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MiniCPM4-8B-mlx

Text Generation
Transformers
Safetensors
Chinese
English
minicpm4
conversational
custom_code
4-bit precision
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Files changed (12) hide show
  1. added_tokens.json +10 -0
  2. chat_template.jinja +4 -0
  3. config.json +182 -0
  4. configuration_minicpm.py +203 -0
  5. generation_config.json +12 -0
  6. model.safetensors +3 -0
  7. model.safetensors.index.json +751 -0
  8. modeling_minicpm.py +0 -0
  9. special_tokens_map.json +33 -0
  10. tokenizer.json +0 -0
  11. tokenizer.model +3 -0
  12. tokenizer_config.json +117 -0
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+ {
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+ "<|fim_prefix|>": 73445,
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+ "<|im_end|>": 73440,
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+ "<|tool_call|>": 73442
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+ }
chat_template.jinja ADDED
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+ {% for message in messages %}{{'<|im_start|>' + message['role'] + '
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+ ' + message['content'] + '<|im_end|>' + '
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+ '}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
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+ ' }}{% endif %}
config.json ADDED
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+ {
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+ "architectures": [
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+ "MiniCPMForCausalLM"
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+ ],
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+ "auto_map": {
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+ "AutoConfig": "configuration_minicpm.MiniCPMConfig",
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+ "AutoModel": "modeling_minicpm.MiniCPMModel",
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+ "AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
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+ "AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
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+ }
configuration_minicpm.py ADDED
@@ -0,0 +1,203 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2025 The OpenBMB Team. All rights reserved.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ """ MiniCPM model configuration"""
16
+
17
+ from transformers.configuration_utils import PretrainedConfig
18
+ from transformers.utils import logging
19
+
20
+ logger = logging.get_logger(__name__)
21
+
22
+ MINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
23
+
24
+
25
+ class MiniCPMConfig(PretrainedConfig):
26
+ r"""
27
+ This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM
28
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
29
+ defaults will yield a similar configuration to that of the MiniCPM-7B.
30
+
31
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
32
+ documentation from [`PretrainedConfig`] for more information.
33
+
34
+
35
+ Args:
36
+ vocab_size (`int`, *optional*, defaults to 32000):
37
+ Vocabulary size of the MiniCPM model. Defines the number of different tokens that can be represented by the
38
+ `inputs_ids` passed when calling [`MiniCPMModel`]
39
+ hidden_size (`int`, *optional*, defaults to 4096):
40
+ Dimension of the hidden representations.
41
+ intermediate_size (`int`, *optional*, defaults to 11008):
42
+ Dimension of the MLP representations.
43
+ num_hidden_layers (`int`, *optional*, defaults to 32):
44
+ Number of hidden layers in the Transformer decoder.
45
+ num_attention_heads (`int`, *optional*, defaults to 32):
46
+ Number of attention heads for each attention layer in the Transformer decoder.
47
+ num_key_value_heads (`int`, *optional*):
48
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
49
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
50
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
51
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
52
+ by meanpooling all the original heads within that group. For more details checkout [this
53
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
54
+ `num_attention_heads`.
55
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
56
+ The non-linear activation function (function or string) in the decoder.
57
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
58
+ The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens,
59
+ MiniCPM 2 up to 4096, CodeMiniCPM up to 16384.
60
+ initializer_range (`float`, *optional*, defaults to 0.02):
61
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
62
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
63
+ The epsilon used by the rms normalization layers.
64
+ use_cache (`bool`, *optional*, defaults to `True`):
65
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
66
+ relevant if `config.is_decoder=True`.
67
+ pad_token_id (`int`, *optional*):
68
+ Padding token id.
69
+ bos_token_id (`int`, *optional*, defaults to 1):
70
+ Beginning of stream token id.
71
+ eos_token_id (`int`, *optional*, defaults to 2):
72
+ End of stream token id.
73
+ pretraining_tp (`int`, *optional*, defaults to 1):
74
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
75
+ document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
76
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
77
+ issue](https://github.com/pytorch/pytorch/issues/76232).
78
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
79
+ Whether to tie weight embeddings
80
+ rope_theta (`float`, *optional*, defaults to 10000.0):
81
+ The base period of the RoPE embeddings.
82
+ rope_scaling (`Dict`, *optional*):
83
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
84
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
85
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
86
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
87
+ these scaling strategies behave:
88
+ https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
89
+ experimental feature, subject to breaking API changes in future versions.
