Hugging Face's logo

rayliuca
/
TRagx-GPTQ-internlm2-7b

Text2Text Generation
Transformers
Safetensors
English
Japanese
Chinese
internlm2
feature-extraction
custom_code
4-bit precision
gptq
Model card Files Community
rayliuca commited on
Commit
5e583b7
•
1 Parent(s): 59d1b68

Upload InternLM2ForCausalLM

Browse files
Files changed (7) hide show
  1. config.json +57 -0
  2. configuration_internlm2.py +151 -0
  3. generation_config.json +7 -0
  4. model-00001-of-00002.safetensors +3 -0
  5. model-00002-of-00002.safetensors +3 -0
  6. model.safetensors.index.json +874 -0
  7. modeling_internlm2.py +1391 -0
config.json ADDED
@@ -0,0 +1,57 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "rayliuca/TRagx-Merged-internlm2-7b",
3
+ "architectures": [
4
+ "InternLM2ForCausalLM"
5
+ ],
6
+ "attn_implementation": "eager",
7
+ "auto_map": {
8
+ "AutoConfig": "configuration_internlm2.InternLM2Config",
9
+ "AutoModel": "internlm/internlm2-7b--modeling_internlm2.InternLM2ForCausalLM",
10
+ "AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM"
11
+ },
12
+ "bias": false,
13
+ "bos_token_id": 1,
14
+ "eos_token_id": 2,
15
+ "hidden_act": "silu",
16
+ "hidden_size": 4096,
17
+ "initializer_range": 0.02,
18
+ "intermediate_size": 14336,
19
+ "max_position_embeddings": 32768,
20
+ "model_type": "internlm2",
21
+ "num_attention_heads": 32,
22
+ "num_hidden_layers": 32,
23
+ "num_key_value_heads": 8,
24
+ "pad_token_id": 2,
25
+ "quantization_config": {
26
+ "batch_size": 1,
27
+ "bits": 4,
28
+ "block_name_to_quantize": null,
29
+ "cache_block_outputs": true,
30
+ "damp_percent": 0.1,
31
+ "dataset": "custom_c4_zh_en_ja",
32
+ "desc_act": false,
33
+ "exllama_config": {
34
+ "version": 1
35
+ },
36
+ "group_size": 128,
37
+ "max_input_length": null,
38
+ "model_seqlen": null,
39
+ "module_name_preceding_first_block": null,
40
+ "modules_in_block_to_quantize": null,
41
+ "pad_token_id": null,
42
+ "quant_method": "gptq",
43
+ "sym": true,
44
+ "tokenizer": null,
45
+ "true_sequential": true,
46
+ "use_cuda_fp16": false,
47
+ "use_exllama": true
48
+ },
49
+ "rms_norm_eps": 1e-05,
50
+ "rope_scaling": null,
51
+ "rope_theta": 1000000,
52
+ "tie_word_embeddings": false,
53
+ "torch_dtype": "float16",
54
+ "transformers_version": "4.38.1",
55
+ "use_cache": true,
56
+ "vocab_size": 92544
57
+ }
configuration_internlm2.py ADDED
@@ -0,0 +1,151 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on transformers/src/transformers/models/llama/configuration_llama.py
5
+ #
6
+ # Licensed under the Apache License, Version 2.0 (the "License");
7
+ # you may not use this file except in compliance with the License.
8
+ # You may obtain a copy of the License at
9
+ #
10
+ # http://www.apache.org/licenses/LICENSE-2.0
11
+ #
12
+ # Unless required by applicable law or agreed to in writing, software
13
+ # distributed under the License is distributed on an "AS IS" BASIS,
14
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15
+ # See the License for the specific language governing permissions and
16
+ # limitations under the License.
