Feature Extraction
Transformers
Safetensors
diva
custom_code
Helw150 commited on
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547936a
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'Is this a *custom model*?' meme

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+ "connector.layers.26.final_layer_norm.weight": "model-00001-of-00004.safetensors",
740
+ "connector.layers.27.final_layer_norm.weight": "model-00001-of-00004.safetensors",
741
+ "connector.layers.28.final_layer_norm.weight": "model-00001-of-00004.safetensors",
742
+ "connector.layers.29.final_layer_norm.weight": "model-00001-of-00004.safetensors",
743
+ "connector.layers.30.final_layer_norm.weight": "model-00001-of-00004.safetensors",
744
+ "connector.layers.31.final_layer_norm.weight": "model-00001-of-00004.safetensors",
745
+ "connector.layers.0.final_layer_norm.bias": "model-00001-of-00004.safetensors",
746
+ "connector.layers.1.final_layer_norm.bias": "model-00001-of-00004.safetensors",
747
+ "connector.layers.2.final_layer_norm.bias": "model-00001-of-00004.safetensors",
748
+ "connector.layers.3.final_layer_norm.bias": "model-00001-of-00004.safetensors",
749
+ "connector.layers.4.final_layer_norm.bias": "model-00001-of-00004.safetensors",
750
+ "connector.layers.5.final_layer_norm.bias": "model-00001-of-00004.safetensors",
751
+ "connector.layers.6.final_layer_norm.bias": "model-00001-of-00004.safetensors",
752
+ "connector.layers.7.final_layer_norm.bias": "model-00001-of-00004.safetensors",
753
+ "connector.layers.8.final_layer_norm.bias": "model-00001-of-00004.safetensors",
754
+ "connector.layers.9.final_layer_norm.bias": "model-00001-of-00004.safetensors",
755
+ "connector.layers.10.final_layer_norm.bias": "model-00001-of-00004.safetensors",
756
+ "connector.layers.11.final_layer_norm.bias": "model-00001-of-00004.safetensors",
757
+ "connector.layers.12.final_layer_norm.bias": "model-00001-of-00004.safetensors",
758
+ "connector.layers.13.final_layer_norm.bias": "model-00001-of-00004.safetensors",
759
+ "connector.layers.14.final_layer_norm.bias": "model-00001-of-00004.safetensors",
760
+ "connector.layers.15.final_layer_norm.bias": "model-00001-of-00004.safetensors",
761
+ "connector.layers.16.final_layer_norm.bias": "model-00001-of-00004.safetensors",
762
+ "connector.layers.17.final_layer_norm.bias": "model-00001-of-00004.safetensors",
763
+ "connector.layers.18.final_layer_norm.bias": "model-00001-of-00004.safetensors",
764
+ "connector.layers.19.final_layer_norm.bias": "model-00001-of-00004.safetensors",
765
+ "connector.layers.20.final_layer_norm.bias": "model-00001-of-00004.safetensors",
766
+ "connector.layers.21.final_layer_norm.bias": "model-00001-of-00004.safetensors",
767
+ "connector.layers.22.final_layer_norm.bias": "model-00001-of-00004.safetensors",
768
+ "connector.layers.23.final_layer_norm.bias": "model-00001-of-00004.safetensors",
769
+ "connector.layers.24.final_layer_norm.bias": "model-00001-of-00004.safetensors",
770
+ "connector.layers.25.final_layer_norm.bias": "model-00001-of-00004.safetensors",
771
