Upload folder using huggingface_hub
Browse files- llm/config.json +33 -0
- llm/generation_config.json +10 -0
- llm/pytorch_model-00001-of-00014.bin +3 -0
- llm/pytorch_model-00002-of-00014.bin +3 -0
- llm/pytorch_model-00003-of-00014.bin +3 -0
- llm/pytorch_model-00004-of-00014.bin +3 -0
- llm/pytorch_model-00005-of-00014.bin +3 -0
- llm/pytorch_model-00006-of-00014.bin +3 -0
- llm/pytorch_model-00007-of-00014.bin +3 -0
- llm/pytorch_model-00008-of-00014.bin +3 -0
- llm/pytorch_model-00009-of-00014.bin +3 -0
- llm/pytorch_model-00010-of-00014.bin +3 -0
- llm/pytorch_model-00011-of-00014.bin +3 -0
- llm/pytorch_model-00012-of-00014.bin +3 -0
- llm/pytorch_model-00013-of-00014.bin +3 -0
- llm/pytorch_model-00014-of-00014.bin +3 -0
- llm/pytorch_model.bin.index.json +370 -0
- llm/special_tokens_map.json +30 -0
- llm/tokenizer.json +0 -0
- llm/tokenizer.model +3 -0
- llm/tokenizer_config.json +42 -0
- llm_adapter/README.md +204 -0
- llm_adapter/adapter_config.json +31 -0
- llm_adapter/adapter_model.safetensors +3 -0
- projector/config.json +17 -0
- projector/configuration_projector.py +23 -0
- projector/model.safetensors +3 -0
- projector/modeling_projector.py +41 -0
- visual_encoder_adapter/README.md +204 -0
- visual_encoder_adapter/adapter_config.json +33 -0
- visual_encoder_adapter/adapter_model.safetensors +3 -0
- vit/config.json +22 -0
- vit/preprocessor_config.json +27 -0
- vit/pytorch_model-00001-of-00002.bin +3 -0
- vit/pytorch_model-00002-of-00002.bin +3 -0
- vit/pytorch_model.bin.index.json +526 -0
- xtuner_config.py +255 -0
llm/config.json
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{
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"_name_or_path": "lmsys/vicuna-13b-v1.5-16k",
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"architectures": [
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"LlamaForCausalLM"
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],
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"type": "linear"
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llm/generation_config.json
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llm/pytorch_model-00001-of-00014.bin
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llm/pytorch_model-00011-of-00014.bin
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llm/tokenizer.json
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3 |
+
size 499723
|
llm/tokenizer_config.json
ADDED
@@ -0,0 +1,42 @@
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1 |
+
{
|
2 |
+
"add_bos_token": true,
|
3 |
+
"add_eos_token": false,
|
4 |
+
"added_tokens_decoder": {
|
5 |
+
"0": {
|
6 |
+
"content": "<unk>",
|
7 |
+
"lstrip": false,
|
8 |
+
"normalized": false,
|
9 |
+
"rstrip": false,
|
10 |
+
"single_word": false,
|
11 |
+
"special": true
|
12 |
+
},
|
13 |
+
"1": {
|
14 |
+
"content": "<s>",
|
15 |
+
"lstrip": false,
|
16 |
+
"normalized": false,
|
17 |
+
"rstrip": false,
|
18 |
+
"single_word": false,
|
19 |
+
"special": true
|
20 |
+
},
|
21 |
+
"2": {
|
22 |
+
"content": "</s>",
|
23 |
+
"lstrip": false,
|
24 |
+
"normalized": false,
|
25 |
+
"rstrip": false,
|
26 |
+
"single_word": false,
|
27 |
+
"special": true
|
28 |
+
}
|
29 |
+
},
|
30 |
+
"bos_token": "<s>",
|
31 |
+
"clean_up_tokenization_spaces": false,
|
32 |
+
"eos_token": "</s>",
|
33 |
+
"legacy": false,
|
34 |
+
"model_max_length": 16384,
|
35 |
+
"pad_token": "<unk>",
|
36 |
+
"padding_side": "right",
|
37 |
+
"sp_model_kwargs": {},
|
38 |
+
"spaces_between_special_tokens": false,
|
39 |
+
"tokenizer_class": "LlamaTokenizer",
|
40 |
+
"unk_token": "<unk>",
|
41 |
+
"use_default_system_prompt": false
|
42 |
+
}
|
llm_adapter/README.md
ADDED
@@ -0,0 +1,204 @@
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|
1 |
+
---
|
2 |
+
library_name: peft
|
3 |
+
base_model: lmsys/vicuna-13b-v1.5-16k
|
4 |
+
---
|
5 |
+
|
6 |
+
# Model Card for Model ID
|
7 |
+
|
8 |
+
<!-- Provide a quick summary of what the model is/does. -->
|
9 |
+
|
10 |
+
|
11 |
+
|
12 |
+
## Model Details
|
13 |
+
|
14 |
+
### Model Description
|
15 |
+
|
16 |
+
<!-- Provide a longer summary of what this model is. -->
|
17 |
+
|
18 |
+
|
19 |
+
|
20 |
+
- **Developed by:** [More Information Needed]
|
21 |
+
- **Funded by [optional]:** [More Information Needed]
|
22 |
+
- **Shared by [optional]:** [More Information Needed]
|
23 |
+
- **Model type:** [More Information Needed]
|
24 |
+
- **Language(s) (NLP):** [More Information Needed]
|
25 |
+
- **License:** [More Information Needed]
|
26 |
+
- **Finetuned from model [optional]:** [More Information Needed]
|
27 |
+
|
28 |
+
### Model Sources [optional]
|
29 |
+
|
30 |
+
<!-- Provide the basic links for the model. -->
|
31 |
+
|
32 |
+
- **Repository:** [More Information Needed]
|
33 |
+
- **Paper [optional]:** [More Information Needed]
|
34 |
+
- **Demo [optional]:** [More Information Needed]
|
35 |
+
|
36 |
+
## Uses
|
37 |
+
|
38 |
+
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
39 |
+
|
40 |
+
### Direct Use
|
41 |
+
|
42 |
+
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
43 |
+
|
44 |
+
[More Information Needed]
|
45 |
+
|
46 |
+
### Downstream Use [optional]
|
47 |
+
|
48 |
+
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
49 |
+
|
50 |
+
[More Information Needed]
|
51 |
+
|
52 |
+
### Out-of-Scope Use
|
53 |
+
|
54 |
+
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
55 |
+
|
56 |
+
[More Information Needed]
|
57 |
+
|
58 |
+
## Bias, Risks, and Limitations
|
59 |
+
|
60 |
+
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
61 |
+
|
62 |
+
[More Information Needed]
|
63 |
+
|
64 |
+
### Recommendations
|
65 |
+
|
66 |
+
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
67 |
+
|
68 |
+
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
|
69 |
+
|
70 |
+
## How to Get Started with the Model
|
71 |
+
|
72 |
+
Use the code below to get started with the model.