17
+ """ InternLM2 model configuration"""
18
+
19
+ from transformers.configuration_utils import PretrainedConfig
20
+ from transformers.utils import logging
21
+
22
+ logger = logging.get_logger(__name__)
23
+
24
+ INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
25
+
26
+
27
+ # Modified from transformers.model.llama.configuration_llama.LlamaConfig
28
+ class InternLM2Config(PretrainedConfig):
29
+ r"""
30
+ This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
31
+ an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
32
+ configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
33
+
34
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
35
+ documentation from [`PretrainedConfig`] for more information.
36
+
37
+
38
+ Args:
39
+ vocab_size (`int`, *optional*, defaults to 32000):
40
+ Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
41
+ `inputs_ids` passed when calling [`InternLM2Model`]
42
+ hidden_size (`int`, *optional*, defaults to 4096):
43
+ Dimension of the hidden representations.
44
+ intermediate_size (`int`, *optional*, defaults to 11008):
45
+ Dimension of the MLP representations.
46
+ num_hidden_layers (`int`, *optional*, defaults to 32):
47
+ Number of hidden layers in the Transformer encoder.
48
+ num_attention_heads (`int`, *optional*, defaults to 32):
49
+ Number of attention heads for each attention layer in the Transformer encoder.
50
+ num_key_value_heads (`int`, *optional*):
51
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
52
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
53
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
54
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
55
+ by meanpooling all the original heads within that group. For more details checkout [this
56
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
57
+ `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.
60
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
61
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
62
+ just in case (e.g., 512 or 1024 or 2048).
63
+ initializer_range (`float`, *optional*, defaults to 0.02):
64
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
65
+ rms_norm_eps (`float`, *optional*, defaults to 1e-12):
66
+ The epsilon used by the rms normalization layers.
67
+ use_cache (`bool`, *optional*, defaults to `True`):
68
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
69
+ relevant if `config.is_decoder=True`.
70
+ tie_word_embeddings(`bool`, *optional*, defaults to `False`):
71
+ Whether to tie weight embeddings
72
+ Example:
73
+
74
+ """
75
+ model_type = "internlm2"
76
+ _auto_class = "AutoConfig"
77
+
78
+ def __init__( # pylint: disable=W0102
79
+ self,
80
+ vocab_size=103168,
81
+ hidden_size=4096,
82
+ intermediate_size=11008,
83
+ num_hidden_layers=32,
84
+ num_attention_heads=32,
85
+ num_key_value_heads=None,
86
+ hidden_act="silu",
87
+ max_position_embeddings=2048,
88
+ initializer_range=0.02,
89
+ rms_norm_eps=1e-6,
90
+ use_cache=True,
91
+ pad_token_id=0,
92
+ bos_token_id=1,
93
+ eos_token_id=2,
94
+ tie_word_embeddings=False,
95
+ bias=True,
96
+ rope_theta=10000,
97
+ rope_scaling=None,
98
+ attn_implementation="eager",
99
+ **kwargs,
100
+ ):
101
+ self.vocab_size = vocab_size
102
+ self.max_position_embeddings = max_position_embeddings
103
+ self.hidden_size = hidden_size
104
+ self.intermediate_size = intermediate_size
105
+ self.num_hidden_layers = num_hidden_layers
106
+ self.num_attention_heads = num_attention_heads
107
+ self.bias = bias
108
+
109
+ if num_key_value_heads is None:
110
+ num_key_value_heads = num_attention_heads
111
+ self.num_key_value_heads = num_key_value_heads
112
+
113
+ self.hidden_act = hidden_act
114
+ self.initializer_range = initializer_range
115
+ self.rms_norm_eps = rms_norm_eps
116
+ self.use_cache = use_cache
117
+ self.rope_theta = rope_theta
118
+ self.rope_scaling = rope_scaling
119
+ self._rope_scaling_validation()
120
+
121
+ self.attn_implementation = attn_implementation
122
+ if self.attn_implementation is None:
123
+ self.attn_implementation = "eager"
124
+ super().__init__(
125
+ pad_token_id=pad_token_id,
126
+ bos_token_id=bos_token_id,
127
+ eos_token_id=eos_token_id,
128
+ tie_word_embeddings=tie_word_embeddings,
129
+ **kwargs,
130
+ )
131
+
132
+ def _rope_scaling_validation(self):
133
+ """
134
+ Validate the `rope_scaling` configuration.