+ "connector.layers.26.final_layer_norm.bias": "model-00001-of-00004.safetensors",
772
+ "connector.layers.27.final_layer_norm.bias": "model-00001-of-00004.safetensors",
773
+ "connector.layers.28.final_layer_norm.bias": "model-00001-of-00004.safetensors",
774
+ "connector.layers.29.final_layer_norm.bias": "model-00001-of-00004.safetensors",
775
+ "connector.layers.30.final_layer_norm.bias": "model-00001-of-00004.safetensors",
776
+ "connector.layers.31.final_layer_norm.bias": "model-00001-of-00004.safetensors",
777
+ "connector.layer_norm.weight": "model-00001-of-00004.safetensors",
778
+ "connector.layer_norm.bias": "model-00001-of-00004.safetensors",
779
+ "connector.embed_tokens.weight": "model-00001-of-00004.safetensors",
780
+ "connector.embed_positions.weight": "model-00001-of-00004.safetensors",
781
+ }
782
+ }
modeling_diva.py ADDED
@@ -0,0 +1,237 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import copy
2
+ import json
3
+ import os
4
+ from typing import Optional, Union
5
+
6
+ import gradio as gr
7
+ import librosa
8
+ import numpy as np
9
+ import torch
10
+ import torch.nn.functional as F
11
+ from datasets import Audio
12
+ from safetensors.torch import load, load_model
13
+ from torch import nn
14
+ from transformers import (
15
+ AutoProcessor,
16
+ AutoTokenizer,
17
+ LlamaForCausalLM,
18
+ PretrainedConfig,
19
+ PreTrainedModel,
20
+ WhisperForConditionalGeneration,
21
+ )
22
+
23
+
24
+ class WhisperConnector(nn.Module):
25
+ def __init__(
26
+ self,
27
+ ):
28
+ super().__init__()
29
+ self.decoder = None
30
+ self.projection = nn.Linear(1280, 4096)
31
+ self.query_tokens = nn.Parameter(torch.randn(448, 1280))
32
+
33
+ def forward(self, x, output_device="cuda:1"):
34
+ bsz = x.shape[0]
35
+ query_tokens = self.query_tokens[None, :, :].expand(bsz, -1, -1)
36
+ virt_whisper_tokens = self.decoder(
37
+ inputs_embeds=query_tokens, encoder_hidden_states=x
38
+ )
39
+ if self.projection.weight.shape[-1] == 5120:
40
+ virtual_tokens = self.projection(virt_whisper_tokens[0].reshape(112, 5120))
41
+ else:
42
+ virtual_tokens = self.projection(virt_whisper_tokens[0])
43
+ return virtual_tokens.to(output_device)
44
+
45
+
46
+ class DiVAModel(PreTrainedModel):
47
+ def __init__(
48
+ self, via_path=None, config_dict={}, device_map=None, speech_encoder_device=None
49
+ ):
50
+ super().__init__(PretrainedConfig.from_dict(config_dict))
51
+ if speech_encoder_device is None:
52
+ speech_encoder_device = "cuda:0"
53
+ whisper = WhisperForConditionalGeneration.from_pretrained(
54
+ "openai/whisper-large-v3"
55
+ )
56
+ connector = WhisperConnector()
57
+ connector.decoder = copy.deepcopy(whisper.model.decoder)
58
+ if via_path is not None:
59
+ with open(via_path, "rb") as f:
60
+ sd = load(f.read())
61
+
62
+ with torch.no_grad():
63
+ connector.query_tokens = nn.Parameter(sd["query_tokens"])
64
+ connector.projection.weight = nn.Parameter(sd["projection.weight"].T)
65
+ connector.projection.bias = nn.Parameter(sd["projection.bias"])
66
+ wsd = {
67
+ key.replace("connector.", ""): sd[key]
68
+ for key in sd
69
+ if key.startswith("connector.")
