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  1. config.json +60 -0
  2. configuration_deepseek.py +206 -0
  3. generation_config.json +9 -0
  4. model-00001-of-000055.safetensors +3 -0
  5. model-00002-of-000055.safetensors +3 -0
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config.json ADDED
@@ -0,0 +1,60 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
2
+ "architectures": [
3
+ "DeepseekV2ForCausalLM"
4
+ ],
5
+ "attention_bias": false,
6
+ "attention_dropout": 0.0,
7
+ "auto_map": {
8
+ "AutoConfig": "configuration_deepseek.DeepseekV2Config",
9
+ "AutoModel": "modeling_deepseek.DeepseekV2Model",
10
+ "AutoModelForCausalLM": "modeling_deepseek.DeepseekV2ForCausalLM"
11
+ },
12
+ "aux_loss_alpha": 0.001,
13
+ "bos_token_id": 100000,
14
+ "eos_token_id": 100001,
15
+ "ep_size": 1,
16
+ "first_k_dense_replace": 1,
17
+ "hidden_act": "silu",
18
+ "hidden_size": 5120,
19
+ "initializer_range": 0.02,
20
+ "intermediate_size": 12288,
21
+ "kv_lora_rank": 512,
22
+ "max_position_embeddings": 163840,
23
+ "model_type": "deepseek_v2",
24
+ "moe_intermediate_size": 1536,
25
+ "moe_layer_freq": 1,
26
+ "n_group": 8,
27
+ "n_routed_experts": 160,
28
+ "n_shared_experts": 2,
29
+ "norm_topk_prob": false,
30
+ "num_attention_heads": 128,
31
+ "num_experts_per_tok": 6,
32
+ "num_hidden_layers": 60,
33
+ "num_key_value_heads": 128,
34
+ "pretraining_tp": 1,
35
+ "q_lora_rank": 1536,
36
+ "qk_nope_head_dim": 128,
37
+ "qk_rope_head_dim": 64,
38
+ "rms_norm_eps": 1e-06,
39
+ "rope_scaling": {
40
+ "beta_fast": 32,
41
+ "beta_slow": 1,
42
+ "factor": 40,
43
+ "mscale": 1.0,
44
+ "mscale_all_dim": 1.0,
45
+ "original_max_position_embeddings": 4096,
46
+ "type": "yarn"
47
+ },
48
+ "rope_theta": 10000,
49
+ "routed_scaling_factor": 16.0,
50
+ "scoring_func": "softmax",
51
+ "seq_aux": true,
52
+ "tie_word_embeddings": false,
53
+ "topk_group": 3,
54
+ "topk_method": "group_limited_greedy",
55
+ "torch_dtype": "bfloat16",
56
+ "transformers_version": "4.39.3",
57
+ "use_cache": true,
58
+ "v_head_dim": 128,
59
+ "vocab_size": 102400
60
+ }
configuration_deepseek.py ADDED
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1
+ from transformers.configuration_utils import PretrainedConfig
2
+ from transformers.utils import logging
3
+
4
+ logger = logging.get_logger(__name__)
5
+
6
+ DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
7
+ class DeepseekV2Config(PretrainedConfig):
8
+ r"""
9
+ This is the configuration class to store the configuration of a [`DeepseekV2Model`]. It is used to instantiate an DeepSeek
10
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
11
+ defaults will yield a similar configuration to that of the DeepSeek-V2.
12
+
13
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
14
+ documentation from [`PretrainedConfig`] for more information.
15
+
16
+
17
+ Args:
18
+ vocab_size (`int`, *optional*, defaults to 102400):
19
+ Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
20
+ `inputs_ids` passed when calling [`DeepseekV2Model`]
21
+ hidden_size (`int`, *optional*, defaults to 4096):
22
+ Dimension of the hidden representations.
23
+ intermediate_size (`int`, *optional*, defaults to 11008):
24
+ Dimension of the MLP representations.
25
+ moe_intermediate_size (`int`, *optional*, defaults to 1407):
26
+ Dimension of the MoE representations.
