first commit
Browse files- README.md +230 -0
- config.json +46 -0
- configuration_fegeo_llama.py +253 -0
- generation_config.json +7 -0
- model-00001-of-00004.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model.safetensors.index.json +867 -0
- modeling_fegeo_llama.py +0 -0
- sample/4927.png +0 -0
- special_tokens_map.json +30 -0
- tokenizer.model +3 -0
- tokenizer_config.json +53 -0
README.md
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| 1 |
+
# 基于FormalGeo7K的推理模型
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| 2 |
+
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| 3 |
+
## 快速开始
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| 4 |
+
在运行脚本之前，首先安装如下必要的依赖。
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| 5 |
+
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| 6 |
+
```shell
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| 7 |
+
pip install --upgrade pip
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| 8 |
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pip install torch transformers==4.40.0
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| 9 |
+
pip install sentencepiece
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| 10 |
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pip install accelerate pillow
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| 11 |
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pip install ninja
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| 12 |
+
pip install packaging
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| 13 |
+
pip install flash-attn --no-build-isolation
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| 14 |
+
```
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| 15 |
+
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| 16 |
+
```python
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| 17 |
+
import torch
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| 18 |
+
import transformers
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| 19 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
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| 20 |
+
from PIL import Image
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| 21 |
+
import warnings
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| 22 |
+
import numpy as np
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| 23 |
+
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| 24 |
+
# set device
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| 25 |
+
device = 'cuda' # or cpu
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| 26 |
+
torch.set_default_device(device)
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| 27 |
+
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| 28 |
+
# create model
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| 29 |
+
model = AutoModelForCausalLM.from_pretrained(
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| 30 |
+
'NaughtyDog97/FormalEnhencedGPS',
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| 31 |
+
torch_dtype=torch.float16, # float32 for cpu
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| 32 |
+
device_map='auto',
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| 33 |
+
trust_remote_code=True)
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| 34 |
+
tokenizer = AutoTokenizer.from_pretrained(
|
| 35 |
+
'NaughtyDog97/FormalEnhencedGPS',
|
| 36 |
+
trust_remote_code=True)
|
| 37 |
+
|
| 38 |
+
# text prompt
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| 39 |
+
img_path = 'sample/4927.png'
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| 40 |
+
qs = 'As shown in the diagram, AE/AB=1/4, M is the midpoint of segment AC, BE is parallel to CP, EA is parallel to CP. Find the ratio of the length of line BC to the length of line CD.'
|
| 41 |
+
prompt = f'Using the provided geometric image and question, first predict the construction_cdl and image_cdl. Then, give a detailed step-by-step solution.\nThe question is:\n{qs}'
|
| 42 |
+
text = f'<|im_start|>user\n<image>\n{prompt}<|im_end|>\n<|im_start|>assistant\n'
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| 43 |
+
text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
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| 44 |
+
input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1][1:], dtype=torch.long).unsqueeze(0).to(device)