135
+ """
136
+ if self.rope_scaling is None:
137
+ return
138
+
139
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
140
+ raise ValueError(
141
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
142
+ f"got {self.rope_scaling}"
143
+ )
144
+ rope_scaling_type = self.rope_scaling.get("type", None)
145
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
146
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
147
+ raise ValueError(
148
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
149
+ )
150
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor < 1.0:
151
+ raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {rope_scaling_factor}")
generation_config.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 1,
4
+ "eos_token_id": 2,
5
+ "pad_token_id": 2,
6
+ "transformers_version": "4.38.1"
7
+ }
model-00001-of-00002.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5ee0234a224e4f2df327285762c426ff87b1a1ecefb7d5834ef0405596cae52a
3
+ size 4391409568
model-00002-of-00002.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9fb45b455badb6f3b24f773c3565d68fc8ad3bd08a42c014fff6036fffc44ee9
3
+ size 758120576
model.safetensors.index.json ADDED
@@ -0,0 +1,874 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metadata": {
3
+ "total_size": 5149433856
4
+ },
5
+ "weight_map": {
6
+ "model.layers.0.attention.wo.bias": "model-00001-of-00002.safetensors",
7
+ "model.layers.0.attention.wo.g_idx": "model-00001-of-00002.safetensors",
8
+ "model.layers.0.attention.wo.qweight": "model-00001-of-00002.safetensors",
9
+ "model.layers.0.attention.wo.qzeros": "model-00001-of-00002.safetensors",
10
+ "model.layers.0.attention.wo.scales": "model-00001-of-00002.safetensors",
11
+ "model.layers.0.attention.wqkv.bias": "model-00001-of-00002.safetensors",
12
+ "model.layers.0.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
13
+ "model.layers.0.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
14
+ "model.layers.0.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
15
+ "model.layers.0.attention.wqkv.scales": "model-00001-of-00002.safetensors",
16
+ "model.layers.0.attention_norm.weight": "model-00001-of-00002.safetensors",
17
+ "model.layers.0.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
18
+ "model.layers.0.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
19
+ "model.layers.0.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
20
+ "model.layers.0.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
21
+ "model.layers.0.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
22
+ "model.layers.0.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
23
+ "model.layers.0.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
24
+ "model.layers.0.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
25
+ "model.layers.0.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
26
+ "model.layers.0.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
27
+ "model.layers.0.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
28
+ "model.layers.0.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
29
+ "model.layers.0.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
30
+ "model.layers.0.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
31
+ "model.layers.0.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
32
+ "model.layers.0.ffn_norm.weight": "model-00001-of-00002.safetensors",
33
+ "model.layers.1.attention.wo.bias": "model-00001-of-00002.safetensors",
34
+ "model.layers.1.attention.wo.g_idx": "model-00001-of-00002.safetensors",
35
+ "model.layers.1.attention.wo.qweight": "model-00001-of-00002.safetensors",
36
+ "model.layers.1.attention.wo.qzeros": "model-00001-of-00002.safetensors",
37
+ "model.layers.1.attention.wo.scales": "model-00001-of-00002.safetensors",
38
+ "model.layers.1.attention.wqkv.bias": "model-00001-of-00002.safetensors",
39
+ "model.layers.1.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
40
+ "model.layers.1.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
41
+ "model.layers.1.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
42
+ "model.layers.1.attention.wqkv.scales": "model-00001-of-00002.safetensors",
43
+ "model.layers.1.attention_norm.weight": "model-00001-of-00002.safetensors",
44
+ "model.layers.1.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
45
+ "model.layers.1.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
46
+ "model.layers.1.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
47
+ "model.layers.1.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
48
+ "model.layers.1.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
49
+ "model.layers.1.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
50
+ "model.layers.1.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
51
+ "model.layers.1.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
52
+ "model.layers.1.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
53
+ "model.layers.1.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
