Dekel Cohen
commited on
Commit
·
3fa10de
1
Parent(s):
c9118b0
new model
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- .gitattributes +1 -0
- config.json +3 -0
- configuration_cohere.py +159 -0
- generation_config.json +3 -0
- model-00001-of-00015_chunk_1.txt.zip +3 -0
- model-00001-of-00015_chunk_2.txt.zip +3 -0
- model-00001-of-00015_chunk_3.txt.zip +3 -0
- model-00001-of-00015_chunk_4.txt.zip +3 -0
- model-00001-of-00015_chunk_5.txt.zip +3 -0
- model-00002-of-00015_chunk_1.txt.zip +3 -0
- model-00002-of-00015_chunk_2.txt.zip +3 -0
- model-00002-of-00015_chunk_3.txt.zip +3 -0
- model-00002-of-00015_chunk_4.txt.zip +3 -0
- model-00002-of-00015_chunk_5.txt.zip +3 -0
- model-00003-of-00015_chunk_1.txt.zip +3 -0
- model-00003-of-00015_chunk_2.txt.zip +3 -0
- model-00003-of-00015_chunk_3.txt.zip +3 -0
- model-00003-of-00015_chunk_4.txt.zip +3 -0
- model-00003-of-00015_chunk_5.txt.zip +3 -0
- model-00004-of-00015_chunk_1.txt.zip +3 -0
- model-00004-of-00015_chunk_2.txt.zip +3 -0
- model-00004-of-00015_chunk_3.txt.zip +3 -0
- model-00004-of-00015_chunk_4.txt.zip +3 -0
- model-00004-of-00015_chunk_5.txt.zip +3 -0
- model-00005-of-00015_chunk_1.txt.zip +3 -0
- model-00005-of-00015_chunk_2.txt.zip +3 -0
- model-00005-of-00015_chunk_3.txt.zip +3 -0
- model-00005-of-00015_chunk_4.txt.zip +3 -0
- model-00005-of-00015_chunk_5.txt.zip +3 -0
- model-00006-of-00015_chunk_1.txt.zip +3 -0
- model-00006-of-00015_chunk_2.txt.zip +3 -0
- model-00006-of-00015_chunk_3.txt.zip +3 -0
- model-00006-of-00015_chunk_4.txt.zip +3 -0
- model-00006-of-00015_chunk_5.txt.zip +3 -0
- model-00007-of-00015_chunk_1.txt.zip +3 -0
- model-00007-of-00015_chunk_2.txt.zip +3 -0
- model-00007-of-00015_chunk_3.txt.zip +3 -0
- model-00007-of-00015_chunk_4.txt.zip +3 -0
- model-00007-of-00015_chunk_5.txt.zip +3 -0
- model-00008-of-00015_chunk_1.txt.zip +3 -0
- model-00008-of-00015_chunk_2.txt.zip +3 -0
- model-00008-of-00015_chunk_3.txt.zip +3 -0
- model-00008-of-00015_chunk_4.txt.zip +3 -0
- model-00008-of-00015_chunk_5.txt.zip +3 -0
- model-00009-of-00015_chunk_1.txt.zip +3 -0
- model-00009-of-00015_chunk_2.txt.zip +3 -0
- model-00009-of-00015_chunk_3.txt.zip +3 -0
- model-00009-of-00015_chunk_4.txt.zip +3 -0
- model-00009-of-00015_chunk_5.txt.zip +3 -0
- model-00010-of-00015_chunk_1.txt.zip +3 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zip 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
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config.json
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configuration_cohere.py
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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.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# 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
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# limitations under the License.
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""" Cohere model configuration"""
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from transformers import PretrainedConfig, AutoConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class CohereConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`CohereModel`]. It is used to instantiate an Cohere
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model according to the specified arguments, defining the model architecture.
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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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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`.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 8192):
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The maximum sequence length that this model might ever be used with.
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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.
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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).
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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rope_theta (`float`, *optional*, defaults to 10000.0):
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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.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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```python
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>>> from transformers import CohereModel, CohereConfig
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>>> # Initializing a Cohere model configuration
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>>> configuration = CohereConfig()
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>>> # Initializing a model from the Cohere configuration
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>>> model = CohereModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "cohere"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=256000,
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hidden_size=8192,
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intermediate_size=22528,
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num_hidden_layers=40,
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num_attention_heads=64,
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num_key_value_heads=None,
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hidden_act="silu",
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max_position_embeddings=8192,
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initializer_range=0.02,
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layer_norm_eps=1e-5,
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use_cache=True,
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pad_token_id=0,
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bos_token_id=5,
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eos_token_id=255001,
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pretraining_tp=1,
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tie_word_embeddings=True,
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rope_theta=10000.0,
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attention_bias=False,
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attention_dropout=0.0,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.pretraining_tp = pretraining_tp
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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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,
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**kwargs,
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)
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# register the model config to AutoConfig
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AutoConfig.register("cohere", CohereConfig)
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generation_config.json
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version https://git-lfs.github.com/spec/v1
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model-00001-of-00015_chunk_1.txt.zip
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model-00001-of-00015_chunk_3.txt.zip
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model-00001-of-00015_chunk_4.txt.zip
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model-00002-of-00015_chunk_4.txt.zip
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model-00003-of-00015_chunk_1.txt.zip
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