|
73 |
+
|
74 |
+
[More Information Needed]
|
75 |
+
|
76 |
+
## Training Details
|
77 |
+
|
78 |
+
### Training Data
|
79 |
+
|
80 |
+
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
|
81 |
+
|
82 |
+
[More Information Needed]
|
83 |
+
|
84 |
+
### Training Procedure
|
85 |
+
|
86 |
+
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
87 |
+
|
88 |
+
#### Preprocessing [optional]
|
89 |
+
|
90 |
+
[More Information Needed]
|
91 |
+
|
92 |
+
|
93 |
+
#### Training Hyperparameters
|
94 |
+
|
95 |
+
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
96 |
+
|
97 |
+
#### Speeds, Sizes, Times [optional]
|
98 |
+
|
99 |
+
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
100 |
+
|
101 |
+
[More Information Needed]
|
102 |
+
|
103 |
+
## Evaluation
|
104 |
+
|
105 |
+
<!-- This section describes the evaluation protocols and provides the results. -->
|
106 |
+
|
107 |
+
### Testing Data, Factors & Metrics
|
108 |
+
|
109 |
+
#### Testing Data
|
110 |
+
|
111 |
+
<!-- This should link to a Dataset Card if possible. -->
|
112 |
+
|
113 |
+
[More Information Needed]
|
114 |
+
|
115 |
+
#### Factors
|
116 |
+
|
117 |
+
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
118 |
+
|
119 |
+
[More Information Needed]
|
120 |
+
|
121 |
+
#### Metrics
|
122 |
+
|
123 |
+
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
124 |
+
|
125 |
+
[More Information Needed]
|
126 |
+
|
127 |
+
### Results
|
128 |
+
|
129 |
+
[More Information Needed]
|
130 |
+
|
131 |
+
#### Summary
|
132 |
+
|
133 |
+
|
134 |
+
|
135 |
+
## Model Examination [optional]
|
136 |
+
|
137 |
+
<!-- Relevant interpretability work for the model goes here -->
|
138 |
+
|
139 |
+
[More Information Needed]
|
140 |
+
|
141 |
+
## Environmental Impact
|
142 |
+
|
143 |
+
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
144 |
+
|
145 |
+
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).
|
146 |
+
|
147 |
+
- **Hardware Type:** [More Information Needed]
|
148 |
+
- **Hours used:** [More Information Needed]
|
149 |
+
- **Cloud Provider:** [More Information Needed]
|
150 |
+
- **Compute Region:** [More Information Needed]
|
151 |
+
- **Carbon Emitted:** [More Information Needed]
|
152 |
+
|
153 |
+
## Technical Specifications [optional]
|
154 |
+
|
155 |
+
### Model Architecture and Objective
|
156 |
+
|
157 |
+
[More Information Needed]
|
158 |
+
|
159 |
+
### Compute Infrastructure
|
160 |
+
|
161 |
+
[More Information Needed]
|
162 |
+
|
163 |
+
#### Hardware
|
164 |
+
|
165 |
+
[More Information Needed]
|
166 |
+
|
167 |
+
#### Software
|
168 |
+
|
169 |
+
[More Information Needed]
|
170 |
+
|
171 |
+
## Citation [optional]
|
172 |
+
|
173 |
+
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
174 |
+
|
175 |
+
**BibTeX:**
|
176 |
+
|
177 |
+
[More Information Needed]
|
178 |
+
|
179 |
+
**APA:**
|
180 |
+
|
181 |
+
[More Information Needed]
|
182 |
+
|
183 |
+
## Glossary [optional]
|
184 |
+
|
185 |
+
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
186 |
+
|
187 |
+
[More Information Needed]