27
+ num_hidden_layers (`int`, *optional*, defaults to 32):
28
+ Number of hidden layers in the Transformer decoder.
29
+ num_attention_heads (`int`, *optional*, defaults to 32):
30
+ Number of attention heads for each attention layer in the Transformer decoder.
31
+ n_shared_experts (`int`, *optional*, defaults to None):
32
+ Number of shared experts, None means dense model.
33
+ n_routed_experts (`int`, *optional*, defaults to None):
34
+ Number of routed experts, None means dense model.
35
+ routed_scaling_factor (`float`, *optional*, defaults to 1.0):
36
+ Scaling factor or routed experts.
37
+ topk_method (`str`, *optional*, defaults to `gready`):
38
+ Topk method used in routed gate.
39
+ n_group (`int`, *optional*, defaults to None):
40
+ Number of groups for routed experts.
41
+ topk_group (`int`, *optional*, defaults to None):
42
+ Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
43
+ num_experts_per_tok (`int`, *optional*, defaults to None):
44
+ Number of selected experts, None means dense model.
45
+ moe_layer_freq (`int`, *optional*, defaults to 1):
46
+ The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
47
+ first_k_dense_replace (`int`, *optional*, defaults to 0):
48
+ Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
49
+ \--k dense layers--/
50
+ norm_topk_prob (`bool`, *optional*, defaults to False):
51
+ Whether to normalize the weights of the routed experts.
52
+ scoring_func (`str`, *optional*, defaults to 'softmax'):
53
+ Method of computing expert weights.
54
+ aux_loss_alpha (`float`, *optional*, defaults to 0.001):
55
+ Auxiliary loss weight coefficient.
56
+ seq_aux = (`bool`, *optional*, defaults to True):
57
+ Whether to compute the auxiliary loss for each individual sample.
58
+ num_key_value_heads (`int`, *optional*):
59
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
60
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
61
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
62
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
63
+ by meanpooling all the original heads within that group. For more details checkout [this
64
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
65
+ `num_attention_heads`.
66
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
67
+ The non-linear activation function (function or string) in the decoder.
68
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
69
+ The maximum sequence length that this model might ever be used with.
70
+ initializer_range (`float`, *optional*, defaults to 0.02):
71
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
72
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
73
+ The epsilon used by the rms normalization layers.
74
+ use_cache (`bool`, *optional*, defaults to `True`):
75
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
76
+ relevant if `config.is_decoder=True`.
77
+ pad_token_id (`int`, *optional*):
78
+ Padding token id.
79
+ bos_token_id (`int`, *optional*, defaults to 1):
80
+ Beginning of stream token id.
81
+ eos_token_id (`int`, *optional*, defaults to 2):
82
+ End of stream token id.
83
+ pretraining_tp (`int`, *optional*, defaults to 1):
84
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
85
+ document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
86
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
87
+ issue](https://github.com/pytorch/pytorch/issues/76232).
88
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
89
+ Whether to tie weight embeddings
90
+ rope_theta (`float`, *optional*, defaults to 10000.0):
91
+ The base period of the RoPE embeddings.
92
+ rope_scaling (`Dict`, *optional*):
93
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
94
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
95
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
96
+ `max_position_embeddings` to the expected new maximum.
