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Add files using upload-large-folder tool

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  1. README.md +8 -0
  2. chat_template.jinja +14 -0
  3. config.json +66 -0
  4. configuration_deepseek.py +199 -0
  5. generation_config.json +9 -0
  6. model-00001-of-00088.safetensors +3 -0
  7. model-00002-of-00088.safetensors +3 -0
  8. model-00003-of-00088.safetensors +3 -0
  9. model-00004-of-00088.safetensors +3 -0
  10. model-00005-of-00088.safetensors +3 -0
  11. model-00006-of-00088.safetensors +3 -0
  12. model-00007-of-00088.safetensors +3 -0
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  15. model-00010-of-00088.safetensors +3 -0
  16. model-00011-of-00088.safetensors +3 -0
  17. model-00012-of-00088.safetensors +3 -0
  18. model-00013-of-00088.safetensors +3 -0
  19. model-00014-of-00088.safetensors +3 -0
  20. model-00015-of-00088.safetensors +3 -0
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README.md ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ library_name: mlx
4
+ base_model: deepseek-ai/DeepSeek-R1-0528
5
+ tags:
6
+ - mlx
7
+ pipeline_tag: text-generation
8
+ ---
chat_template.jinja ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='', is_first_sp=true, is_last_user=false) %}{%- for message in messages %}{%- if message['role'] == 'system' %}{%- if ns.is_first_sp %}{% set ns.system_prompt = ns.system_prompt + message['content'] %}{% set ns.is_first_sp = false %}{%- else %}{% set ns.system_prompt = ns.system_prompt + '
2
+
3
+ ' + message['content'] %}{%- endif %}{%- endif %}{%- endfor %}{{ bos_token }}{{ ns.system_prompt }}{%- for message in messages %}{% set content = message['content'] %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{%- set ns.is_first = false -%}{%- set ns.is_last_user = true -%}{{'<|User|>' + content + '<|Assistant|>'}}{%- endif %}{%- if message['role'] == 'assistant' %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{% endif %}{%- if message['role'] == 'assistant' and message['tool_calls'] is defined and message['tool_calls'] is not none %}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{'<|tool▁outputs▁end|>'}}{%- endif %}{%- set ns.is_first = false %}{%- set ns.is_tool = false -%}{%- set ns.is_output_first = true %}{%- for tool in message['tool_calls'] %}{%- if not ns.is_first %}{%- if content is none %}{{'<|tool▁calls▁begin|><|tool▁call▁begin|>' + tool['type'] + '<|tool▁sep|>' + tool['function']['name'] + '
4
+ ' + '```json' + '
5
+ ' + tool['function']['arguments'] + '
6
+ ' + '```' + '<|tool▁call▁end|>'}}{%- else %}{{content + '<|tool▁calls▁begin|><|tool▁call▁begin|>' + tool['type'] + '<|tool▁sep|>' + tool['function']['name'] + '
7
+ ' + '```json' + '
8
+ ' + tool['function']['arguments'] + '
9
+ ' + '```' + '<|tool▁call▁end|>'}}{%- endif %}{%- set ns.is_first = true -%}{%- else %}{{'
10
+ ' + '<|tool▁call▁begin|>' + tool['type'] + '<|tool▁sep|>' + tool['function']['name'] + '
11
+ ' + '```json' + '
12
+ ' + tool['function']['arguments'] + '
13
+ ' + '```' + '<|tool▁call▁end|>'}}{%- endif %}{%- endfor %}{{'<|tool▁calls▁end|><|end▁of▁sentence|>'}}{%- endif %}{%- if message['role'] == 'assistant' and (message['tool_calls'] is not defined or message['tool_calls'] is none)%}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{'<|tool▁outputs▁end|>' + content + '<|end▁of▁sentence|>'}}{%- set ns.is_tool = false -%}{%- else %}{{content + '<|end▁of▁sentence|>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_last_user = false -%}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<|tool▁outputs▁begin|><|tool▁output▁begin|>' + content + '<|tool▁output▁end|>'}}{%- set ns.is_output_first = false %}{%- else %}{{'
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+ <|tool▁output▁begin|>' + content + '<|tool▁output▁end|>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<|tool▁outputs▁end|>'}}{% endif %}{% if add_generation_prompt and not ns.is_last_user and not ns.is_tool %}{{'<|Assistant|>'}}{% endif %}
config.json ADDED
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1
+ {
2
+ "architectures": [
3
+ "DeepseekV3ForCausalLM"
4
+ ],
5
+ "attention_bias": false,
6
+ "attention_dropout": 0.0,
7
+ "auto_map": {
8
+ "AutoConfig": "configuration_deepseek.DeepseekV3Config",
9
+ "AutoModel": "modeling_deepseek.DeepseekV3Model",
10
+ "AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"
11
+ },
12
+ "bos_token_id": 0,
13
+ "eos_token_id": 1,
14
+ "ep_size": 1,
15
+ "first_k_dense_replace": 3,
16
+ "hidden_act": "silu",
17
+ "hidden_size": 7168,
18
+ "initializer_range": 0.02,
19
+ "intermediate_size": 18432,
20
+ "kv_lora_rank": 512,