|
| 45 |
+
|
| 46 |
+
# image, sample images can be found in images folder
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| 47 |
+
image = Image.open(img_path).convert('RGB')
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| 48 |
+
|
| 49 |
+
image_tensor = model.process_images([image], model.config).to(dtype=model.dtype, device=device)
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| 50 |
+
|
| 51 |
+
# generate
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| 52 |
+
with torch.inference_mode():
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| 53 |
+
output_ids = model.generate(
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| 54 |
+
input_ids,
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| 55 |
+
images=image_tensor,
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| 56 |
+
do_sample=False,
|
| 57 |
+
temperature=None,
|
| 58 |
+
top_p=None,
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| 59 |
+
top_k=None,
|
| 60 |
+
num_beams=1,
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| 61 |
+
max_new_tokens=3500,
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| 62 |
+
eos_token_id=tokenizer.eos_token_id,
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| 63 |
+
repetition_penalty=None,
|
| 64 |
+
use_cache=True
|
| 65 |
+
)[0]
|
| 66 |
+
|
| 67 |
+
|
| 68 |
+
respones = tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()
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| 69 |
+
print(respones)
|
| 70 |
+
|
| 71 |
+
```
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| 72 |
+
|
| 73 |
+
我们的模型支持的求解方式有如下三种:
|
| 74 |
+
```python
|
| 75 |
+
# Q => Predicted CDL + CoT Answer
|
| 76 |
+
prompt = f'Using the provided geometric image and question, first predict the construction_cdl and image_cdl. Then, give a detailed step-by-step solution.\nThe question is:\n{qs}'
|
| 77 |
+
|
| 78 |
+
|
| 79 |
+
# Q + Predicted CDL => CoT Answer
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| 80 |
+
prompt = f'Using the provided geometric image, construction_cdl, image_cdl, and question, give a detailed step-by-step solution. Note that there may be minor errors in the construction_cdl and image_cdl.\nThe construction_cdl is:\n{predict_consCDL}\nThe image_cdl is:\n{predict_imgCDL}\nThe question is:\n{qs}'
|
| 81 |
+
|
| 82 |
+
|
| 83 |
+
# Q + Predicted CDL => Calibrated CDL + CoT Answer
|
| 84 |
+
prompt = f'Using the provided geometric image and the possibly erroneous construction_cdl and image_cdl, first calibrate the construction_cdl and image_cdl, then give a detailed step-by-step solution to the question.\nThe initial construction_cdl is:\n{predict_consCDL}\nThe initial image_cdl is:\n{predict_imgCDL}\nThe question is:\n{qs}'
|
| 85 |
+
|
| 86 |
+
|
| 87 |
+
```
|
| 88 |
+
|
| 89 |
+
## 结合Formalization模型的推理
|
| 90 |
+
```python
|
| 91 |
+
import torch
|
| 92 |
+
import transformers
|
| 93 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 94 |
+
from PIL import Image
|
| 95 |
+
import warnings
|
| 96 |
+
import numpy as np
|
| 97 |
+
import re
|
| 98 |
+
|
| 99 |
+
|
| 100 |
+
def parse_cdl(input_string):
|
| 101 |
+
# 使用正则表达式查找各个部分
|
| 102 |
+
patterns = {
|
| 103 |
+
'construction_cdl': r'(?:The )?(?:calibrate )?construction_cdl(?: is)?:\n(.*?)(?=\n(?:The )?(?:calibrate )?\w+_cdl is:|\n(?:The )?(?:calibrate )?\w+_cdl:|\nSolution is:|\Z)',
|
| 104 |
+
'image_cdl': r'(?:The )?(?:calibrate )?image_cdl(?: is)?:\n(.*?)(?=\n(?:The )?(?:calibrate )?\w+_cdl is:|\n(?:The )?(?:calibrate )?\w+_cdl:|\nSolution is:|\Z)',
|
| 105 |
+
'text_cdl': r'(?:The )?text_cdl(?: is)?:\n(.*?)(?=\n(?:The )?\w+_cdl is:|\n(?:The )?\w+_cdl:|\nSolution is:|\Z)',
|
| 106 |
+
'goal_cdl': r'(?:The )?goal_cdl(?: is)?:\n(.*?)(?=\n(?:The )?\w+_cdl is:|\n(?:The )?\w+_cdl:|\nSolution is:|\Z)'
|
| 107 |
+
}
|
| 108 |
+
|
| 109 |
+
results = {}
|
| 110 |
+
|
| 111 |
+
# 优先匹配包含"calibrate"的版本
|
| 112 |
+
for key, pattern in patterns.items():
|
| 113 |
+
pattern = pattern.replace("(?:calibrate )?", "(?:calibrate )")
|
| 114 |
+
match = re.search(pattern, input_string, re.DOTALL)
|
| 115 |
+
if match:
|
| 116 |
+
results[key] = match.group(1).strip()
|
| 117 |
+
else:
|
| 118 |
+
# 如果未找到包含"calibrate"的版本，尝试匹配不含"calibrate"的版本
|
| 119 |
+
pattern = pattern.replace("(?:calibrate )", "(?:calibrate )?")