|
188 |
+
|
189 |
+
## More Information [optional]
|
190 |
+
|
191 |
+
[More Information Needed]
|
192 |
+
|
193 |
+
## Model Card Authors [optional]
|
194 |
+
|
195 |
+
[More Information Needed]
|
196 |
+
|
197 |
+
## Model Card Contact
|
198 |
+
|
199 |
+
[More Information Needed]
|
200 |
+
|
201 |
+
|
202 |
+
### Framework versions
|
203 |
+
|
204 |
+
- PEFT 0.7.1
|
llm_adapter/adapter_config.json
ADDED
@@ -0,0 +1,31 @@
|
|
|
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|
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|
|
|
|
|
1 |
+
{
|
2 |
+
"alpha_pattern": {},
|
3 |
+
"auto_mapping": null,
|
4 |
+
"base_model_name_or_path": "lmsys/vicuna-13b-v1.5-16k",
|
5 |
+
"bias": "none",
|
6 |
+
"fan_in_fan_out": false,
|
7 |
+
"inference_mode": true,
|
8 |
+
"init_lora_weights": true,
|
9 |
+
"layers_pattern": null,
|
10 |
+
"layers_to_transform": null,
|
11 |
+
"loftq_config": {},
|
12 |
+
"lora_alpha": 256,
|
13 |
+
"lora_dropout": 0.05,
|
14 |
+
"megatron_config": null,
|
15 |
+
"megatron_core": "megatron.core",
|
16 |
+
"modules_to_save": null,
|
17 |
+
"peft_type": "LORA",
|
18 |
+
"r": 512,
|
19 |
+
"rank_pattern": {},
|
20 |
+
"revision": null,
|
21 |
+
"target_modules": [
|
22 |
+
"v_proj",
|
23 |
+
"up_proj",
|
24 |
+
"o_proj",
|
25 |
+
"k_proj",
|
26 |
+
"q_proj",
|
27 |
+
"down_proj",
|
28 |
+
"gate_proj"
|
29 |
+
],
|
30 |
+
"task_type": "CAUSAL_LM"
|
31 |
+
}
|
llm_adapter/adapter_model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a17317836e0db05dfee44bad2c4f2890207421c2b7151a695afd077ed05cc567
|
3 |
+
size 4005637552
|
projector/config.json
ADDED
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"ProjectorModel"
|
4 |
+
],
|
5 |
+
"auto_map": {
|
6 |
+
"AutoConfig": "configuration_projector.ProjectorConfig",
|
7 |
+
"AutoModel": "modeling_projector.ProjectorModel"
|
8 |
+
},
|
9 |
+
"bias": true,
|
10 |
+
"depth": 2,
|
11 |
+
"hidden_act": "gelu",
|
12 |
+
"llm_hidden_size": 5120,
|
13 |
+
"model_type": "projector",
|
14 |
+
"torch_dtype": "float32",
|
15 |
+
"transformers_version": "4.37.2",
|
16 |
+
"visual_hidden_size": 1280
|
17 |
+
}
|
projector/configuration_projector.py
ADDED
@@ -0,0 +1,23 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright (c) OpenMMLab. All rights reserved.
|
2 |
+
from transformers import PretrainedConfig
|
3 |
+
|
4 |
+
|
5 |
+
class ProjectorConfig(PretrainedConfig):
|
6 |
+
model_type = "projector"
|
7 |
+
_auto_class = "AutoConfig"
|
8 |
+
|
9 |
+
def __init__(
|
10 |
+
self,
|
11 |
+
visual_hidden_size=4096,
|
12 |
+
llm_hidden_size=4096,
|
13 |
+
depth=2,
|
14 |
+
hidden_act="gelu",
|
15 |
+
bias=True,
|
16 |
+
**kwargs,
|
17 |
+
):
|
18 |
+
self.visual_hidden_size = visual_hidden_size
|
19 |
+
self.llm_hidden_size = llm_hidden_size
|
20 |
+
self.depth = depth
|
21 |
+
self.hidden_act = hidden_act
|
22 |
+
self.bias = bias
|
23 |
+
super().__init__(**kwargs)
|
projector/model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0c2126a4ee2ac250fe6f7e67c5eb00167494e852dee344949e0f43b1c4dfe7b2