70
+ }
71
+ connector.decoder.load_state_dict(wsd)
72
+
73
+ if device_map == None:
74
+ num_layers = 32
75
+ num_gpus = 2
76
+ device_map = dict(
77
+ **{"model.embed_tokens": 1, "model.norm": 1, "lm_head": 2},
78
+ **{
79
+ "model.layers." + str(i): 1 + (i // (num_layers // num_gpus))
80
+ for i in range(num_layers)
81
+ },
82
+ )
83
+
84
+ self.connector = connector.to(speech_encoder_device)
85
+ self.whisper_encoder = whisper.model.encoder.to(speech_encoder_device)
86
+ self.llama_decoder = LlamaForCausalLM.from_pretrained(
87
+ "meta-llama/Meta-Llama-3-8B-Instruct",
88
+ device_map=device_map,
89
+ torch_dtype=torch.float16,
90
+ )
91
+ self.processor = AutoProcessor.from_pretrained("openai/whisper-large-v3")
92
+ self.tokenizer = AutoTokenizer.from_pretrained("WillHeld/via-llama")
93
+ self.prefix = torch.tensor([128000, 128006, 882, 128007, 271]).to(
94
+ self.llama_decoder.model.embed_tokens.weight.device
95
+ )
96
+
97
+ self.pre_user_suffix = torch.tensor(
98
+ self.tokenizer.encode(
99
+ "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n"
100
+ )
101
+ ).to(self.llama_decoder.model.embed_tokens.weight.device)
102
+ self.final_header = torch.tensor([128009, 128006, 78191, 128007, 271]).to(
103
+ self.llama_decoder.model.embed_tokens.weight.device
104
+ )
105
+ self.speech_encoder_device = speech_encoder_device
106
+
107
+ @classmethod
108
+ def from_pretrained(
109
+ cls,
110
+ pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
111
+ *model_args,
112
+ config=None,
113
+ cache_dir=None,
114
+ **kwargs,
115
+ ):
116
+ if os.path.isdir(pretrained_model_name_or_path):
117
+ via_path = (
118
+ pretrained_model_name_or_path + "/model-00001-of-00004.safetensors"
119
+ )
120
+ config_path = pretrained_model_name_or_path + "/config.json"
121
+ else:
122
+ # Loading from huggingface repo
123
+ from huggingface_hub import hf_hub_download
124
+
125
+ hf_hub_download(
126
+ repo_id=pretrained_model_name_or_path,
127
+ token=kwargs.get("token", None),
128
+ local_dir=os.path.dirname(__file__),
129
+ )
130
+ via_path = os.path.dirname(__file__) + "/model-00001-of-00004.safetensors"
131
+ config_path = os.path.dirname(__file__) + "/config.json"
132
+ with open(config_path, "r") as f:
133
+ config_dict = json.loads(f.read())
134
+ return cls(
135
+ via_path,
136
+ config_dict,
137
+ kwargs["device_map"] if "device_map" in kwargs else None,
138
+ (
139
+ kwargs["speech_encoder_device"]
140
+ if "speech_encoder_device" in kwargs
141
+ else None
142
+ ),
143
+ )
144
+
145
+ def forward(self, audio, prefix_text_tokens, suffix_text_tokens):
146
+ inputs = self.processor(audio, return_tensors="pt", sampling_rate=16_000)
147
+ input_features = inputs.input_features.to(self.speech_encoder_device)
148
+ hidden_states = self.whisper_encoder(input_features=input_features)[
149
+ "last_hidden_state"
150
+ ]
151
+ virt_tokens = self.connector(
152
+ hidden_states,
153
+ output_device=self.llama_decoder.model.embed_tokens.weight.device,
154
+ ).squeeze()
155
+
156
+ prefix_embed = self.llama_decoder.model.embed_tokens(prefix_text_tokens)
157
+ suffix_embed = self.llama_decoder.model.embed_tokens(suffix_text_tokens)
158
+ inputs_embeds = torch.cat(
159
+ [prefix_embed, virt_tokens, suffix_embed], axis=0
160
+ ).unsqueeze(0)
161
+
162
+ outputs = self.llama_decoder(
163
+ inputs_embeds=inputs_embeds.to(
164
+ self.llama_decoder.model.embed_tokens.weight.device
165
+ ).half(),