|
| 120 |
+
match = re.search(pattern, input_string, re.DOTALL)
|
| 121 |
+
if match:
|
| 122 |
+
results[key] = match.group(1).strip()
|
| 123 |
+
|
| 124 |
+
return results
|
| 125 |
+
|
| 126 |
+
|
| 127 |
+
# set device
|
| 128 |
+
device = 'cuda' # or cpu
|
| 129 |
+
torch.set_default_device(device)
|
| 130 |
+
|
| 131 |
+
# create model
|
| 132 |
+
formalization_model = AutoModelForCausalLM.from_pretrained(
|
| 133 |
+
'NaughtyDog97/GeoFormalizer',
|
| 134 |
+
torch_dtype=torch.float16, # float32 for cpu
|
| 135 |
+
device_map='auto',
|
| 136 |
+
trust_remote_code=True)
|
| 137 |
+
|
| 138 |
+
formalization_tokenizer = AutoTokenizer.from_pretrained(
|
| 139 |
+
'NaughtyDog97/GeoFormalizer',
|
| 140 |
+
trust_remote_code=True)
|
| 141 |
+
|
| 142 |
+
|
| 143 |
+
reason_model = AutoModelForCausalLM.from_pretrained(
|
| 144 |
+
'NaughtyDog97/FormalEnhencedGPS',
|
| 145 |
+
torch_dtype=torch.float16, # float32 for cpu
|
| 146 |
+
device_map='auto',
|
| 147 |
+
trust_remote_code=True)
|
| 148 |
+
reason_tokenizer = AutoTokenizer.from_pretrained(
|
| 149 |
+
'NaughtyDog97/FormalEnhencedGPS',
|
| 150 |
+
trust_remote_code=True)
|
| 151 |
+
|
| 152 |
+
|
| 153 |
+
|
| 154 |
+
img_path = 'sample/4927.png'
|
| 155 |
+
image = Image.open(img_path).convert('RGB')
|
| 156 |
+
|
| 157 |
+
|
| 158 |
+
# formalization
|
| 159 |
+
prompt = 'Based on the image, first describe what you see in the figure, then predict the construction_cdl and image_cdl and calibrate it.'
|
| 160 |
+
text = f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image>\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
|
| 161 |
+
text_chunks = [formalization_tokenizer(chunk).input_ids for chunk in text.split('<image>')]
|
| 162 |
+
input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1][1:], dtype=torch.long).unsqueeze(0).to(device)
|
| 163 |
+
|
| 164 |
+
# generate
|
| 165 |
+
image_tensor = formalization_model.process_images([image], formalization_model.config).to(dtype=formalization_model.dtype, device=device)
|
| 166 |
+
with torch.inference_mode():
|
| 167 |
+
output_ids = formalization_model.generate(
|
| 168 |
+
input_ids,
|
| 169 |
+
images=image_tensor,
|
| 170 |
+
do_sample=False,
|
| 171 |
+
temperature=None,
|
| 172 |
+
top_p=None,
|
| 173 |
+
top_k=None,
|
| 174 |
+
num_beams=1,
|
| 175 |
+
max_new_tokens=3500,
|
| 176 |
+
eos_token_id=formalization_tokenizer.eos_token_id,
|
| 177 |
+
repetition_penalty=None,
|
| 178 |
+
use_cache=True
|
| 179 |
+
)[0]
|
| 180 |
+
|
| 181 |
+
|
| 182 |
+
respones = formalization_tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()
|
| 183 |
+
print(f'Formalization result is\n{respones}')
|
| 184 |
+
cdl_info = parse_cdl(respones)
|
| 185 |
+
predict_consCDL = cdl_info['construction_cdl']
|
| 186 |
+
predict_imgCDL = cdl_info['image_cdl']
|
| 187 |
+
|
| 188 |
+
|
| 189 |
+
|
| 190 |
+
# reasoning
|
| 191 |
+
|
| 192 |
+
qs = 'As shown in the diagram, AE/AB=1/4, M is the midpoint of segment AC, BE is parallel to CP, EA is parallel to CP. Find the ratio of the length of line BC to the length of line CD.'