|
3 |
+
size 131113328
|
projector/modeling_projector.py
ADDED
@@ -0,0 +1,41 @@
|
|
|
|
|
|
|
|
|
|
|
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|
1 |
+
# Copyright (c) OpenMMLab. All rights reserved.
|
2 |
+
import torch
|
3 |
+
import torch.nn as nn
|
4 |
+
from transformers import PreTrainedModel
|
5 |
+
from transformers.activations import ACT2FN
|
6 |
+
|
7 |
+
from .configuration_projector import ProjectorConfig
|
8 |
+
|
9 |
+
|
10 |
+
class ProjectorModel(PreTrainedModel):
|
11 |
+
_auto_class = "AutoModel"
|
12 |
+
config_class = ProjectorConfig
|
13 |
+
base_model_prefix = "model"
|
14 |
+
supports_gradient_checkpointing = True
|
15 |
+
|
16 |
+
def __init__(self, config: ProjectorConfig) -> None:
|
17 |
+
super().__init__(config)
|
18 |
+
self.gradient_checkpointing = False
|
19 |
+
|
20 |
+
modules = [nn.Linear(config.visual_hidden_size, config.llm_hidden_size, bias=config.bias)]
|
21 |
+
for _ in range(1, config.depth):
|
22 |
+
modules.append(ACT2FN[config.hidden_act])
|
23 |
+
modules.append(nn.Linear(config.llm_hidden_size, config.llm_hidden_size, bias=config.bias))
|
24 |
+
self.model = nn.Sequential(*modules)
|
25 |
+
|
26 |
+
def enable_input_require_grads(self):
|
27 |
+
def make_inputs_require_grad(module, input, output):
|
28 |
+
output.requires_grad_(True)
|
29 |
+
|
30 |
+
self.model.register_forward_hook(make_inputs_require_grad)
|
31 |
+
|
32 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
33 |
+
if isinstance(module, ProjectorModel):
|
34 |
+
module.gradient_checkpointing = value
|
35 |
+
|
36 |
+
def forward(self, x):
|
37 |
+
if self.gradient_checkpointing and self.training:
|
38 |
+
layer_outputs = torch.utils.checkpoint.checkpoint(self.model, x)
|
39 |
+
else:
|
40 |
+
layer_outputs = self.model(x)
|
41 |
+
return layer_outputs
|
visual_encoder_adapter/README.md
ADDED
@@ -0,0 +1,204 @@
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|
1 |
+
---
|
2 |
+
library_name: peft
|
3 |
+
base_model: apple/DFN5B-CLIP-ViT-H-14-378
|
4 |
+
---
|
5 |
+
|
6 |
+
# Model Card for Model ID
|
7 |
+
|
8 |
+
<!-- Provide a quick summary of what the model is/does. -->
|
9 |
+
|
10 |
+
|
11 |
+
|
12 |
+
## Model Details
|
13 |
+
|
14 |
+
### Model Description
|
15 |
+
|
16 |
+
<!-- Provide a longer summary of what this model is. -->
|
17 |
+
|
18 |
+
|
19 |
+
|
20 |
+
- **Developed by:** [More Information Needed]
|
21 |
+
- **Funded by [optional]:** [More Information Needed]
|
22 |
+
- **Shared by [optional]:** [More Information Needed]
|
23 |
+
- **Model type:** [More Information Needed]
|
24 |
+
- **Language(s) (NLP):** [More Information Needed]
|
25 |
+
- **License:** [More Information Needed]
|
26 |
+
- **Finetuned from model [optional]:** [More Information Needed]
|
27 |
+
|
28 |
+
### Model Sources [optional]
|
29 |
+
|
30 |
+
<!-- Provide the basic links for the model. -->
|
31 |
+
|
32 |
+
- **Repository:** [More Information Needed]
|
33 |
+
- **Paper [optional]:** [More Information Needed]
|
34 |
+
- **Demo [optional]:** [More Information Needed]
|
35 |
+
|
36 |
+
## Uses
|
37 |
+
|
38 |
+
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
39 |
+
|
40 |
+
### Direct Use
|
41 |
+
|
42 |
+
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
43 |
+
|
44 |
+
[More Information Needed]
|
45 |
+
|
46 |
+
### Downstream Use [optional]
|
47 |
+
|
48 |
+
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
49 |
+
|
50 |
+
[More Information Needed]
|
51 |
+
|
52 |
+
### Out-of-Scope Use
|
53 |
+
|
54 |
+
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
55 |
+
|
56 |
+
[More Information Needed]
|
57 |
+
|
58 |
+
## Bias, Risks, and Limitations
|
59 |
+
|
60 |
+
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
61 |
+
|
62 |
+
[More Information Needed]
|
63 |
+
|
64 |
+
### Recommendations
|
65 |
+
|
66 |
+
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
67 |
+
|
68 |
+
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
|
69 |
+
|
70 |
+
## How to Get Started with the Model
|
71 |
+
|
72 |
+
Use the code below to get started with the model.
|
73 |
+
|
74 |
+
[More Information Needed]
|
75 |
+
|
76 |
+
## Training Details
|
77 |
+
|
78 |
+
### Training Data
|
79 |
+
|
80 |
+
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
|
81 |
+
|
82 |
+
[More Information Needed]
|
83 |
+
|
84 |
+
### Training Procedure
|
85 |
+
|
86 |
+
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
87 |
+
|
88 |
+
#### Preprocessing [optional]
|
89 |
+
|
90 |
+
[More Information Needed]
|
91 |
+
|
92 |
+
|
93 |
+
#### Training Hyperparameters
|
94 |
+
|
95 |
+
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
96 |
+
|
97 |
+
#### Speeds, Sizes, Times [optional]
|
98 |
+
|
99 |
+
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
100 |
+
|
101 |
+
[More Information Needed]
|
102 |
+
|
103 |
+
## Evaluation
|
104 |
+
|
105 |
+
<!-- This section describes the evaluation protocols and provides the results. -->
|
106 |
+
|
107 |
+
### Testing Data, Factors & Metrics
|
108 |
+
|
109 |
+
#### Testing Data
|
110 |
+
|
111 |
+
<!-- This should link to a Dataset Card if possible. -->
|
112 |
+
|
113 |
+
[More Information Needed]
|
114 |
+
|
115 |
+
#### Factors
|
116 |
+
|
117 |
+
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
118 |
+
|
119 |
+
[More Information Needed]
|
120 |
+
|
121 |
+
#### Metrics
|
122 |
+
|
123 |
+
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
124 |
+
|
125 |
+
[More Information Needed]
|
126 |
+
|
127 |
+
### Results
|
128 |
+
|
129 |
+
[More Information Needed]
|
130 |
+
|
131 |
+
#### Summary
|
132 |
+
|
133 |
+
|
134 |
+
|
135 |
+
## Model Examination [optional]
|
136 |
+
|
137 |
+
<!-- Relevant interpretability work for the model goes here -->
|
138 |
+
|
139 |
+
[More Information Needed]
|
140 |
+
|
141 |
+
## Environmental Impact
|
142 |
+
|
143 |
+
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
144 |
+
|
145 |
+
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).