166
+ return_dict=True,
167
+ output_hidden_states=True,
168
+ past_key_values=past_key_values,
169
+ )
170
+
171
+ return outputs
172
+
173
+ def generate(
174
+ self, audio, prompt, do_sample=False, logits_processor=None, max_new_tokens=128
175
+ ):
176
+ inputs = self.processor(audio, return_tensors="pt", sampling_rate=16_000)
177
+ input_features = inputs.input_features.to(self.speech_encoder_device)
178
+ hidden_states = self.whisper_encoder(input_features=input_features)[
179
+ "last_hidden_state"
180
+ ]
181
+ virt_tokens = self.connector(
182
+ hidden_states,
183
+ output_device=self.llama_decoder.model.embed_tokens.weight.device,
184
+ ).squeeze()
185
+
186
+ if prompt != None and prompt != "":
187
+ user_prompt_text = torch.tensor(
188
+ self.tokenizer(prompt, add_special_tokens=False)["input_ids"],
189
+ device=self.pre_user_suffix.device,
190
+ )
191
+ prefix = torch.cat(
192
+ [self.pre_user_suffix, user_prompt_text, self.prefix], axis=0
193
+ )
194
+ else:
195
+ prefix = self.prefix
196
+ prefix_embed = self.llama_decoder.model.embed_tokens(prefix)
197
+ suffix = self.final_header
198
+ suffix_embed = self.llama_decoder.model.embed_tokens(suffix)
199
+ inputs_embeds = torch.cat(
200
+ [prefix_embed, virt_tokens, suffix_embed], axis=0
201
+ ).unsqueeze(0)
202
+ outs = []
203
+ outputs = None
204
+ greedy = 1
205
+ i = 0
206
+ while greedy != 128009 and len(outs) < max_new_tokens:
207
+ past_key_values = outputs.past_key_values if outputs else None
208
+ outputs = self.llama_decoder(
209
+ inputs_embeds=inputs_embeds.to(
210
+ self.llama_decoder.model.embed_tokens.weight.device
211
+ ).half(),
212
+ return_dict=True,
213
+ output_hidden_states=True,
214
+ past_key_values=past_key_values,
215
+ )
216
+ next_token_logits = outputs.logits[-1, -1, :]
217
+
218
+ if logits_processor:
219
+ local_outs = torch.tensor(outs) if outs != [] else suffix
220
+ local_outs = local_outs.reshape(1, -1)
221
+ next_token_logits = logits_processor(
222
+ local_outs,
223
+ next_token_logits.reshape(1, -1),
224
+ )
225
+ next_token_logits = next_token_logits.flatten()
226
+ if do_sample:
227
+ logits = next_token_logits / temperature
228
+ probs = F.softmax(logits, dim=-1)
229
+ greedy = torch.multinomial(probs, num_samples=1)[0]
230
+ else:
231
+ greedy = next_token_logits.argmax()
232
+ outs.append(greedy)
233
+ next_embed = self.llama_decoder.model.embed_tokens(greedy.reshape(1, 1))
234
+ inputs_embeds = next_embed
235
+ return self.tokenizer.decode(outs, skip_special_tokens=True).replace(
236
+ "<|eot_id|>", ""
237
+ )
preprocessor_config.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "chunk_length": 30,
3
+ "feature_extractor_type": "WhisperFeatureExtractor",
4
+ "feature_size": 128,
5
+ "hop_length": 160,
6
+ "n_fft": 400,
7
+ "n_samples": 480000,
8
+ "nb_max_frames": 3000,
9
+ "padding_side": "right",
10
+ "padding_value": 0.0,
11
+ "processor_class": "WhisperProcessor",
12
+ "return_attention_mask": false,
13
+ "sampling_rate": 16000
14
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|begin_of_text|>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|eot_id|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "<|reserved_special_token_0|>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ }
23
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1,2064 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "128000": {
4
+ "content": "<|begin_of_text|>",