|
| 193 |
+
prompt = f'Using the provided geometric image and the possibly erroneous construction_cdl and image_cdl, first calibrate the construction_cdl and image_cdl, then give a detailed step-by-step solution to the question.\nThe initial construction_cdl is:\n{predict_consCDL}\nThe initial image_cdl is:\n{predict_imgCDL}\nThe question is:\n{qs}'
|
| 194 |
+
text = f'<|im_start|>user\n<image>\n{prompt}<|im_end|>\n<|im_start|>assistant\n'
|
| 195 |
+
text_chunks = [reason_tokenizer(chunk).input_ids for chunk in text.split('<image>')]
|
| 196 |
+
input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1][1:], dtype=torch.long).unsqueeze(0).to(device)
|
| 197 |
+
|
| 198 |
+
|
| 199 |
+
|
| 200 |
+
# generate
|
| 201 |
+
image_tensor = reason_model.process_images([image], reason_model.config).to(dtype=reason_model.dtype, device=device)
|
| 202 |
+
with torch.inference_mode():
|
| 203 |
+
output_ids = reason_model.generate(
|
| 204 |
+
input_ids,
|
| 205 |
+
images=image_tensor,
|
| 206 |
+
do_sample=False,
|
| 207 |
+
temperature=None,
|
| 208 |
+
top_p=None,
|
| 209 |
+
top_k=None,
|
| 210 |
+
num_beams=1,
|
| 211 |
+
max_new_tokens=3500,
|
| 212 |
+
eos_token_id=reason_tokenizer.eos_token_id,
|
| 213 |
+
repetition_penalty=None,
|
| 214 |
+
use_cache=True
|
| 215 |
+
)[0]
|
| 216 |
+
|
| 217 |
+
respones = reason_tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()
|
| 218 |
+
print(f'Reasoning steps is\n{respones}')
|
| 219 |
+
|
| 220 |
+
|
| 221 |
+
|
| 222 |
+
```
|
| 223 |
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|
| 224 |
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|
| 225 |
+
|
| 226 |
+
## Performance
|
| 227 |
+
| | Q => Predicted CDL + CoT Answer | Q + Predicted CDL => CoT Answer | Q + Predicted CDL => Calibrated CDL + CoT Answer |
|
| 228 |
+
|-----|-------------------------------------|--------------------------------------|------------------------------------------------------|
|
| 229 |
+
| siglip-0.4B-yi1.5-9B | 62.46/72.65 | 63.11/76.05 | 62.78/74.92 |
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| 230 |
+
|
config.json
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "NaughtyDog97/FormalEnhencedGPS",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"FegeoLlamaForCausalLM"
|
| 5 |
+
],
|
| 6 |
+
"auto_map": {
|
| 7 |
+
"AutoConfig": "configuration_fegeo_llama.FEGeoLlamaConfig",
|
| 8 |
+
"AutoModelForCausalLM": "modeling_fegeo_llama.FEGeoLlamaForCausalLM"
|
| 9 |
+
},
|
| 10 |
+
"attention_bias": false,
|
| 11 |
+
"attention_dropout": 0.0,
|
| 12 |
+
"bos_token_id": 1,
|
| 13 |
+
"eos_token_id": 7,
|
| 14 |
+
"freeze_mm_mlp_adapter": false,
|
| 15 |
+
"freeze_vision_tower": true,
|
| 16 |
+
"hidden_act": "silu",
|
| 17 |
+
"hidden_size": 4096,
|
| 18 |
+
"image_aspect_ratio": "pad",
|
| 19 |
+
"initializer_range": 0.02,
|
| 20 |
+
"intermediate_size": 11008,
|
| 21 |
+
"max_position_embeddings": 4096,
|
| 22 |
+
"mm_hidden_size": 1152,
|
| 23 |
+
"mm_projector_lr": null,
|
| 24 |
+
"mm_projector_type": "mlp2x_gelu",
|
| 25 |
+
"mm_vision_tower": "google/siglip-so400m-patch14-384",
|
| 26 |
+
"model_type": "fegeo-llama",
|
| 27 |
+
"num_attention_heads": 32,
|
| 28 |
+
"num_hidden_layers": 48,
|
| 29 |
+
"num_key_value_heads": 4,
|
| 30 |
+
"pad_token_id": 0,
|
| 31 |
+
"pretraining_tp": 1,
|
| 32 |
+
"rms_norm_eps": 1e-06,
|
| 33 |
+
"rope_scaling": null,
|
| 34 |
+
"rope_theta": 5000000.0,
|
| 35 |
+
"tie_word_embeddings": false,
|
| 36 |
+
"tokenizer_model_max_length": 4096,
|
| 37 |
+
"tokenizer_padding_side": "right",
|
| 38 |
+
"torch_dtype": "bfloat16",
|
| 39 |
+
"transformers_version": "4.40.0",
|
| 40 |
+
"tune_mm_mlp_adapter": false,
|
| 41 |
+
"tune_vision_tower": false,
|
| 42 |
+
"use_cache": true,
|
| 43 |
+
