self.use_default_system_prompt else "false")
default_message = DEFAULT_SYSTEM_PROMPT.replace("\n", "\\n").replace("'", "\\'")
template = template.replace("DEFAULT_SYSTEM_MESSAGE", default_message)
return template

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version https://git-lfs.github.com/spec/v1
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
size 499723

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{
"add_bos_token": true,
"add_eos_token": false,
"bos_token": {
"__type": "AddedToken",
"content": "<s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"clean_up_tokenization_spaces": false,
"eos_token": {
"__type": "AddedToken",
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"legacy": false,
"model_max_length": 1000000000000000019884624838656,
"pad_token": null,
"padding_side": "right",
"sp_model_kwargs": {},
"tokenizer_class": "LlamaTokenizer",
"unk_token": {
"__type": "AddedToken",
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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import torch
from torch import nn
import torch.nn.functional as F
from abc import abstractmethod
from torch import tensor as Tensor
from typing import List, Any
class BaseVAE(nn.Module):
def __init__(self) -> None:
super(BaseVAE, self).__init__()
def encode(self, input: Tensor) -> List[Tensor]:
raise NotImplementedError
def decode(self, input: Tensor) -> Any:
raise NotImplementedError
def sample(self, batch_size: int, current_device: int, **kwargs) -> Tensor:
raise NotImplementedError
def generate(self, x: Tensor, **kwargs) -> Tensor:
raise NotImplementedError
@abstractmethod
def forward(self, *inputs: Tensor) -> Tensor:
pass
@abstractmethod
def loss_function(self, *inputs: Any, **kwargs) -> Tensor:
pass
class MLPAE(BaseVAE):
def __init__(
self,
in_channels: int,
semantic_latent_dim: int,
truthful_latent_dim: int,
semantic_hidden_dims: List = None,
truthful_hidden_dims: List = None,
decoder_hidden_dims: List = None,
**kwargs
) -> None:
super(MLPAE, self).__init__()
self.semantic_latent_dim = semantic_latent_dim
if semantic_hidden_dims is None:
semantic_hidden_dims = []
# Build Semantic Encoder
semantic_encoder_modules = []
flat_size = in_channels
for h_dim in semantic_hidden_dims:
semantic_encoder_modules.append(
nn.Sequential(
nn.Linear(flat_size, h_dim), nn.LayerNorm(h_dim), nn.LeakyReLU()
)
)
flat_size = h_dim
semantic_encoder_modules.append(
nn.Sequential(
nn.Linear(flat_size, semantic_latent_dim),
nn.LayerNorm(semantic_latent_dim),
nn.LeakyReLU(),
)
)
self.semantic_encoder = nn.Sequential(*semantic_encoder_modules)
if truthful_hidden_dims is None:
truthful_hidden_dims = []
# Build Truthful Encoder
truthful_encoder_modules = []
flat_size = in_channels
for h_dim in truthful_hidden_dims:
truthful_encoder_modules.append(
nn.Sequential(
(
nn.Linear(flat_size, h_dim)
if flat_size != h_dim
else nn.Identity()
),
nn.LayerNorm(h_dim),
nn.LeakyReLU(),
)
)
flat_size = h_dim
truthful_encoder_modules.append(
nn.Sequential(
(
nn.Linear(flat_size, truthful_latent_dim)
if flat_size != truthful_latent_dim
else nn.Identity()
),
nn.LayerNorm(truthful_latent_dim),
nn.LeakyReLU(),
)
)
self.truthful_encoder = nn.Sequential(*truthful_encoder_modules)
# Cross-Attention Module
self.num_heads = 1
self.cross_attention = nn.MultiheadAttention(
embed_dim=semantic_latent_dim, num_heads=self.num_heads
)
self.proj = None
if semantic_latent_dim != truthful_latent_dim:
self.proj = nn.Linear(truthful_latent_dim, semantic_latent_dim, bias=False)
# Build Decoder
decoder_modules = []
if len(decoder_hidden_dims) > 0:
flat_size = semantic_latent_dim
for h_dim in decoder_hidden_dims:
decoder_modules.append(
nn.Sequential(
nn.Linear(flat_size, h_dim), nn.LayerNorm(h_dim), nn.LeakyReLU()
)
)
flat_size = h_dim
flat_size = decoder_hidden_dims[-1]
self.decoder = nn.Sequential(*decoder_modules)
else:
self.decoder_input = None
self.decoder = None
flat_size = semantic_latent_dim
self.final_layer = nn.Sequential(nn.Linear(flat_size, in_channels))
def encode_semantic(self, input: Tensor) -> List[Tensor]:
semantic_latent_rep = self.semantic_encoder(input)
return semantic_latent_rep
def encode_truthful(self, input: Tensor) -> List[Tensor]:
truthful_latent_rep = self.truthful_encoder(input)
truthful_latent_rep = F.normalize(truthful_latent_rep, p=2, dim=-1)
return truthful_latent_rep
def attention(self, query: Tensor, key: Tensor, value: Tensor) -> Tensor:
if self.proj is not None and query.size(-1) != key.size(-1):
key = self.proj(key)
value = self.proj(value)
query = query.unsqueeze(0)
key = key.unsqueeze(0)
value = value.unsqueeze(0)
output, attention_weights = self.cross_attention(query, key, value)