90
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
91
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
92
+ attention_dropout (`float`, *optional*, defaults to 0.0):
93
+ The dropout ratio for the attention probabilities.
94
+
95
+ ```python
96
+ >>> from transformers import MiniCPMModel, MiniCPMConfig
97
+
98
+ >>> # Initializing a MiniCPM minicpm-7b style configuration
99
+ >>> configuration = MiniCPMConfig()
100
+
101
+ >>> # Initializing a model from the minicpm-7b style configuration
102
+ >>> model = MiniCPMModel(configuration)
103
+
104
+ >>> # Accessing the model configuration
105
+ >>> configuration = model.config
106
+ ```"""
107
+
108
+ model_type = 'minicpm'
109
+ keys_to_ignore_at_inference = ['past_key_values']
110
+
111
+ def __init__(
112
+ self,
113
+ vocab_size=32000,
114
+ hidden_size=4096,
115
+ intermediate_size=11008,
116
+ num_hidden_layers=32,
117
+ num_attention_heads=32,
118
+ num_key_value_heads=None,
119
+ hidden_act='silu',
120
+ max_position_embeddings=2048,
121
+ initializer_range=0.02,
122
+ rms_norm_eps=1e-6,
123
+ use_cache=True,
124
+ pad_token_id=None,
125
+ bos_token_id=1,
126
+ eos_token_id=2,
127
+ pretraining_tp=1,
128
+ tie_word_embeddings=True,
129
+ rope_theta=10000.0,
130
+ rope_scaling=None,
131
+ attention_bias=False,
132
+ attention_dropout=0.0,
133
+ scale_emb=1,
134
+ dim_model_base=1,
135
+ scale_depth=1,
136
+ mup_denominator=32,
137
+ sparse_config=None,
138
+ **kwargs):
139
+
140
+ self.vocab_size = vocab_size
141
+ self.max_position_embeddings = max_position_embeddings
142
+ self.hidden_size = hidden_size
143
+ self.intermediate_size = intermediate_size
144
+ self.num_hidden_layers = num_hidden_layers
145
+ self.num_attention_heads = num_attention_heads
146
+
147
+ # for backward compatibility
148
+ if num_key_value_heads is None:
149
+ num_key_value_heads = num_attention_heads
150
+
151
+ self.num_key_value_heads = num_key_value_heads
152
+ self.hidden_act = hidden_act
153
+ self.initializer_range = initializer_range
154
+ self.rms_norm_eps = rms_norm_eps
155
+ self.pretraining_tp = pretraining_tp
156
+ self.use_cache = use_cache
157
+ self.rope_theta = rope_theta
158
+ self.rope_scaling = rope_scaling
159
+ # self._rope_scaling_validation()
160
+ self.attention_bias = attention_bias
161
+ self.attention_dropout = attention_dropout
162
+ self.scale_emb = scale_emb
163
+ self.dim_model_base = dim_model_base
164
+ self.scale_depth = scale_depth
165
+ # only used for Eagle Head
166
+ self.mup_denominator = mup_denominator
167
+
168
+ # sparse config
169
+ self.sparse_config = sparse_config
170
+
171
+ super().__init__(
172
+ pad_token_id=pad_token_id,
173
+ bos_token_id=bos_token_id,
174
+ eos_token_id=eos_token_id,
175
+ tie_word_embeddings=tie_word_embeddings,
176
+ **kwargs,
177
+ )
178
+ try:
179
+ import flash_attn
180
+ self._attn_implementation = 'flash_attention_2'
181
+ except:
182
+ pass
183
+
184
+ def _rope_scaling_validation(self):
185
+ """
186
+ Validate the `rope_scaling` configuration.
187
+ """
188
+ if self.rope_scaling is None:
189
+ return
190
+
191
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
192
+ raise ValueError(
193
+ '`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, '
194
+ f'got {self.rope_scaling}'
195
+ )
196
+ rope_scaling_type = self.rope_scaling.get('type', None)
197
+ rope_scaling_factor = self.rope_scaling.get('factor', None)
198
+ if rope_scaling_type is None or rope_scaling_type not in ['linear', 'dynamic']:
199
+ raise ValueError(
200
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
201
+ )
202
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
203
+ raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
generation_config.json ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "bos_token_id": 1,
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+ "do_sample": true,
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+ "pad_token_id": 2,
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+ "temperature": 0.8,
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+ "top_p": 0.8,
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+ "transformers_version": "4.46.1"
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+ }
model.safetensors ADDED
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