"use_mm_proj": true,
|
| 44 |
+
"use_s2": false,
|
| 45 |
+
"vocab_size": 64000
|
| 46 |
+
}
|
configuration_fegeo_llama.py
ADDED
|
@@ -0,0 +1,253 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
|
|
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|
|
|
|
|
|
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|
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|
|
|
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|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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 |
+
|
| 26 |
+
logger = logging.get_logger(__name__)
|
| 27 |
+
|
| 28 |
+
|
| 29 |
+
from transformers.models.deprecated._archive_maps import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
class LlamaConfig(PretrainedConfig):
|
| 33 |
+
r"""
|
| 34 |
+
This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
|
| 35 |
+
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
| 36 |
+
defaults will yield a similar configuration to that of the LLaMA-7B.
|
| 37 |
+
|
| 38 |
+
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
| 39 |
+
documentation from [`PretrainedConfig`] for more information.
|
| 40 |
+
|
| 41 |
+
|
| 42 |
+
Args:
|
| 43 |
+
vocab_size (`int`, *optional*, defaults to 32000):
|
| 44 |
+
Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
|
| 45 |
+
`inputs_ids` passed when calling [`LlamaModel`]
|
| 46 |
+
hidden_size (`int`, *optional*, defaults to 4096):
|
| 47 |
+
Dimension of the hidden representations.
|
| 48 |
+
intermediate_size (`int`, *optional*, defaults to 11008):
|
| 49 |
+
Dimension of the MLP representations.
|
| 50 |
+
num_hidden_layers (`int`, *optional*, defaults to 32):
|
| 51 |
+
Number of hidden layers in the Transformer decoder.
|
| 52 |
+
num_attention_heads (`int`, *optional*, defaults to 32):
|
| 53 |
+
Number of attention heads for each attention layer in the Transformer decoder.
|
| 54 |
+
num_key_value_heads (`int`, *optional*):
|
| 55 |
+
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
| 56 |
+
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
| 57 |
+
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
| 58 |
+
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
| 59 |
+
by meanpooling all the original heads within that group. For more details checkout [this
|
| 60 |
+
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
| 61 |
+
`num_attention_heads`.
|
| 62 |
+
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
| 63 |
+
The non-linear activation function (function or string) in the decoder.
|
| 64 |
+
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
| 65 |
+
The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
|
| 66 |
+
Llama 2 up to 4096, CodeLlama up to 16384.
|
| 67 |
+
initializer_range (`float`, *optional*, defaults to 0.02):
|
| 68 |
+
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
| 69 |
+
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
| 70 |
+
The epsilon used by the rms normalization layers.
|
| 71 |
+
use_cache (`bool`, *optional*, defaults to `True`):
|
| 72 |
+
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
| 73 |
+
relevant if `config.is_decoder=True`.
|
| 74 |
+
pad_token_id (`int`, *optional*):
|
| 75 |
+
Padding token id.
|
| 76 |
+
bos_token_id (`int`, *optional*, defaults to 1):
|
| 77 |
+
Beginning of stream token id.
|
| 78 |
+
eos_token_id (`int`, *optional*, defaults to 2):
|
| 79 |
+
End of stream token id.
|
| 80 |
+
pretraining_tp (`int`, *optional*, defaults to 1):
|
| 81 |
+
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
|
| 82 |
+
document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
|
| 83 |
+
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
|
| 84 |
+
issue](https://github.com/pytorch/pytorch/issues/76232).
