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README.md CHANGED
@@ -4,7 +4,7 @@ license: cc-by-4.0
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5
  # Seeing Clearly, Answering Incorrectly: A Multimodal Robustness Benchmark for Evaluating MLLMs on Leading Questions
6
 
7
- ๐Ÿ“– [**Paper**](https://arxiv.org/abs/2402.11530) | ๐Ÿ  [**Code**](https://github.com/BAAI-DCAI/Multimodal-Robustness-Benchmark) | ๐Ÿ“– [**Data**](https://huggingface.co/datasets/BAAI/Multimodal-Robustness-Benchmark)
8
 
9
 
10
  ## Overview
@@ -20,7 +20,7 @@ MMR provides a comprehensive suite to evaluate the understanding capabilities of
20
 
21
  3. **Combined Dataset and Models:**
22
  - The new dataset merges the proposed dataset with existing ones.
23
- - Trained models include Bunny-MMR-3B, Bunny-MMR-4B, and Bunny-MMR-8B.
24
 
25
  In this repository, we provide Bunny-MMR-8B, which is built upon [SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) and [Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). More details about this model can be found in [GitHub](https://github.com/BAAI-DCAI/Multimodal-Robustness-Benchmark).
26
 
@@ -93,14 +93,14 @@ print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True
93
  If you find this repository helpful, please cite the paper below.
94
 
95
  ```bibtex
96
- @article{he2024bunny,
97
- title={Efficient Multimodal Learning from Data-centric Perspective},
98
- author={He, Muyang and Liu, Yexin and Wu, Boya and Yuan, Jianhao and Wang, Yueze and Huang, Tiejun and Zhao, Bo},
99
- journal={arXiv preprint arXiv:2402.11530},
100
- year={2024}
 
101
  }
102
  ```
103
 
104
  ## License
105
- This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses.
106
- The content of this project itself is licensed under the [cc-by-4.0](./LICENSE).
 
4
 
5
  # Seeing Clearly, Answering Incorrectly: A Multimodal Robustness Benchmark for Evaluating MLLMs on Leading Questions
6
 
7
+ ๐Ÿ“– [**Paper**](https://arxiv.org/abs/2406.10638) | ๐Ÿ  [**Code**](https://github.com/BAAI-DCAI/Multimodal-Robustness-Benchmark) | ๐Ÿ“– [**Data**](https://huggingface.co/datasets/BAAI/Multimodal-Robustness-Benchmark)
8
 
9
 
10
  ## Overview
 
20
 
21
  3. **Combined Dataset and Models:**
22
  - The new dataset merges the proposed dataset with existing ones.
23
+ - Trained models include [Bunny-MMR-3B](https://huggingface.co/AI4VR/Bunny-MMR-3B), [Bunny-MMR-4B](https://huggingface.co/AI4VR/Bunny-MMR-4B), and [Bunny-MMR-8B](https://huggingface.co/AI4VR/Bunny-MMR-8B).
24
 
25
  In this repository, we provide Bunny-MMR-8B, which is built upon [SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) and [Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). More details about this model can be found in [GitHub](https://github.com/BAAI-DCAI/Multimodal-Robustness-Benchmark).
26
 
 
93
  If you find this repository helpful, please cite the paper below.
94
 
95
  ```bibtex
96
+ @misc{liu2024seeing,
97
+ title={Seeing Clearly, Answering Incorrectly: A Multimodal Robustness Benchmark for Evaluating MLLMs on Leading Questions},
98
+ author={Yexin Liu and Zhengyang Liang and Yueze Wang and Muyang He and Jian Li and Bo Zhao},
99
+ year={2024},
100
+ eprint={2406.10638},
101
+ archivePrefix={arXiv},
102
  }
103
  ```
104
 
105
  ## License
106
+ The project employs specific datasets and checkpoints that are governed by their original licenses. Users must adhere to all terms and conditions outlined in these licenses. The checkpoints are restricted to uses that comply with the license agreements of Bunny, LLaMA 3, Phi-2, Phi-3, and GPT-4. The dataset is provided under the CC-BY-4.0 license.
 
