bowang0911 commited on
Commit
8c3f26d
·
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1 Parent(s): b23c5c6

add 0.6b qat checkpoint

Browse files
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
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+ {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
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+ {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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config.json ADDED
@@ -0,0 +1,70 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
2
+ "architectures": [
3
+ "Qwen3Model"
4
+ ],
5
+ "attention_bias": false,
6
+ "attention_dropout": 0.0,
7
+ "auto_map": {
8
+ "AutoConfig": "configuration_qwen3.Qwen3Config",
9
+ "AutoModel": "perplexity-ai/bidirectional-qwen3-implementation--modeling_qwen3.Qwen3Model",
10
+ "AutoModelForMaskedLM": "modeling_qwen3.Qwen3ForMaskedLM"
11
+ },
12
+ "bos_token_id": 151643,
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+ "dtype": "float32",
14
+ "eos_token_id": 151643,
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+ "head_dim": 128,
16
+ "hidden_act": "silu",
17
+ "hidden_size": 1024,
18
+ "initializer_range": 0.02,
19
+ "intermediate_size": 3072,
20
+ "layer_types": [
21
+ "full_attention",
22
+ "full_attention",
23
+ "full_attention",
24
+ "full_attention",
25
+ "full_attention",
26
+ "full_attention",
27
+ "full_attention",
28
+ "full_attention",
29
+ "full_attention",
30
+ "full_attention",
31
+ "full_attention",
32
+ "full_attention",
33
+ "full_attention",
34
+ "full_attention",
35
+ "full_attention",
36
+ "full_attention",
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+ "full_attention",
38
+ "full_attention",
39
+ "full_attention",
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+ "full_attention",
41
+ "full_attention",
42
+ "full_attention",
43
+ "full_attention",
44
+ "full_attention",
45
+ "full_attention",
46
+ "full_attention",
47
+ "full_attention",
48
+ "full_attention"
49
+ ],
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+ "max_position_embeddings": 32768,
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+ "max_window_layers": 28,
52
+ "mlm_loss_variant": "elbo_normalize",
53
+ "model_type": "qwen3",
54
+ "num_attention_heads": 16,
55
+ "num_hidden_layers": 28,
56
+ "num_key_value_heads": 8,
57
+ "rms_norm_eps": 1e-06,
58
+ "rope_parameters": {
59
+ "rope_theta": 1000000,
60
+ "rope_type": "default"
61
+ },
62
+ "rope_theta": 1000000,
63
+ "sliding_window": null,
64
+ "tie_word_embeddings": true,
65
+ "transformers_version": "5.0.0.dev0",
66
+ "use_cache": false,
67
+ "use_sliding_window": false,
68
+ "variant": "bidirectional",
69
+ "vocab_size": 151936
70
+ }
configuration_qwen3.py ADDED
@@ -0,0 +1,206 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. 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
+ """Qwen3 model configuration"""
16
+
17
+ from typing import Optional, Literal
18
+
19
+ import warnings
20
+
21
+ from transformers.configuration_utils import PreTrainedConfig, layer_type_validation
22
+ from transformers.modeling_rope_utils import RopeParameters, rope_config_validation, standardize_rope_params
23
+ from transformers.utils import logging
24
+
25
+
26
+ logger = logging.get_logger(__name__)
27
+
28
+
29
+ class Qwen3Config(PreTrainedConfig):
30
+ r"""
31
+ This is the configuration class to store the configuration of a [`Qwen3Model`]. It is used to instantiate a
32
+ Qwen3 model according to the specified arguments, defining the model architecture. Instantiating a configuration
33
+ with the defaults will yield a similar configuration to that of
34
+ Qwen3-8B [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
35
+
36
+ Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
37
+ documentation from [`PreTrainedConfig`] for more information.