|
| 85 |
+
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
| 86 |
+
Whether to tie weight embeddings
|
| 87 |
+
rope_theta (`float`, *optional*, defaults to 10000.0):
|
| 88 |
+
The base period of the RoPE embeddings.
|
| 89 |
+
rope_scaling (`Dict`, *optional*):
|
| 90 |
+
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
|
| 91 |
+
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
|
| 92 |
+
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
|
| 93 |
+
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
|
| 94 |
+
these scaling strategies behave:
|
| 95 |
+
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
|
| 96 |
+
experimental feature, subject to breaking API changes in future versions.
|
| 97 |
+
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
|
| 98 |
+
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
| 99 |
+
attention_dropout (`float`, *optional*, defaults to 0.0):
|
| 100 |
+
The dropout ratio for the attention probabilities.
|
| 101 |
+
|
| 102 |
+
```python
|
| 103 |
+
>>> from transformers import LlamaModel, LlamaConfig
|
| 104 |
+
|
| 105 |
+
>>> # Initializing a LLaMA llama-7b style configuration
|
| 106 |
+
>>> configuration = LlamaConfig()
|
| 107 |
+
|
| 108 |
+
>>> # Initializing a model from the llama-7b style configuration
|
| 109 |
+
>>> model = LlamaModel(configuration)
|
| 110 |
+
|
| 111 |
+
>>> # Accessing the model configuration
|
| 112 |
+
>>> configuration = model.config
|
| 113 |
+
```"""
|
| 114 |
+
|
| 115 |
+
model_type = "llama"
|
| 116 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
| 117 |
+
|
| 118 |
+
def __init__(
|
| 119 |
+
self,
|
| 120 |
+
vocab_size=32000,
|
| 121 |
+
hidden_size=4096,
|
| 122 |
+
intermediate_size=11008,
|
| 123 |
+
num_hidden_layers=32,
|
| 124 |
+
num_attention_heads=32,
|
| 125 |
+
num_key_value_heads=None,
|
| 126 |
+
hidden_act="silu",
|
| 127 |
+
max_position_embeddings=2048,
|
| 128 |
+
initializer_range=0.02,
|
| 129 |
+
rms_norm_eps=1e-6,
|
| 130 |
+
use_cache=True,
|
| 131 |
+
pad_token_id=None,
|
| 132 |
+
bos_token_id=1,
|
| 133 |
+
eos_token_id=2,
|
| 134 |
+
pretraining_tp=1,
|
| 135 |
+
tie_word_embeddings=False,
|
| 136 |
+
rope_theta=10000.0,
|
| 137 |
+
rope_scaling=None,
|
| 138 |
+
attention_bias=False,
|
| 139 |
+
attention_dropout=0.0,
|
| 140 |
+
**kwargs,
|
| 141 |
+
):
|
| 142 |
+
self.vocab_size = vocab_size
|
| 143 |
+
self.max_position_embeddings = max_position_embeddings
|
| 144 |
+
self.hidden_size = hidden_size
|
| 145 |
+
self.intermediate_size = intermediate_size
|
| 146 |
+
self.num_hidden_layers = num_hidden_layers
|
| 147 |
+
self.num_attention_heads = num_attention_heads
|
| 148 |
+
|
| 149 |
+
# for backward compatibility
|
| 150 |
+
if num_key_value_heads is None:
|
| 151 |
+
num_key_value_heads = num_attention_heads
|
| 152 |
+
|
| 153 |
+
self.num_key_value_heads = num_key_value_heads
|
| 154 |
+
self.hidden_act = hidden_act
|
| 155 |
+
self.initializer_range = initializer_range
|
| 156 |
+
self.rms_norm_eps = rms_norm_eps
|
| 157 |
+
self.pretraining_tp = pretraining_tp
|
| 158 |
+
self.use_cache = use_cache
|
| 159 |
+
self.rope_theta = rope_theta
|
| 160 |
+
self.rope_scaling = rope_scaling
|
| 161 |
+
self._rope_scaling_validation()
|
| 162 |
+
self.attention_bias = attention_bias
|
| 163 |
+
self.attention_dropout = attention_dropout
|
| 164 |
+
|
| 165 |
+
super().__init__(
|
| 166 |
+
pad_token_id=pad_token_id,
|
| 167 |
+
bos_token_id=bos_token_id,
|
| 168 |
+
eos_token_id=eos_token_id,
|
| 169 |
+
tie_word_embeddings=tie_word_embeddings,
|
| 170 |
+
**kwargs,
|
| 171 |
+
)
|
| 172 |
+
|
| 173 |
+
def _rope_scaling_validation(self):
|
| 174 |
+
"""
|
| 175 |
+
Validate the `rope_scaling` configuration.