config.json ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "AI4VR/Bunny-MMR-8B",
3
+ "architectures": [
4
+ "BunnyLlamaForCausalLM"
5
+ ],
6
+ "auto_map": {
7
+ "AutoConfig": "configuration_bunny_llama.BunnyLlamaConfig",
8
+ "AutoModelForCausalLM": "modeling_bunny_llama.BunnyLlamaForCausalLM"
9
+ },
10
+ "attention_bias": false,
11
+ "attention_dropout": 0.0,
12
+ "bos_token_id": 128000,
13
+ "eos_token_id": 128001,
14
+ "freeze_mm_mlp_adapter": false,
15
+ "hidden_act": "silu",
16
+ "hidden_size": 4096,
17
+ "image_aspect_ratio": "pad",
18
+ "initializer_range": 0.02,
19
+ "intermediate_size": 14336,
20
+ "max_position_embeddings": 8192,
21
+ "mm_hidden_size": 1152,
22
+ "mm_projector_lr": 1e-05,
23
+ "mm_projector_type": "mlp2x_gelu",
24
+ "mm_vision_tower": "google/siglip-so400m-patch14-384",
25
+ "model_type": "bunny-llama",
26
+ "num_attention_heads": 32,
27
+ "num_hidden_layers": 32,
28
+ "num_key_value_heads": 8,
29
+ "pretraining_tp": 1,
30
+ "rms_norm_eps": 1e-05,
31
+ "rope_scaling": null,
32
+ "rope_theta": 500000.0,
33
+ "tie_word_embeddings": false,
34
+ "tokenizer_model_max_length": 4096,
35
+ "tokenizer_padding_side": "right",
36
+ "torch_dtype": "float16",
37
+ "transformers_version": "4.39.1",
38
+ "tune_mm_mlp_adapter": false,
39
+ "unfreeze_vision_tower": false,
40
+ "use_cache": true,
41
+ "use_mm_proj": true,
42
+ "use_s2": false,
43
+ "vocab_size": 128256
44
+ }
configuration_bunny_llama.py ADDED
@@ -0,0 +1,250 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+ # and OPT implementations in this library. It has been modified from its
6
+ # original forms to accommodate minor architectural differences compared
7
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+ #
9
+ # Licensed under the Apache License, Version 2.0 (the "License");
10
+ # you may not use this file except in compliance with the License.
11
+ # You may obtain a copy of the License at
12
+ #
13
+ # http://www.apache.org/licenses/LICENSE-2.0
14
+ #
15
+ # Unless required by applicable law or agreed to in writing, software
16
+ # distributed under the License is distributed on an "AS IS" BASIS,
17
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+ """ LLaMA model configuration"""
21
+
22
+ from transformers.configuration_utils import PretrainedConfig
23
+ from transformers.utils import logging
24
+
25
+ logger = logging.get_logger(__name__)
26
+
27
+
28
+ # from ..deprecated._archive_maps import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
29
+
30
+
31
+ class LlamaConfig(PretrainedConfig):
32
+ r"""
33
+ This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
34
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
35
+ defaults will yield a similar configuration to that of the LLaMA-7B.
36
+
37
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
38
+ documentation from [`PretrainedConfig`] for more information.
39
+
40
+
41
+ Args:
42
+ vocab_size (`int`, *optional*, defaults to 32000):
43
+ Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
44
+ `inputs_ids` passed when calling [`LlamaModel`]
45
+ hidden_size (`int`, *optional*, defaults to 4096):
46
+ Dimension of the hidden representations.
47
+ intermediate_size (`int`, *optional*, defaults to 11008):
48
+ Dimension of the MLP representations.
49
+ num_hidden_layers (`int`, *optional*, defaults to 32):
50
+ Number of hidden layers in the Transformer decoder.
51
+ num_attention_heads (`int`, *optional*, defaults to 32):
52
+ Number of attention heads for each attention layer in the Transformer decoder.
53
+ num_key_value_heads (`int`, *optional*):
54
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
55
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
56
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
57
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
58
+ by meanpooling all the original heads within that group. For more details checkout [this
59
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
60
+ `num_attention_heads`.
61
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
62
+ The non-linear activation function (function or string) in the decoder.
63
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
64
+ The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
65
+ Llama 2 up to 4096, CodeLlama up to 16384.
66
+ initializer_range (`float`, *optional*, defaults to 0.02):
67
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
68
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
69
+ The epsilon used by the rms normalization layers.
70
+ use_cache (`bool`, *optional*, defaults to `True`):
71
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
72
+ relevant if `config.is_decoder=True`.
73
+ pad_token_id (`int`, *optional*):
74
+ Padding token id.
75
+ bos_token_id (`int`, *optional*, defaults to 1):
76
+ Beginning of stream token id.
77
+ eos_token_id (`int`, *optional*, defaults to 2):
78
+ End of stream token id.
79
+ pretraining_tp (`int`, *optional*, defaults to 1):
80
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
81
+ document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
82
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
83
+ issue](https://github.com/pytorch/pytorch/issues/76232).
84
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
85
+ Whether to tie weight embeddings
86
+ rope_theta (`float`, *optional*, defaults to 10000.0):
87
+ The base period of the RoPE embeddings.
88
+ rope_scaling (`Dict`, *optional*):
89
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
90
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
91
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