5
+ "lstrip": false,
6
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+ },
1939
+ "128242": {
1940
+ "content": "<|reserved_special_token_237|>",
1941
+ "lstrip": false,
1942
+ "normalized": false,
1943
+ "rstrip": false,
1944
+ "single_word": false,
1945
+ "special": true
1946
+ },
1947
+ "128243": {
1948
+ "content": "<|reserved_special_token_238|>",
1949
+ "lstrip": false,
1950
+ "normalized": false,
1951
+ "rstrip": false,
1952
+ "single_word": false,
1953
+ "special": true
1954
+ },
1955
+ "128244": {
1956
+ "content": "<|reserved_special_token_239|>",
1957
+ "lstrip": false,
1958
+ "normalized": false,
1959
+ "rstrip": false,
1960
+ "single_word": false,
1961
+ "special": true
1962
+ },
1963
+ "128245": {
1964
+ "content": "<|reserved_special_token_240|>",
1965
+ "lstrip": false,
1966
+ "normalized": false,
1967
+ "rstrip": false,
1968
+ "single_word": false,
1969
+ "special": true
1970
+ },
1971
+ "128246": {
1972
+ "content": "<|reserved_special_token_241|>",
1973
+ "lstrip": false,
1974
+ "normalized": false,
1975
+ "rstrip": false,
1976
+ "single_word": false,
1977
+ "special": true
1978
+ },
1979
+ "128247": {
1980
+ "content": "<|reserved_special_token_242|>",
1981
+ "lstrip": false,
1982
+ "normalized": false,
1983
+ "rstrip": false,
1984
+ "single_word": false,
1985
+ "special": true
1986
+ },
1987
+ "128248": {
1988
+ "content": "<|reserved_special_token_243|>",
1989
+ "lstrip": false,
1990
+ "normalized": false,
1991
+ "rstrip": false,
1992
+ "single_word": false,
1993
+ "special": true
1994
+ },
1995
+ "128249": {
1996
+ "content": "<|reserved_special_token_244|>",
1997
+ "lstrip": false,
1998
+ "normalized": false,
1999
+ "rstrip": false,
2000
+ "single_word": false,
2001
+ "special": true
2002
+ },
2003
+ "128250": {
2004
+ "content": "<|reserved_special_token_245|>",
2005
+ "lstrip": false,
2006
+ "normalized": false,
2007
+ "rstrip": false,
2008
+ "single_word": false,
2009
+ "special": true
2010
+ },
2011
+ "128251": {
2012
+ "content": "<|reserved_special_token_246|>",
2013
+ "lstrip": false,
2014
+ "normalized": false,
2015
+ "rstrip": false,
2016
+ "single_word": false,
2017
+ "special": true
2018
+ },
2019
+ "128252": {
2020
+ "content": "<|reserved_special_token_247|>",
2021
+ "lstrip": false,
2022
+ "normalized": false,
2023
+ "rstrip": false,
2024
+ "single_word": false,
2025
+ "special": true
2026
+ },
2027
+ "128253": {
2028
+ "content": "<|reserved_special_token_248|>",
2029
+ "lstrip": false,
2030
+ "normalized": false,
2031
+ "rstrip": false,
2032
+ "single_word": false,
2033
+ "special": true
2034
+ },
2035
+ "128254": {
2036
+ "content": "<|reserved_special_token_249|>",
2037
+ "lstrip": false,
2038
+ "normalized": false,
2039
+ "rstrip": false,
2040
+ "single_word": false,
2041
+ "special": true
2042
+ },
2043
+ "128255": {
2044
+ "content": "<|reserved_special_token_250|>",
2045
+ "lstrip": false,
2046
+ "normalized": false,
2047
+ "rstrip": false,
2048
+ "single_word": false,
2049
+ "special": true
2050
+ }
2051
+ },
2052
+ "bos_token": "<|begin_of_text|>",
2053
+ "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|eot_id|>",
2056
+ "model_input_names": [
2057
+ "input_ids",
2058
+ "attention_mask"
2059
+ ],
2060
+ "model_max_length": 1000000000000000019884624838656,
2061
+ "pad_token": "<|reserved_special_token_0|>",
2062
+ "padding_side": "right",
2063
+ "tokenizer_class": "PreTrainedTokenizerFast"
2064
+ }