|
146 |
+
|
147 |
+
- **Hardware Type:** [More Information Needed]
|
148 |
+
- **Hours used:** [More Information Needed]
|
149 |
+
- **Cloud Provider:** [More Information Needed]
|
150 |
+
- **Compute Region:** [More Information Needed]
|
151 |
+
- **Carbon Emitted:** [More Information Needed]
|
152 |
+
|
153 |
+
## Technical Specifications [optional]
|
154 |
+
|
155 |
+
### Model Architecture and Objective
|
156 |
+
|
157 |
+
[More Information Needed]
|
158 |
+
|
159 |
+
### Compute Infrastructure
|
160 |
+
|
161 |
+
[More Information Needed]
|
162 |
+
|
163 |
+
#### Hardware
|
164 |
+
|
165 |
+
[More Information Needed]
|
166 |
+
|
167 |
+
#### Software
|
168 |
+
|
169 |
+
[More Information Needed]
|
170 |
+
|
171 |
+
## Citation [optional]
|
172 |
+
|
173 |
+
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
174 |
+
|
175 |
+
**BibTeX:**
|
176 |
+
|
177 |
+
[More Information Needed]
|
178 |
+
|
179 |
+
**APA:**
|
180 |
+
|
181 |
+
[More Information Needed]
|
182 |
+
|
183 |
+
## Glossary [optional]
|
184 |
+
|
185 |
+
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
186 |
+
|
187 |
+
[More Information Needed]
|
188 |
+
|
189 |
+
## More Information [optional]
|
190 |
+
|
191 |
+
[More Information Needed]
|
192 |
+
|
193 |
+
## Model Card Authors [optional]
|
194 |
+
|
195 |
+
[More Information Needed]
|
196 |
+
|
197 |
+
## Model Card Contact
|
198 |
+
|
199 |
+
[More Information Needed]
|
200 |
+
|
201 |
+
|
202 |
+
### Framework versions
|
203 |
+
|
204 |
+
- PEFT 0.7.1
|
visual_encoder_adapter/adapter_config.json
ADDED
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"alpha_pattern": {},
|
3 |
+
"auto_mapping": {
|
4 |
+
"base_model_class": "PikaVidEncoder",
|
5 |
+
"parent_library": "xtuner.model.video_encoder"
|
6 |
+
},
|
7 |
+
"base_model_name_or_path": "apple/DFN5B-CLIP-ViT-H-14-378",
|
8 |
+
"bias": "none",
|
9 |
+
"fan_in_fan_out": false,
|
10 |
+
"inference_mode": true,
|
11 |
+
"init_lora_weights": true,
|
12 |
+
"layers_pattern": null,
|
13 |
+
"layers_to_transform": null,
|
14 |
+
"loftq_config": {},
|
15 |
+
"lora_alpha": 16,
|
16 |
+
"lora_dropout": 0.05,
|
17 |
+
"megatron_config": null,
|
18 |
+
"megatron_core": "megatron.core",
|
19 |
+
"modules_to_save": null,
|
20 |
+
"peft_type": "LORA",
|
21 |
+
"r": 64,
|
22 |
+
"rank_pattern": {},
|
23 |
+
"revision": null,
|
24 |
+
"target_modules": [
|
25 |
+
"out_proj",
|
26 |
+
"v_proj",
|
27 |
+
"k_proj",
|
28 |
+
"fc1",
|
29 |
+
"fc2",
|
30 |
+
"q_proj"
|
31 |
+
],
|
32 |
+
"task_type": null
|
33 |
+
}
|
visual_encoder_adapter/adapter_model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0915794d1b139877e627831e1d36d2ef378872141a4c3712c220d00f6e326a74
|
3 |
+
size 188800496
|
vit/config.json
ADDED
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "apple/DFN5B-CLIP-ViT-H-14-378",
|
3 |
+
"architectures": [
|
4 |
+
"CLIPVisionModel"
|
5 |
+
],
|
6 |
+
"attention_dropout": 0.0,
|
7 |
+
"hidden_act": "quick_gelu",
|
8 |
+
"hidden_size": 1280,
|
9 |
+
"image_size": 378,
|
10 |
+
"initializer_factor": 1.0,
|
11 |
+
"initializer_range": 0.02,
|
12 |
+
"intermediate_size": 5120,
|
13 |
+
"layer_norm_eps": 1e-05,
|
14 |
+
"model_type": "clip_vision_model",
|
15 |
+
"num_attention_heads": 16,
|
16 |
+
"num_channels": 3,
|
17 |
+
"num_hidden_layers": 32,
|
18 |
+
"patch_size": 14,
|
19 |
+
"projection_dim": 512,
|
20 |
+
"torch_dtype": "float32",
|
21 |
+
"transformers_version": "4.37.2"
|
22 |
+
}
|
vit/preprocessor_config.json
ADDED
@@ -0,0 +1,27 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"crop_size": {
|
3 |
+
"height": 378,
|
4 |
+
"width": 378
|
5 |
+
},
|
6 |
+
"do_center_crop": true,
|
7 |
+
"do_convert_rgb": true,
|
8 |
+
"do_normalize": true,
|
9 |
+
"do_rescale": true,
|
10 |
+
"do_resize": true,
|
11 |
+
"image_mean": [
|
12 |
+
0.48145466,
|
13 |
+
0.4578275,
|
14 |
+
0.40821073
|
15 |
+
],
|
16 |
+
"image_processor_type": "CLIPImageProcessor",
|
17 |
+
"image_std": [
|
18 |
+
0.26862954,
|
19 |
+
0.26130258,
|
20 |
+
0.27577711
|
21 |
+
],
|
22 |
+
"resample": 3,
|
23 |
+
"rescale_factor": 0.00392156862745098,
|
24 |
+
"size": {
|
25 |
+
"shortest_edge": 378
|
26 |
+
}
|
27 |
+
}
|
vit/pytorch_model-00001-of-00002.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:4611f601856556b5a54e0631251c9279a2610b42825a51519430efc37d1a64ce
|
3 |
+
size 1994332295
|
vit/pytorch_model-00002-of-00002.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d0a9038c66b6348e1b86889c0a96bcb942b95e53c950ec85ca51345cd9318e51