92
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
93
+ these scaling strategies behave:
94
+ https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
95
+ experimental feature, subject to breaking API changes in future versions.
96
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
97
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
98
+ attention_dropout (`float`, *optional*, defaults to 0.0):
99
+ The dropout ratio for the attention probabilities.
100
+
101
+ ```python
102
+ >>> from transformers import LlamaModel, LlamaConfig
103
+
104
+ >>> # Initializing a LLaMA llama-7b style configuration
105
+ >>> configuration = LlamaConfig()
106
+
107
+ >>> # Initializing a model from the llama-7b style configuration
108
+ >>> model = LlamaModel(configuration)
109
+
110
+ >>> # Accessing the model configuration
111
+ >>> configuration = model.config
112
+ ```"""
113
+
114
+ model_type = "llama"
115
+ keys_to_ignore_at_inference = ["past_key_values"]
116
+
117
+ def __init__(
118
+ self,
119
+ vocab_size=32000,
120
+ hidden_size=4096,
121
+ intermediate_size=11008,
122
+ num_hidden_layers=32,
123
+ num_attention_heads=32,
124
+ num_key_value_heads=None,
125
+ hidden_act="silu",
126
+ max_position_embeddings=2048,
127
+ initializer_range=0.02,
128
+ rms_norm_eps=1e-6,
129
+ use_cache=True,
130
+ pad_token_id=None,
131
+ bos_token_id=1,
132
+ eos_token_id=2,
133
+ pretraining_tp=1,
134
+ tie_word_embeddings=False,
135
+ rope_theta=10000.0,
136
+ rope_scaling=None,
137
+ attention_bias=False,
138
+ attention_dropout=0.0,
139
+ **kwargs,
140
+ ):
141
+ self.vocab_size = vocab_size
142
+ self.max_position_embeddings = max_position_embeddings
143
+ self.hidden_size = hidden_size
144
+ self.intermediate_size = intermediate_size
145
+ self.num_hidden_layers = num_hidden_layers
146
+ self.num_attention_heads = num_attention_heads
147
+
148
+ # for backward compatibility
149
+ if num_key_value_heads is None:
150
+ num_key_value_heads = num_attention_heads
151
+
152
+ self.num_key_value_heads = num_key_value_heads
153
+ self.hidden_act = hidden_act
154
+ self.initializer_range = initializer_range
155
+ self.rms_norm_eps = rms_norm_eps
156
+ self.pretraining_tp = pretraining_tp
157
+ self.use_cache = use_cache
158
+ self.rope_theta = rope_theta
159
+ self.rope_scaling = rope_scaling
160
+ self._rope_scaling_validation()
161
+ self.attention_bias = attention_bias
162
+ self.attention_dropout = attention_dropout
163
+
164
+ super().__init__(
165
+ pad_token_id=pad_token_id,
166
+ bos_token_id=bos_token_id,
167
+ eos_token_id=eos_token_id,
168
+ tie_word_embeddings=tie_word_embeddings,
169
+ **kwargs,
170
+ )
171
+
172
+ def _rope_scaling_validation(self):
173
+ """
174
+ Validate the `rope_scaling` configuration.
175
+ """
176
+ if self.rope_scaling is None:
177
+ return
178
+
179
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
180
+ raise ValueError(
181
+ "`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"
182
+ )
183
+ rope_scaling_type = self.rope_scaling.get("type", None)
184
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
185
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
186
+ raise ValueError(
187
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
188
+ )
189
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
190
+ raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
191
+
192
+
193
+ from typing import Union
194
+ from transformers import PretrainedConfig
195
+ import os
196
+
197
+
198
+ class SigLipVisionConfig(PretrainedConfig):
199
+ model_type = "siglip_vision_model"
200
+
201
+ def __init__(
202
+ self,
203
+ hidden_size=1152,
204
+ image_mean=(0.5, 0.5, 0.5),
205
+ intermediate_size=4304,
206
+ num_hidden_layers=27,
207
+ num_attention_heads=16,
208
+ num_channels=3,
209
+ image_size=384,
210
+ patch_size=14,
211
+ hidden_act="gelu_pytorch_tanh",
212
+ layer_norm_eps=1e-6,
213
+ attention_dropout=0.0,
214
+ **kwargs,
215
+ ):
216
+ super().__init__(**kwargs)
217
+
218
+ self.hidden_size = hidden_size
219
+ self.intermediate_size = intermediate_size
220
+ self.num_hidden_layers = num_hidden_layers
221
+ self.num_attention_heads = num_attention_heads
222
+ self.num_channels = num_channels
223
+ self.patch_size = patch_size
224
+ self.image_size = image_size
225
+ self.attention_dropout = attention_dropout
226
+ self.layer_norm_eps = layer_norm_eps
227
+ self.hidden_act = hidden_act
228
+ self.image_mean = image_mean
229
+
230
+ @classmethod
231
+ def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
232
+ cls._set_token_in_kwargs(kwargs)
233
+
234
+ config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
235
+
236
+ # get the vision config dict if we are loading from SigLipConfig
237
+ if config_dict.get("model_type") == "siglip":
238
+ config_dict = config_dict["vision_config"]
239
+
240
+ if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
241
+ logger.warning(
242
+ f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
243
+ f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
244
+ )
245
+
246
+ return cls.from_dict(config_dict, **kwargs)
247
+
248
+
249
+ class BunnyLlamaConfig(LlamaConfig):
250
+ model_type = "bunny-llama"
generation_config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token_id": 128000,
3
+ "do_sample": true,
4
+ "eos_token_id": 128001,
5
+ "max_length": 4096,
6
+ "pad_token_id": 128001,
7
+ "temperature": 0.6,
8
+ "top_p": 0.9,
9
+ "transformers_version": "4.39.1"
10
+ }
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1
+ {
2
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