21
+ "max_position_embeddings": 163840,
22
+ "model_type": "deepseek_v3",
23
+ "moe_intermediate_size": 2048,
24
+ "moe_layer_freq": 1,
25
+ "n_group": 8,
26
+ "n_routed_experts": 256,
27
+ "n_shared_experts": 1,
28
+ "norm_topk_prob": true,
29
+ "num_attention_heads": 128,
30
+ "num_experts_per_tok": 8,
31
+ "num_hidden_layers": 61,
32
+ "num_key_value_heads": 128,
33
+ "num_nextn_predict_layers": 1,
34
+ "q_lora_rank": 1536,
35
+ "qk_nope_head_dim": 128,
36
+ "qk_rope_head_dim": 64,
37
+ "quantization": {
38
+ "group_size": 64,
39
+ "bits": 5
40
+ },
41
+ "quantization_config": {
42
+ "group_size": 64,
43
+ "bits": 5
44
+ },
45
+ "rms_norm_eps": 1e-06,
46
+ "rope_scaling": {
47
+ "beta_fast": 32,
48
+ "beta_slow": 1,
49
+ "factor": 40,
50
+ "mscale": 1.0,
51
+ "mscale_all_dim": 1.0,
52
+ "original_max_position_embeddings": 4096,
53
+ "type": "yarn"
54
+ },
55
+ "rope_theta": 10000,
56
+ "routed_scaling_factor": 2.5,
57
+ "scoring_func": "sigmoid",
58
+ "tie_word_embeddings": false,
59
+ "topk_group": 4,
60
+ "topk_method": "noaux_tc",
61
+ "torch_dtype": "bfloat16",
62
+ "transformers_version": "4.46.3",
63
+ "use_cache": true,
64
+ "v_head_dim": 128,
65
+ "vocab_size": 129280
66
+ }
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 DeepseekV3Config(PretrainedConfig):
8
+ r"""
9
+ This is the configuration class to store the configuration of a [`DeepseekV3Model`]. 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-V3.
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 129280):
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 [`DeepseekV3Model`]
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_nextn_predict_layers (`int`, *optional*, defaults to 1):
30
+ Number of nextn predict layers in the DeepSeekV3 Model.
31
+ num_attention_heads (`int`, *optional*, defaults to 32):
32
+ Number of attention heads for each attention layer in the Transformer decoder.
33
+ n_shared_experts (`int`, *optional*, defaults to None):
34
+ Number of shared experts, None means dense model.
35
+ n_routed_experts (`int`, *optional*, defaults to None):
36
+ Number of routed experts, None means dense model.
37
+ routed_scaling_factor (`float`, *optional*, defaults to 1.0):
38
+ Scaling factor or routed experts.
39
+ topk_method (`str`, *optional*, defaults to `gready`):
40
+ Topk method used in routed gate.
41
+ n_group (`int`, *optional*, defaults to None):
42
+ Number of groups for routed experts.
43
+ topk_group (`int`, *optional*, defaults to None):
44
+ Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
45
+ num_experts_per_tok (`int`, *optional*, defaults to None):
46
+ Number of selected experts, None means dense model.
47
+ moe_layer_freq (`int`, *optional*, defaults to 1):
48
+ The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
49
+ first_k_dense_replace (`int`, *optional*, defaults to 0):
50
+ Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
51
+ \--k dense layers--/
52
+ norm_topk_prob (`bool`, *optional*, defaults to False):
53
+ Whether to normalize the weights of the routed experts.
54
+ scoring_func (`str`, *optional*, defaults to 'softmax'):
55
+ Method of computing expert weights.
56
+ aux_loss_alpha (`float`, *optional*, defaults to 0.001):
57
+ Auxiliary loss weight coefficient.
58
+ seq_aux = (`bool`, *optional*, defaults to True):
59
+ Whether to compute the auxiliary loss for each individual sample.
60
+ num_key_value_heads (`int`, *optional*):
61
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
62
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
63
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
64
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
65
+ by meanpooling all the original heads within that group. For more details checkout [this
66
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
67
+ `num_attention_heads`.
68
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
69
+ The non-linear activation function (function or string) in the decoder.
70
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
71
+ The maximum sequence length that this model might ever be used with.
72
+ initializer_range (`float`, *optional*, defaults to 0.02):
73
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
74
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
75
+ The epsilon used by the rms normalization layers.
76
+ use_cache (`bool`, *optional*, defaults to `True`):
77
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
78
+ relevant if `config.is_decoder=True`.
79
+ pad_token_id (`int`, *optional*):
80
+ Padding token id.
81
+ bos_token_id (`int`, *optional*, defaults to 1):
82
+ Beginning of stream token id.