|
3 |
+
size 531341514
|
vit/pytorch_model.bin.index.json
ADDED
@@ -0,0 +1,526 @@
|
|
|
|
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484 |
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"vision_model.encoder.layers.7.self_attn.v_proj.bias": "pytorch_model-00001-of-00002.bin",
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"vision_model.encoder.layers.7.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
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"vision_model.encoder.layers.8.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
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"vision_model.encoder.layers.8.self_attn.v_proj.bias": "pytorch_model-00001-of-00002.bin",
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"vision_model.encoder.layers.8.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
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505 |
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"vision_model.encoder.layers.9.layer_norm1.bias": "pytorch_model-00001-of-00002.bin",
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506 |
+
"vision_model.encoder.layers.9.layer_norm1.weight": "pytorch_model-00001-of-00002.bin",
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507 |
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"vision_model.encoder.layers.9.layer_norm2.bias": "pytorch_model-00001-of-00002.bin",
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508 |
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"vision_model.encoder.layers.9.layer_norm2.weight": "pytorch_model-00001-of-00002.bin",
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"vision_model.encoder.layers.9.mlp.fc1.bias": "pytorch_model-00001-of-00002.bin",
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"vision_model.encoder.layers.9.mlp.fc1.weight": "pytorch_model-00001-of-00002.bin",
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"vision_model.encoder.layers.9.self_attn.k_proj.bias": "pytorch_model-00001-of-00002.bin",
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514 |
+
"vision_model.encoder.layers.9.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
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515 |
+
"vision_model.encoder.layers.9.self_attn.out_proj.bias": "pytorch_model-00001-of-00002.bin",
|
516 |
+
"vision_model.encoder.layers.9.self_attn.out_proj.weight": "pytorch_model-00001-of-00002.bin",
|
517 |
+
"vision_model.encoder.layers.9.self_attn.q_proj.bias": "pytorch_model-00001-of-00002.bin",
|
518 |
+
"vision_model.encoder.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
519 |
+
"vision_model.encoder.layers.9.self_attn.v_proj.bias": "pytorch_model-00001-of-00002.bin",
|
520 |
+
"vision_model.encoder.layers.9.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
521 |
+
"vision_model.post_layernorm.bias": "pytorch_model-00002-of-00002.bin",
|
522 |
+
"vision_model.post_layernorm.weight": "pytorch_model-00002-of-00002.bin",
|
523 |
+
"vision_model.pre_layrnorm.bias": "pytorch_model-00001-of-00002.bin",
|
524 |
+
"vision_model.pre_layrnorm.weight": "pytorch_model-00001-of-00002.bin"
|
525 |
+
}
|
526 |
+
}
|
xtuner_config.py
ADDED
@@ -0,0 +1,255 @@
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|
|
1 |
+
import torch
|
2 |
+
from mmengine.dataset import DefaultSampler
|
3 |
+
from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
|
4 |
+
LoggerHook, ParamSchedulerHook)
|
5 |
+
|
6 |
+
from transformers import (AutoModelForCausalLM, AutoTokenizer,
|
7 |
+
BitsAndBytesConfig,
|
8 |
+
CLIPImageProcessor, CLIPVisionModel)
|
9 |
+
from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
|
10 |
+
from peft import LoraConfig
|
11 |
+
from math import sqrt
|
12 |
+
from torch.optim import AdamW
|
13 |
+
from xtuner.dataset import VideoDataset, PikaDataset, ConcatDataset, ShareGPTVideoDataset
|
14 |
+
from xtuner.dataset.collate_fns import default_collate_fn
|
15 |
+
from xtuner.dataset.map_fns import llava_video_map_fn, llava_map_fn, pika_map_fn, template_map_fn_factory
|
16 |
+
from xtuner.dataset.samplers import LengthGroupedSampler
|
17 |
+
from xtuner.engine import DatasetInfoHook, EvaluateChatHook
|
18 |
+
from xtuner.model import PikaModel, PikaVidEncoder
|
19 |
+
from xtuner.utils import PROMPT_TEMPLATE
|
20 |
+
|
21 |
+
|
22 |
+
#######################################################################