return output[0]
def decode(self, z: Tensor) -> Tensor:
result = z
if self.decoder is not None:
result = self.decoder(result)
result = self.final_layer(result)
return result
def forward(
self, input: Tensor, truthful_latent_rep=None, **kwargs
) -> List[Tensor]:
semantic_latent_rep = self.encode_semantic(input)
if truthful_latent_rep is None:
truthful_latent_rep = self.encode_truthful(input)
truthful_latent_rep = truthful_latent_rep.reshape(
-1, truthful_latent_rep.size(-1)
)
z = semantic_latent_rep + self.attention(
semantic_latent_rep,
truthful_latent_rep.contiguous(),
truthful_latent_rep.contiguous(),
)
output = self.decode(z)
return [output, input, semantic_latent_rep, truthful_latent_rep]
def forward_decoder(self, input, semantic_latent_rep, truthful_latent_rep):
z = semantic_latent_rep + self.attention(
semantic_latent_rep, truthful_latent_rep, truthful_latent_rep
)
output = self.decode(z)
return [output, input, semantic_latent_rep, truthful_latent_rep]
def get_semantic_latent_rep(self, input: Tensor, **kwargs) -> List[Tensor]:
semantic_latent_rep = self.encode_semantic(input)
return semantic_latent_rep
def get_truthful_latent_rep(self, input: Tensor, **kwargs) -> List[Tensor]:
truthful_latent_rep = self.encode_truthful(input)
return truthful_latent_rep
def loss_function(self, *args, **kwargs) -> dict:
recons = args[0]
input = args[1]
recons_loss = F.mse_loss(recons, input)
loss = recons_loss
return {"loss": loss, "Reconstruction_Loss": recons_loss.detach()}
class TruthX:
def __init__(self, model_path, hidden_size, edit_strength=1.0, top_layers=10):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
checkpoint = torch.load(model_path)
args = checkpoint["args"]
semantic_latent_dim = args.semantic_latent_dim # Adjust as needed
truthful_latent_dim = args.truthful_latent_dim
semantic_hidden_dims = (
[int(_) for _ in args.semantic_hidden_dims.split(",")]
if args.semantic_hidden_dims != ""
else []
)
truthful_hidden_dims = (
[int(_) for _ in args.truthful_hidden_dims.split(",")]
if args.truthful_hidden_dims != ""
else []
)
decoder_hidden_dims = (
[int(_) for _ in args.decoder_hidden_dims.split(",")]
if args.decoder_hidden_dims != ""
else []
)
ae_model = MLPAE(
in_channels=hidden_size,
semantic_latent_dim=semantic_latent_dim,
truthful_latent_dim=truthful_latent_dim,
semantic_hidden_dims=semantic_hidden_dims,
truthful_hidden_dims=truthful_hidden_dims,
decoder_hidden_dims=decoder_hidden_dims,
).to(device)
ae_model.load_state_dict(checkpoint["state_dict"])
ae_model.pos_center = ((checkpoint["pos_center"])).to(device)
ae_model.neg_center = ((checkpoint["neg_center"])).to(device)
ae_model.eval()
ae_model.to(device)
self.ae_model = ae_model
self.rank = checkpoint["rank"]
self.top_layers = top_layers
self.edit_strength = edit_strength
self.cur_layer_id = 0
self.prompt_length = None
self.mc = False
@torch.inference_mode()
def edit(self, X):
layer_id = int(self.cur_layer_id.split(".")[0])
if self.cur_layer_id.endswith("attn"):
layer_id = 2 * layer_id
else:
layer_id = 2 * layer_id + 1
if self.rank[layer_id] > self.top_layers:
return X
bsz, s_len, d = X.size()
x = (
X.contiguous()
.view(-1, d)
.type_as(self.ae_model.semantic_encoder[0][0].weight)
)
x_truthful = self.ae_model.get_truthful_latent_rep(
X.type_as(self.ae_model.semantic_encoder[0][0].weight)
)
pos_center = self.ae_model.pos_center[layer_id].unsqueeze(0)
neg_center = self.ae_model.neg_center[layer_id].unsqueeze(0)
delta = (pos_center - neg_center).unsqueeze(0)
recon_x_pos = (
self.ae_model(
x,
truthful_latent_rep=F.normalize(
x_truthful + delta, p=2, dim=-1
).type_as(x),
)[0]
.contiguous()
.view(bsz, s_len, d)
)
recon_x_neg = (
self.ae_model(
x,
truthful_latent_rep=F.normalize(
x_truthful - delta, p=2, dim=-1
).type_as(x),
)[0]
.contiguous()
.view(bsz, s_len, d)
)
Delta = recon_x_pos - recon_x_neg
Delta = Delta.contiguous().to(X.dtype)
Delta = F.normalize(Delta, p=2, dim=-1).type_as(X) * torch.norm(
X, p=2, dim=-1
).unsqueeze(2)
mask = torch.ones((bsz, s_len), device=Delta.device)
if self.mc:
# multiple-choice, only edit the tokens in answer
mask[:, : self.prompt_length + 1] = 0
# probing those untruthful position
probing = (
torch.nn.functional.cosine_similarity(
x_truthful, neg_center.unsqueeze(1), dim=-1
)
- torch.nn.functional.cosine_similarity(
x_truthful, pos_center.unsqueeze(1), dim=-1
)
).clamp(0, 999)
mask = mask * probing
else:
# open-ended generation, only edit the generated token (i.e., last token)
mask[:, :-1] = 0
mask[:, -1:] = 1
new_X = X + (Delta.type_as(X)) * self.edit_strength * mask.unsqueeze(2).type_as(X)
return new_X

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