83
+ eos_token_id (`int`, *optional*, defaults to 2):
84
+ End of stream token id.
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.
94
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
95
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
96
+ attention_dropout (`float`, *optional*, defaults to 0.0):
97
+ The dropout ratio for the attention probabilities.
98
+
99
+ ```python
100
+ >>> from transformers import DeepseekV3Model, DeepseekV3Config
101
+
102
+ >>> # Initializing a Deepseek-V3 style configuration
103
+ >>> configuration = DeepseekV3Config()
104
+
105
+ >>> # Accessing the model configuration
106
+ >>> configuration = model.config
107
+ ```"""
108
+
109
+ model_type = "deepseek_v3"
110
+ keys_to_ignore_at_inference = ["past_key_values"]
111
+
112
+ def __init__(
113
+ self,
114
+ vocab_size=129280,
115
+ hidden_size=7168,
116
+ intermediate_size=18432,
117
+ moe_intermediate_size = 2048,
118
+ num_hidden_layers=61,
119
+ num_nextn_predict_layers=1,
120
+ num_attention_heads=128,
121
+ num_key_value_heads=128,
122
+ n_shared_experts = 1,
123
+ n_routed_experts = 256,
124
+ ep_size = 1,
125
+ routed_scaling_factor = 2.5,
126
+ kv_lora_rank = 512,
127
+ q_lora_rank = 1536,
128
+ qk_rope_head_dim = 64,
129
+ v_head_dim = 128,
130
+ qk_nope_head_dim = 128,
131
+ topk_method = 'noaux_tc',
132
+ n_group = 8,
133
+ topk_group = 4,
134
+ num_experts_per_tok = 8,
135
+ moe_layer_freq = 1,
136
+ first_k_dense_replace = 3,
137
+ norm_topk_prob = True,
138
+ scoring_func = 'sigmoid',
139
+ hidden_act="silu",
140
+ max_position_embeddings=4096,
141
+ initializer_range=0.02,
142
+ rms_norm_eps=1e-6,
143
+ use_cache=True,
144
+ pad_token_id=None,
145
+ bos_token_id=0,
146
+ eos_token_id=1,
147
+ tie_word_embeddings=False,
148
+ rope_theta=10000.0,
149
+ rope_scaling=None,
150
+ attention_bias=False,
151
+ attention_dropout=0.0,
152
+ **kwargs,
153
+ ):
154
+ self.vocab_size = vocab_size
155
+ self.max_position_embeddings = max_position_embeddings
156
+ self.hidden_size = hidden_size
157
+ self.intermediate_size = intermediate_size
158
+ self.moe_intermediate_size = moe_intermediate_size
159
+ self.num_hidden_layers = num_hidden_layers
160
+ self.num_nextn_predict_layers = num_nextn_predict_layers
161
+ self.num_attention_heads = num_attention_heads
162
+ self.n_shared_experts = n_shared_experts
163
+ self.n_routed_experts = n_routed_experts
164
+ self.ep_size = ep_size
165
+ self.routed_scaling_factor = routed_scaling_factor
166
+ self.kv_lora_rank = kv_lora_rank
167
+ self.q_lora_rank = q_lora_rank
168
+ self.qk_rope_head_dim = qk_rope_head_dim
169
+ self.v_head_dim = v_head_dim
170
+ self.qk_nope_head_dim = qk_nope_head_dim
171
+ self.topk_method = topk_method
172
+ self.n_group = n_group
173
+ self.topk_group = topk_group
174
+ self.num_experts_per_tok = num_experts_per_tok
175
+ self.moe_layer_freq = moe_layer_freq
176
+ self.first_k_dense_replace = first_k_dense_replace
177
+ self.norm_topk_prob = norm_topk_prob
178
+ self.scoring_func = scoring_func
179
+ # for backward compatibility
180
+ if num_key_value_heads is None:
181
+ num_key_value_heads = num_attention_heads
182
+
183
+ self.num_key_value_heads = num_key_value_heads
184
+ self.hidden_act = hidden_act
185
+ self.initializer_range = initializer_range
186
+ self.rms_norm_eps = rms_norm_eps
187
+ self.use_cache = use_cache
188
+ self.rope_theta = rope_theta
189
+ self.rope_scaling = rope_scaling
190
+ self.attention_bias = attention_bias
191
+ self.attention_dropout = attention_dropout
192
+
193
+ super().__init__(
194
+ pad_token_id=pad_token_id,
195
+ bos_token_id=bos_token_id,
196
+ eos_token_id=eos_token_id,
197
+ tie_word_embeddings=tie_word_embeddings,
198
+ **kwargs,
199
+ )
generation_config.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 0,
4
+ "eos_token_id": 1,
5
+ "do_sample": true,
6
+ "temperature": 0.6,
7
+ "top_p": 0.95,
8
+ "transformers_version": "4.46.3"
9
+ }
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