|
23 |
+
# PART 1 Settings #
|
24 |
+
#######################################################################
|
25 |
+
# Model
|
26 |
+
llm_name_or_path = 'lmsys/vicuna-13b-v1.5-16k'
|
27 |
+
visual_encoder_name_or_path = 'apple/DFN5B-CLIP-ViT-H-14-378'
|
28 |
+
# Specify the s3 pretrained pth
|
29 |
+
pretrained_pth = 'work_dirs/13b_16k_s5/iter_400.pth'
|
30 |
+
prompt_template = PROMPT_TEMPLATE.vicuna
|
31 |
+
|
32 |
+
size = 378
|
33 |
+
# None for sampling all the video frames
|
34 |
+
n_sample_frames = 32
|
35 |
+
visual_token_merge_ratio = 0.1
|
36 |
+
accumulative_counts = 32
|
37 |
+
lr = 1e-4
|
38 |
+
batch_size = 1 # per_device can only be set to 1 to support image and video mix training
|
39 |
+
|
40 |
+
max_length = 4096
|
41 |
+
dataloader_num_workers = 0
|
42 |
+
max_epochs = 1
|
43 |
+
optim_type = AdamW
|
44 |
+
betas = (0.9, 0.999)
|
45 |
+
weight_decay = 0.1
|
46 |
+
max_norm = 1 # grad clip
|
47 |
+
warmup_ratio = 0.03
|
48 |
+
|
49 |
+
# Save
|
50 |
+
save_steps = 500
|
51 |
+
save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited)
|
52 |
+
|
53 |
+
#######################################################################
|
54 |
+
# PART 2 Model & Tokenizer & Image Processor #
|
55 |
+
#######################################################################
|
56 |
+
tokenizer = dict(
|
57 |
+
type=AutoTokenizer.from_pretrained,
|
58 |
+
pretrained_model_name_or_path=llm_name_or_path,
|
59 |
+
trust_remote_code=True,
|
60 |
+
padding_side='right')
|
61 |
+
|
62 |
+
image_processor = dict(
|
63 |
+
type=CLIPImageProcessor.from_pretrained,
|
64 |
+
pretrained_model_name_or_path='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k',
|
65 |
+
trust_remote_code=True,
|
66 |
+
size=size,
|
67 |
+
crop_size=size)
|
68 |
+
|
69 |
+
model = dict(
|
70 |
+
type=PikaModel,
|
71 |
+
freeze_llm=True,
|
72 |
+
freeze_visual_encoder=True,
|
73 |
+
pretrained_pth=pretrained_pth,
|
74 |
+
llm=dict(
|
75 |
+
type=AutoModelForCausalLM.from_pretrained,
|
76 |
+
pretrained_model_name_or_path=llm_name_or_path,
|
77 |
+
trust_remote_code=True,
|
78 |
+
torch_dtype=torch.float16,
|
79 |
+
quantization_config=dict(
|
80 |
+
type=BitsAndBytesConfig,
|
81 |
+
load_in_4bit=True,
|
82 |
+
load_in_8bit=False,
|
83 |
+
llm_int8_threshold=6.0,
|
84 |
+
llm_int8_has_fp16_weight=False,
|
85 |
+
bnb_4bit_compute_dtype=torch.float16,
|
86 |
+
bnb_4bit_use_double_quant=True,
|
87 |
+
bnb_4bit_quant_type='nf4')),
|
88 |
+
llm_lora=dict(
|
89 |
+
type=LoraConfig,
|
90 |
+
r=512,
|
91 |
+
lora_alpha=256,
|
92 |
+
lora_dropout=0.05,
|
93 |
+
bias='none',
|
94 |
+
task_type='CAUSAL_LM'),
|
95 |
+
visual_encoder=dict(
|
96 |
+
# type=CLIPVisionModel.from_pretrained,
|
97 |
+
type=PikaVidEncoder.from_pretrained,
|
98 |
+
pretrained_model_name_or_path=visual_encoder_name_or_path,
|
99 |
+
visual_token_merge_ratio=visual_token_merge_ratio),
|
100 |
+
visual_encoder_lora=dict(
|
101 |
+
type=LoraConfig, r=64, lora_alpha=16, lora_dropout=0.05, bias='none'),
|
102 |
+
)
|
103 |
+
|
104 |
+
|
105 |
+
#######################################################################
|
106 |
+
# PART 3 Dataset & Dataloader #
|
107 |
+
#######################################################################
|
108 |
+
allava_image_caption_dataset = dict(
|
109 |
+
type=PikaDataset,
|
110 |
+
data_path='./data/image_finetune/ALLaVA-Caption-LAION-4V',
|
111 |
+
image_folder='./data/image_data',
|
112 |
+
tokenizer=tokenizer,
|
113 |
+
image_processor=image_processor,
|
114 |
+
dataset_map_fn=llava_map_fn,
|
115 |
+
template_map_fn=dict(
|
116 |
+
type=template_map_fn_factory, template=prompt_template),
|
117 |
+
max_length=max_length,
|
118 |
+
pad_image_to_square=False,
|
119 |
+
keep_aspect_ratio=True,)
|
120 |
+
|
121 |
+
sharegpt4v_video_caption_dataset = dict(
|
122 |
+
type=ShareGPTVideoDataset,
|
123 |
+
data_path='./data/video_finetune/sharegptvideo_caption_full_frame',
|
124 |
+
image_folder='./data/video_data/sharegptvideo_900k',
|
125 |
+
tokenizer=tokenizer,
|
126 |
+
image_processor=image_processor,
|
127 |
+
dataset_map_fn=llava_video_map_fn,
|
128 |
+
template_map_fn=dict(
|
129 |
+
type=template_map_fn_factory, template=prompt_template),
|
130 |
+
max_length=max_length,
|
131 |
+
pad_image_to_square=False,
|
132 |
+
frame_number=n_sample_frames,
|
133 |
+
keep_aspect_ratio=True,)
|
134 |
+
|
135 |
+
# mix video and image
|
136 |
+