|
| 176 |
+
"""
|
| 177 |
+
if self.rope_scaling is None:
|
| 178 |
+
return
|
| 179 |
+
|
| 180 |
+
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
|
| 181 |
+
raise ValueError(
|
| 182 |
+
"`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"
|
| 183 |
+
)
|
| 184 |
+
rope_scaling_type = self.rope_scaling.get("type", None)
|
| 185 |
+
rope_scaling_factor = self.rope_scaling.get("factor", None)
|
| 186 |
+
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
|
| 187 |
+
raise ValueError(
|
| 188 |
+
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
|
| 189 |
+
)
|
| 190 |
+
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
|
| 191 |
+
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
|
| 192 |
+
|
| 193 |
+
|
| 194 |
+
|
| 195 |
+
"""Vision model configuration"""
|
| 196 |
+
from typing import Union
|
| 197 |
+
from transformers import PretrainedConfig
|
| 198 |
+
import os
|
| 199 |
+
|
| 200 |
+
|
| 201 |
+
class SigLipVisionConfig(PretrainedConfig):
|
| 202 |
+
model_type = "siglip_vision_model"
|
| 203 |
+
|
| 204 |
+
def __init__(
|
| 205 |
+
self,
|
| 206 |
+
hidden_size=1152,
|
| 207 |
+
image_mean=(0.5, 0.5, 0.5),
|
| 208 |
+
intermediate_size=4304,
|
| 209 |
+
num_hidden_layers=27,
|
| 210 |
+
num_attention_heads=16,
|
| 211 |
+
num_channels=3,
|
| 212 |
+
image_size=384,
|
| 213 |
+
patch_size=14,
|
| 214 |
+
hidden_act="gelu_pytorch_tanh",
|
| 215 |
+
layer_norm_eps=1e-6,
|
| 216 |
+
attention_dropout=0.0,
|
| 217 |
+
**kwargs,
|
| 218 |
+
):
|
| 219 |
+
super().__init__(**kwargs)
|
| 220 |
+
|
| 221 |
+
self.hidden_size = hidden_size
|
| 222 |
+
self.intermediate_size = intermediate_size
|
| 223 |
+
self.num_hidden_layers = num_hidden_layers
|
| 224 |
+
self.num_attention_heads = num_attention_heads
|
| 225 |
+
self.num_channels = num_channels
|
| 226 |
+
self.patch_size = patch_size
|
| 227 |
+
self.image_size = image_size
|
| 228 |
+
self.attention_dropout = attention_dropout
|
| 229 |
+
self.layer_norm_eps = layer_norm_eps
|
| 230 |
+
self.hidden_act = hidden_act
|
| 231 |
+
self.image_mean = image_mean
|
| 232 |
+
|
| 233 |
+
@classmethod
|
| 234 |
+
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
|
| 235 |
+
cls._set_token_in_kwargs(kwargs)
|
| 236 |
+
|
| 237 |
+
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
|
| 238 |
+
|
| 239 |
+
# get the vision config dict if we are loading from SigLipConfig
|
| 240 |
+
if config_dict.get("model_type") == "siglip":
|
| 241 |
+
config_dict = config_dict["vision_config"]
|
| 242 |
+
|
| 243 |
+
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
|
| 244 |
+
logger.warning(
|
| 245 |
+
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
|
| 246 |
+
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
|
| 247 |
+
)
|
| 248 |
+
|
| 249 |
+
return cls.from_dict(config_dict, **kwargs)
|
| 250 |
+
|
| 251 |
+
|
| 252 |
+
class FEGeoLlamaConfig(LlamaConfig):
|
| 253 |
+
model_type = "fegeo-llama"
|
generation_config.json
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 1,
|
| 4 |
+
"eos_token_id": 7,
|
| 5 |
+
"pad_token_id": 0,
|
| 6 |
+
"transformers_version": "4.40.0"