38
+
39
+
40
+ Args:
41
+ vocab_size (`int`, *optional*, defaults to 151936):
42
+ Vocabulary size of the Qwen3 model. Defines the number of different tokens that can be represented by the
43
+ `inputs_ids` passed when calling [`Qwen3Model`]
44
+ hidden_size (`int`, *optional*, defaults to 4096):
45
+ Dimension of the hidden representations.
46
+ intermediate_size (`int`, *optional*, defaults to 22016):
47
+ Dimension of the MLP representations.
48
+ num_hidden_layers (`int`, *optional*, defaults to 32):
49
+ Number of hidden layers in the Transformer encoder.
50
+ num_attention_heads (`int`, *optional*, defaults to 32):
51
+ Number of attention heads for each attention layer in the Transformer encoder.
52
+ num_key_value_heads (`int`, *optional*, defaults to 32):
53
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
54
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
55
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
56
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
57
+ by meanpooling all the original heads within that group. For more details, check out [this
58
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
59
+ head_dim (`int`, *optional*, defaults to 128):
60
+ The attention head dimension.
61
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
62
+ The non-linear activation function (function or string) in the decoder.
63
+ max_position_embeddings (`int`, *optional*, defaults to 32768):
64
+ The maximum sequence length that this model might ever be used with.
65
+ initializer_range (`float`, *optional*, defaults to 0.02):
66
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
67
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
68
+ The epsilon used by the rms normalization layers.
69
+ use_cache (`bool`, *optional*, defaults to `True`):
70
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
71
+ relevant if `config.is_decoder=True`.
72
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
73
+ Whether the model's input and output word embeddings should be tied.
74
+ rope_parameters (`RopeParameters`, *optional*):
75
+ Dictionary containing the configuration parameters for the RoPE embeddings. The dictionaty should contain
76
+ a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
77
+ with longer `max_position_embeddings`.
78
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
79
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
80
+ use_sliding_window (`bool`, *optional*, defaults to `False`):
81
+ Whether to use sliding window attention.
82
+ sliding_window (`int`, *optional*, defaults to 4096):
83
+ Sliding window attention (SWA) window size. If not specified, will default to `4096`.
84
+ max_window_layers (`int`, *optional*, defaults to 28):
85
+ The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any
86
+ additional layer afterwards will use SWA (Sliding Window Attention).
87
+ layer_types (`list`, *optional*):
88
+ Attention pattern for each layer.
89
+ attention_dropout (`float`, *optional*, defaults to 0.0):
90
+ The dropout ratio for the attention probabilities.
91
+
92
+ ```python
93
+ >>> from transformers import Qwen3Model, Qwen3Config
94
+
95
+ >>> # Initializing a Qwen3 style configuration
96
+ >>> configuration = Qwen3Config()
97
+
98
+ >>> # Initializing a model from the Qwen3-8B style configuration
99
+ >>> model = Qwen3Model(configuration)
100
+
101
+ >>> # Accessing the model configuration
102
+ >>> configuration = model.config
103
+ ```"""
104
+
105
+ model_type = "qwen3"
106
+ keys_to_ignore_at_inference = ["past_key_values"]
107
+
108
+ # Default tensor parallel plan for base model `Qwen3`
109
+ base_model_tp_plan = {
110
+ "layers.*.self_attn.q_proj": "colwise",
111
+ "layers.*.self_attn.k_proj": "colwise",
112
+ "layers.*.self_attn.v_proj": "colwise",
113
+ "layers.*.self_attn.o_proj": "rowwise",
114
+ "layers.*.mlp.gate_proj": "colwise",