train_dataset = dict(
|
137 |
+
type=ConcatDataset,
|
138 |
+
datasets=[
|
139 |
+
allava_image_caption_dataset,
|
140 |
+
sharegpt4v_video_caption_dataset,
|
141 |
+
])
|
142 |
+
|
143 |
+
train_dataloader = dict(
|
144 |
+
batch_size=batch_size,
|
145 |
+
num_workers=dataloader_num_workers,
|
146 |
+
dataset=train_dataset,
|
147 |
+
# sampler=dict(
|
148 |
+
# type=LengthGroupedSampler,
|
149 |
+
# length_property='modality_length',
|
150 |
+
# per_device_batch_size=batch_size * accumulative_counts),
|
151 |
+
sampler=dict(type=DefaultSampler, shuffle=True),
|
152 |
+
collate_fn=dict(type=default_collate_fn))
|
153 |
+
|
154 |
+
#######################################################################
|
155 |
+
# PART 4 Scheduler & Optimizer #
|
156 |
+
#######################################################################
|
157 |
+
# optimizer
|
158 |
+
optim_wrapper = dict(
|
159 |
+
type=AmpOptimWrapper,
|
160 |
+
optimizer=dict(
|
161 |
+
type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
|
162 |
+
clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
|
163 |
+
accumulative_counts=accumulative_counts,
|
164 |
+
loss_scale='dynamic',
|
165 |
+
dtype='float16')
|
166 |
+
|
167 |
+
# learning policy
|
168 |
+
# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501
|
169 |
+
param_scheduler = [
|
170 |
+
dict(
|
171 |
+
type=LinearLR,
|
172 |
+
start_factor=1e-5,
|
173 |
+
by_epoch=True,
|
174 |
+
begin=0,
|
175 |
+
end=warmup_ratio * max_epochs,
|
176 |
+
convert_to_iter_based=True),
|
177 |
+
dict(
|
178 |
+
type=CosineAnnealingLR,
|
179 |
+
eta_min=0.0,
|
180 |
+
by_epoch=True,
|
181 |
+
begin=warmup_ratio * max_epochs,
|
182 |
+
T_max=max_epochs,
|
183 |
+
convert_to_iter_based=True)
|
184 |
+
]
|
185 |
+
|
186 |
+
# train, val, test setting
|
187 |
+
train_cfg = dict(by_epoch=True, max_epochs=max_epochs, val_interval=1)
|
188 |
+
|
189 |
+
#######################################################################
|
190 |
+
# PART 5 Runtime #
|
191 |
+
#######################################################################
|
192 |
+
# Evaluate the generation performance during the training
|
193 |
+
evaluation_freq = 500
|
194 |
+
SYSTEM = ''
|
195 |
+
evaluation_images = 'https://llava-vl.github.io/static/images/view.jpg'
|
196 |
+
evaluation_inputs = ['请描述一下这张照片', 'Please describe this picture']
|
197 |
+
|
198 |
+
|
199 |
+
# Log the dialogue periodically during the training process, optional
|
200 |
+
custom_hooks = [
|
201 |
+
dict(type=DatasetInfoHook, tokenizer=tokenizer),
|
202 |
+
dict(
|
203 |
+
type=EvaluateChatHook,
|
204 |
+
tokenizer=tokenizer,
|
205 |
+
image_processor=image_processor,
|
206 |
+
every_n_iters=evaluation_freq,
|
207 |
+
evaluation_inputs=evaluation_inputs,
|
208 |
+
evaluation_images=evaluation_images,
|
209 |
+
system=SYSTEM,
|
210 |
+
prompt_template=prompt_template)
|
211 |
+
]
|
212 |
+
|
213 |
+
# configure default hooks
|
214 |
+
default_hooks = dict(
|
215 |
+
# record the time of every iteration.
|
216 |
+
timer=dict(type=IterTimerHook),
|
217 |
+
# print log every 100 iterations.
|
218 |
+
logger=dict(type=LoggerHook, interval=10),
|
219 |
+
# enable the parameter scheduler.
|
220 |
+
param_scheduler=dict(type=ParamSchedulerHook),
|
221 |
+
# save checkpoint per epoch.
|
222 |
+
# checkpoint=dict(type=CheckpointHook, interval=1),
|
223 |
+
checkpoint=dict(
|
224 |
+
type=CheckpointHook,
|
225 |
+
by_epoch=False,
|
226 |
+
interval=save_steps,
|
227 |
+
max_keep_ckpts=save_total_limit),
|
228 |
+
# set sampler seed in distributed evrionment.
|
229 |
+
sampler_seed=dict(type=DistSamplerSeedHook),
|
230 |
+
)
|
231 |
+
|
232 |
+
# configure environment
|
233 |
+
env_cfg = dict(
|
234 |
+
# whether to enable cudnn benchmark
|
235 |
+
cudnn_benchmark=False,
|
236 |
+
# set multi process parameters
|
237 |
+
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
|
238 |
+
# set distributed parameters
|
239 |
+
dist_cfg=dict(backend='nccl'),
|
240 |
+
)
|
241 |
+
|
242 |
+
# set visualizer
|
243 |
+
visualizer = None
|
244 |
+
|
245 |
+
# set log level
|
246 |
+
log_level = 'INFO'
|
247 |
+
|
248 |
+
# load from which checkpoint
|
249 |
+
load_from = None
|
250 |
+
|
251 |
+
# whether to resume training from the loaded checkpoint
|
252 |
+
resume = False
|
253 |
+
|
254 |
+
# Defaults to use random seed and disable `deterministic`
|
255 |
+
randomness = dict(seed=None, deterministic=False)
|