|
| 7 |
+
}
|
model-00001-of-00004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d1610e23d7513474a91e8f52e058923e1886089874dfce67aa2efb8b07c91276
|
| 3 |
+
size 4932711424
|
model-00002-of-00004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8a666a7f2eb4bea1c84bf46b4c836c8b864c8ff51ba3c60dd940324e5d4c6805
|
| 3 |
+
size 4976802816
|
model-00003-of-00004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b2fbe347a0629c56e02ddb6d9f19c7a1e842510b6f6648498dbae60a20162257
|
| 3 |
+
size 4968397360
|
model-00004-of-00004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3d4e0fa5d1d8bea505e7e9af18629e24714091ddb52785b3229a0fe04edb76f9
|
| 3 |
+
size 3619523440
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,867 @@
|
|
|
|
|
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|
|
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|
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"single_word": false
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| 29 |
+
}
|
| 30 |
+
}
|
tokenizer.model
ADDED
|
@@ -0,0 +1,3 @@
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:386c49cf943d71aa110361135338c50e38beeff0a66593480421f37b319e1a39
|
| 3 |
+
size 1033105
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,53 @@
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|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": false,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"add_prefix_space": true,
|
| 5 |
+
"added_tokens_decoder": {
|
| 6 |
+
"0": {
|
| 7 |
+
"content": "<unk>",
|
| 8 |
+
"lstrip": false,
|
| 9 |
+
"normalized": true,
|
| 10 |
+
"rstrip": false,
|
| 11 |
+
"single_word": false,
|
| 12 |
+
"special": true
|
| 13 |
+
},
|
| 14 |
+
"1": {
|
| 15 |
+
"content": "<|startoftext|>",
|
| 16 |
+
"lstrip": false,
|
| 17 |
+
"normalized": true,
|
| 18 |
+
"rstrip": false,
|
| 19 |
+
"single_word": false,
|
| 20 |
+
"special": true
|
| 21 |
+
},
|
| 22 |
+
"2": {
|
| 23 |
+
"content": "<|endoftext|>",
|
| 24 |
+
"lstrip": false,
|
| 25 |
+
"normalized": true,
|
| 26 |
+
"rstrip": false,
|
| 27 |
+
"single_word": false,
|
| 28 |
+
"special": true
|
| 29 |
+
},
|
| 30 |
+
"7": {
|
| 31 |
+
"content": "<|im_end|>",
|
| 32 |
+
"lstrip": false,
|
| 33 |
+
"normalized": false,
|
| 34 |
+
"rstrip": false,
|
| 35 |
+
"single_word": false,
|
| 36 |
+
"special": true
|
| 37 |
+
}
|
| 38 |
+
},
|
| 39 |
+
"bos_token": "<|startoftext|>",
|
| 40 |
+
"chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\\n' + content + '<|im_end|>\\n<|im_start|>assistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\\n' }}{% endif %}{% endfor %}",
|
| 41 |
+
"clean_up_tokenization_spaces": false,
|
| 42 |
+
"eos_token": "<|im_end|>",
|
| 43 |
+
"legacy": true,
|
| 44 |
+
"model_max_length": 4096,
|
| 45 |
+
"pad_token": "<unk>",
|
| 46 |
+
"padding_side": "right",
|
| 47 |
+
"sp_model_kwargs": {},
|
| 48 |
+
"spaces_between_special_tokens": false,
|
| 49 |
+
"split_special_tokens": false,
|
| 50 |
+
"tokenizer_class": "LlamaTokenizer",
|
| 51 |
+
"unk_token": "<unk>",
|
| 52 |
+
"use_default_system_prompt": false
|
| 53 |
+
}
|