115
+ "layers.*.mlp.up_proj": "colwise",
116
+ "layers.*.mlp.down_proj": "rowwise",
117
+ }
118
+ base_model_pp_plan = {
119
+ "embed_tokens": (["input_ids"], ["inputs_embeds"]),
120
+ "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
121
+ "norm": (["hidden_states"], ["hidden_states"]),
122
+ }
123
+
124
+ def __init__(
125
+ self,
126
+ vocab_size: Optional[int] = 151936,
127
+ hidden_size: Optional[int] = 4096,
128
+ intermediate_size: Optional[int] = 22016,
129
+ num_hidden_layers: Optional[int] = 32,
130
+ num_attention_heads: Optional[int] = 32,
131
+ num_key_value_heads: Optional[int] = 32,
132
+ head_dim: Optional[int] = 128,
133
+ hidden_act: Optional[str] = "silu",
134
+ max_position_embeddings: Optional[int] = 32768,
135
+ initializer_range: Optional[float] = 0.02,
136
+ rms_norm_eps: Optional[int] = 1e-6,
137
+ use_cache: Optional[bool] = True,
138
+ tie_word_embeddings: Optional[bool] = False,
139
+ rope_parameters: Optional[RopeParameters | dict[RopeParameters]] = None,
140
+ attention_bias: Optional[bool] = False,
141
+ use_sliding_window: Optional[bool] = False,
142
+ sliding_window: Optional[int] = 4096,
143
+ max_window_layers: Optional[int] = 28,
144
+ layer_types: Optional[list[str]] = None,
145
+ attention_dropout: Optional[float] = 0.0,
146
+ variant: Literal["causal", "bidirectional", "causal_dropout"] = "causal",
147
+ mlm_loss_variant: Literal["simple", "masked_normalize", "elbo_normalize", "flat_cart"] = "simple",
148
+ **kwargs,
149
+ ):
150
+ self.vocab_size = vocab_size
151
+ self.max_position_embeddings = max_position_embeddings
152
+ self.hidden_size = hidden_size
153
+ self.intermediate_size = intermediate_size
154
+ self.num_hidden_layers = num_hidden_layers
155
+ self.num_attention_heads = num_attention_heads
156
+ self.use_sliding_window = use_sliding_window
157
+ self.sliding_window = sliding_window if self.use_sliding_window else None
158
+ self.max_window_layers = max_window_layers
159
+
160
+ # for backward compatibility
161
+ if num_key_value_heads is None:
162
+ num_key_value_heads = num_attention_heads
163
+
164
+ self.num_key_value_heads = num_key_value_heads
165
+ self.head_dim = head_dim
166
+ self.hidden_act = hidden_act
167
+ self.initializer_range = initializer_range
168
+ self.rms_norm_eps = rms_norm_eps
169
+ self.use_cache = use_cache
170
+ self.attention_bias = attention_bias
171
+ self.attention_dropout = attention_dropout
172
+ # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
173
+ rope_scaling = kwargs.pop("rope_scaling", None)
174
+ self.rope_parameters = rope_scaling or rope_parameters
175
+
176
+ self.layer_types = layer_types
177
+ if self.layer_types is None:
178
+ self.layer_types = [
179
+ "sliding_attention"
180
+ if self.sliding_window is not None and i >= self.max_window_layers
181
+ else "full_attention"
182
+ for i in range(self.num_hidden_layers)
183
+ ]
184
+ layer_type_validation(self.layer_types, self.num_hidden_layers)
185
+
186
+ # Validate the correctness of rotary position embeddings parameters
187
+ rope_theta = kwargs.get("rope_theta", 10000.0)
188
+ standardize_rope_params(self, rope_theta=rope_theta)
189
+ rope_config_validation(self)
190
+
191
+ self.variant = variant
192
+ self.mlm_loss_variant = mlm_loss_variant
193
+
194
+ if mlm_loss_variant not in ["simple", "masked_normalize", "elbo_normalize", "flat_cart"]:
195
+ raise NotImplementedError(f"Loss variant {mlm_loss_variant} unknown")
196
+
197
+ if variant != "causal" and use_cache:
198
+ warnings.warn("Cannot use cache (use_cache) and bidirectional attention (is_causal=False)")
199
+
200
+ super().__init__(
201
+ tie_word_embeddings=tie_word_embeddings,
202
+ **kwargs,
203
+ )
204
+
205
+
206
+ __all__ = ["Qwen3Config"]
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vocab.json ADDED
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