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 DeepseekV2Model, DeepseekV2Config
104
+
105
+ >>> # Initializing a Deepseek-V2 style configuration
106
+ >>> configuration = DeepseekV2Config()
107
+
108
+ >>> # Accessing the model configuration
109
+ >>> configuration = model.config
110
+ ```"""
111
+
112
+ model_type = "deepseek_v2"
113
+ keys_to_ignore_at_inference = ["past_key_values"]
114
+
115
+ def __init__(
116
+ self,
117
+ vocab_size=102400,
118
+ hidden_size=4096,
119
+ intermediate_size=11008,
120
+ moe_intermediate_size = 1407,
121
+ num_hidden_layers=30,
122
+ num_attention_heads=32,
123
+ num_key_value_heads=32,
124
+ n_shared_experts = None,
125
+ n_routed_experts = None,
126
+ ep_size = 1,
127
+ routed_scaling_factor = 1.0,
128
+ kv_lora_rank = 512,
129
+ q_lora_rank = 1536,
130
+ qk_rope_head_dim = 64,
131
+ v_head_dim = 128,
132
+ qk_nope_head_dim = 128,
133
+ topk_method = 'gready',
134
+ n_group = None,
135
+ topk_group = None,
136
+ num_experts_per_tok = None,
137
+ moe_layer_freq = 1,
138
+ first_k_dense_replace = 0,
139
+ norm_topk_prob = False,
140
+ scoring_func = 'softmax',
141
+ aux_loss_alpha = 0.001,
142
+ seq_aux = True,
143
+ hidden_act="silu",
144
+ max_position_embeddings=2048,
145
+ initializer_range=0.02,
146
+ rms_norm_eps=1e-6,
147
+ use_cache=True,
148
+ pad_token_id=None,
149
+ bos_token_id=100000,
150
+ eos_token_id=100001,
151
+ pretraining_tp=1,
152
+ tie_word_embeddings=False,
153
+ rope_theta=10000.0,
154
+ rope_scaling=None,
155
+ attention_bias=False,
156
+ attention_dropout=0.0,
157
+ **kwargs,
158
+ ):
159
+ self.vocab_size = vocab_size
160
+ self.max_position_embeddings = max_position_embeddings
161
+ self.hidden_size = hidden_size
162
+ self.intermediate_size = intermediate_size
163
+ self.moe_intermediate_size = moe_intermediate_size
164
+ self.num_hidden_layers = num_hidden_layers
165
+ self.num_attention_heads = num_attention_heads
166
+ self.n_shared_experts = n_shared_experts
167
+ self.n_routed_experts = n_routed_experts
168
+ self.ep_size = ep_size
169
+ self.routed_scaling_factor = routed_scaling_factor
170
+ self.kv_lora_rank = kv_lora_rank
171
+ self.q_lora_rank = q_lora_rank
172
+ self.qk_rope_head_dim = qk_rope_head_dim
173
+ self.v_head_dim = v_head_dim
174
+ self.qk_nope_head_dim = qk_nope_head_dim
175
+ self.topk_method = topk_method
176
+ self.n_group = n_group
177
+ self.topk_group = topk_group
178
+ self.num_experts_per_tok = num_experts_per_tok
179
+ self.moe_layer_freq = moe_layer_freq
180
+ self.first_k_dense_replace = first_k_dense_replace
181
+ self.norm_topk_prob = norm_topk_prob
182
+ self.scoring_func = scoring_func
183
+ self.aux_loss_alpha = aux_loss_alpha
184
+ self.seq_aux = seq_aux
185
+ # for backward compatibility
186
+ if num_key_value_heads is None:
187
+ num_key_value_heads = num_attention_heads
188
+
189
+ self.num_key_value_heads = num_key_value_heads
190
+ self.hidden_act = hidden_act
191
+ self.initializer_range = initializer_range
192
+ self.rms_norm_eps = rms_norm_eps
193
+ self.pretraining_tp = pretraining_tp
194
+ self.use_cache = use_cache
195
+ self.rope_theta = rope_theta
196
+ self.rope_scaling = rope_scaling
197
+ self.attention_bias = attention_bias
198
+ self.attention_dropout = attention_dropout
199
+
200
+ super().__init__(
201
+ pad_token_id=pad_token_id,
202
+ bos_token_id=bos_token_id,
203
+ eos_token_id=eos_token_id,
204
+ tie_word_embeddings=tie_word_embeddings,
205
+ **kwargs,
206
+ )
generation_config.json ADDED
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+ {
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+ "_from_model_config": true,
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+ "bos_token_id": 100000,
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+ "eos_token_id": 100001,
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+ "do_sample": true,
6
+ "temperature": 0.3,
7
+ "top_p": 0.95,
8
+ "transformers_version": "4.39.3"
9
+ }
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