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  1. .gitattributes +1 -0
  2. config.json +3 -0
  3. configuration_cohere.py +159 -0
  4. generation_config.json +3 -0
  5. model-00001-of-00015_chunk_1.txt.zip +3 -0
  6. model-00001-of-00015_chunk_2.txt.zip +3 -0
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  14. model-00002-of-00015_chunk_5.txt.zip +3 -0
  15. model-00003-of-00015_chunk_1.txt.zip +3 -0
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  25. model-00005-of-00015_chunk_1.txt.zip +3 -0
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  28. model-00005-of-00015_chunk_4.txt.zip +3 -0
  29. model-00005-of-00015_chunk_5.txt.zip +3 -0
  30. model-00006-of-00015_chunk_1.txt.zip +3 -0
  31. model-00006-of-00015_chunk_2.txt.zip +3 -0
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  34. model-00006-of-00015_chunk_5.txt.zip +3 -0
  35. model-00007-of-00015_chunk_1.txt.zip +3 -0
  36. model-00007-of-00015_chunk_2.txt.zip +3 -0
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  39. model-00007-of-00015_chunk_5.txt.zip +3 -0
  40. model-00008-of-00015_chunk_1.txt.zip +3 -0
  41. model-00008-of-00015_chunk_2.txt.zip +3 -0
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  43. model-00008-of-00015_chunk_4.txt.zip +3 -0
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  50. model-00010-of-00015_chunk_1.txt.zip +3 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ *.json filter=lfs diff=lfs merge=lfs -text
config.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:eff7add3ca04269e7341d69d1938df15c3408fdf9de227291ef809164f891408
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+ size 897
configuration_cohere.py ADDED
@@ -0,0 +1,159 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # coding=utf-8
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+ # Copyright 2024 Cohere team. All rights reserved.
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+ #
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+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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+ # and OPT implementations in this library. It has been modified from its
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+ # original forms to accommodate minor architectural differences compared
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+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # 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
+ #
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+ # 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.
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+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+ """ Cohere model configuration"""
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+
22
+ from transformers import PretrainedConfig, AutoConfig
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+ from transformers.utils import logging
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+
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+
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+ logger = logging.get_logger(__name__)
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+
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+
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+ class CohereConfig(PretrainedConfig):
30
+ r"""
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+ This is the configuration class to store the configuration of a [`CohereModel`]. It is used to instantiate an Cohere
32
+ model according to the specified arguments, defining the model architecture.
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+
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+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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+ documentation from [`PretrainedConfig`] for more information.
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+
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+
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+ Args:
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+ vocab_size (`int`, *optional*, defaults to 256000):
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+ Vocabulary size of the Cohere model. Defines the number of different tokens that can be represented by the
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+ `inputs_ids` passed when calling [`CohereModel`]
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+ hidden_size (`int`, *optional*, defaults to 8192):
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+ Dimension of the hidden representations.
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+ intermediate_size (`int`, *optional*, defaults to 22528):
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+ Dimension of the MLP representations.
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+ num_hidden_layers (`int`, *optional*, defaults to 40):
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+ Number of hidden layers in the Transformer decoder.
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+ num_attention_heads (`int`, *optional*, defaults to 64):
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+ Number of attention heads for each attention layer in the Transformer decoder.
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+ num_key_value_heads (`int`, *optional*):
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+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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+ by meanpooling all the original heads within that group. For more details checkout [this
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+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
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+ `num_attention_heads`.
58
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
59
+ The non-linear activation function (function or string) in the decoder.
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+ max_position_embeddings (`int`, *optional*, defaults to 8192):
61
+ The maximum sequence length that this model might ever be used with.
62
+ initializer_range (`float`, *optional*, defaults to 0.02):
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+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
64
+ layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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+ The epsilon used by the layer normalization.
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+ use_cache (`bool`, *optional*, defaults to `True`):
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+ Whether or not the model should return the last key/values attentions (not used by all models). Only
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+ relevant if `config.is_decoder=True`.
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+ pad_token_id (`int`, *optional*, defaults to 0):
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+ Padding token id.
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+ bos_token_id (`int`, *optional*, defaults to 5):
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+ Beginning of stream token id.
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+ eos_token_id (`int`, *optional*, defaults to 255001):
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+ End of stream token id.
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+ pretraining_tp (`int`, *optional*, defaults to 1):
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+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
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+ document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
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+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
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+ issue](https://github.com/pytorch/pytorch/issues/76232).
80
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
81
+ Whether to tie weight embeddings
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+ rope_theta (`float`, *optional*, defaults to 10000.0):
83
+ The base period of the RoPE embeddings.
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+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
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+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
86
+ attention_dropout (`float`, *optional*, defaults to 0.0):
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+ The dropout ratio for the attention probabilities.
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+
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+ ```python
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+ >>> from transformers import CohereModel, CohereConfig
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+
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+ >>> # Initializing a Cohere model configuration
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+ >>> configuration = CohereConfig()
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+
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+ >>> # Initializing a model from the Cohere configuration
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+ >>> model = CohereModel(configuration)
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+
98
+ >>> # Accessing the model configuration
99
+ >>> configuration = model.config
100
+ ```"""
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+
102
+ model_type = "cohere"
103
+ keys_to_ignore_at_inference = ["past_key_values"]
104
+
105
+ def __init__(
106
+ self,
107
+ vocab_size=256000,
108
+ hidden_size=8192,
109
+ intermediate_size=22528,
110
+ num_hidden_layers=40,
111
+ num_attention_heads=64,
112
+ num_key_value_heads=None,
113
+ hidden_act="silu",
114
+ max_position_embeddings=8192,
115
+ initializer_range=0.02,
116
+ layer_norm_eps=1e-5,
117
+ use_cache=True,
118
+ pad_token_id=0,
119
+ bos_token_id=5,
120
+ eos_token_id=255001,
121
+ pretraining_tp=1,
122
+ tie_word_embeddings=True,
123
+ rope_theta=10000.0,
124
+ attention_bias=False,
125
+ attention_dropout=0.0,
126
+ **kwargs,
127
+ ):
128
+ self.vocab_size = vocab_size
129
+ self.max_position_embeddings = max_position_embeddings
130
+ self.hidden_size = hidden_size
131
+ self.intermediate_size = intermediate_size
132
+ self.num_hidden_layers = num_hidden_layers
133
+ self.num_attention_heads = num_attention_heads
134
+
135
+ # for backward compatibility
136
+ if num_key_value_heads is None:
137
+ num_key_value_heads = num_attention_heads
138
+
139
+ self.num_key_value_heads = num_key_value_heads
140
+ self.hidden_act = hidden_act
141
+ self.initializer_range = initializer_range
142
+ self.layer_norm_eps = layer_norm_eps
143
+ self.pretraining_tp = pretraining_tp
144
+ self.use_cache = use_cache
145
+ self.rope_theta = rope_theta
146
+ self.attention_bias = attention_bias
147
+ self.attention_dropout = attention_dropout
148
+
149
+ super().__init__(
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+ pad_token_id=pad_token_id,
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+ bos_token_id=bos_token_id,
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+ eos_token_id=eos_token_id,
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+ tie_word_embeddings=tie_word_embeddings,
154
+ **kwargs,
155
+ )
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+
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+
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+ # register the model config to AutoConfig
159
+ AutoConfig.register("cohere", CohereConfig)
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