54
+ "model.layers.1.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
55
+ "model.layers.1.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
56
+ "model.layers.1.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
57
+ "model.layers.1.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
58
+ "model.layers.1.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
59
+ "model.layers.1.ffn_norm.weight": "model-00001-of-00002.safetensors",
60
+ "model.layers.10.attention.wo.bias": "model-00001-of-00002.safetensors",
61
+ "model.layers.10.attention.wo.g_idx": "model-00001-of-00002.safetensors",
62
+ "model.layers.10.attention.wo.qweight": "model-00001-of-00002.safetensors",
63
+ "model.layers.10.attention.wo.qzeros": "model-00001-of-00002.safetensors",
64
+ "model.layers.10.attention.wo.scales": "model-00001-of-00002.safetensors",
65
+ "model.layers.10.attention.wqkv.bias": "model-00001-of-00002.safetensors",
66
+ "model.layers.10.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
67
+ "model.layers.10.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
68
+ "model.layers.10.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
69
+ "model.layers.10.attention.wqkv.scales": "model-00001-of-00002.safetensors",
70
+ "model.layers.10.attention_norm.weight": "model-00001-of-00002.safetensors",
71
+ "model.layers.10.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
72
+ "model.layers.10.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
73
+ "model.layers.10.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
74
+ "model.layers.10.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
75
+ "model.layers.10.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
76
+ "model.layers.10.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
77
+ "model.layers.10.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
78
+ "model.layers.10.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
79
+ "model.layers.10.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
80
+ "model.layers.10.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
81
+ "model.layers.10.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
82
+ "model.layers.10.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
83
+ "model.layers.10.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
84
+ "model.layers.10.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
85
+ "model.layers.10.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
86
+ "model.layers.10.ffn_norm.weight": "model-00001-of-00002.safetensors",
87
+ "model.layers.11.attention.wo.bias": "model-00001-of-00002.safetensors",
88
+ "model.layers.11.attention.wo.g_idx": "model-00001-of-00002.safetensors",
89
+ "model.layers.11.attention.wo.qweight": "model-00001-of-00002.safetensors",
90
+ "model.layers.11.attention.wo.qzeros": "model-00001-of-00002.safetensors",
91
+ "model.layers.11.attention.wo.scales": "model-00001-of-00002.safetensors",
92
+ "model.layers.11.attention.wqkv.bias": "model-00001-of-00002.safetensors",
93
+ "model.layers.11.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
94
+ "model.layers.11.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
95
+ "model.layers.11.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
96
+ "model.layers.11.attention.wqkv.scales": "model-00001-of-00002.safetensors",
97
+ "model.layers.11.attention_norm.weight": "model-00001-of-00002.safetensors",
98
+ "model.layers.11.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
99
+ "model.layers.11.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
100
+ "model.layers.11.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
101
+ "model.layers.11.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
102
+ "model.layers.11.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
103
+ "model.layers.11.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
104
+ "model.layers.11.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
105
+ "model.layers.11.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
106
+ "model.layers.11.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
107
+ "model.layers.11.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
108
+ "model.layers.11.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
109
+ "model.layers.11.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
110
+ "model.layers.11.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
111
+ "model.layers.11.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
112
+ "model.layers.11.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
113
+ "model.layers.11.ffn_norm.weight": "model-00001-of-00002.safetensors",
114
+ "model.layers.12.attention.wo.bias": "model-00001-of-00002.safetensors",
115
+ "model.layers.12.attention.wo.g_idx": "model-00001-of-00002.safetensors",
116
+ "model.layers.12.attention.wo.qweight": "model-00001-of-00002.safetensors",
117
+ "model.layers.12.attention.wo.qzeros": "model-00001-of-00002.safetensors",
118
+ "model.layers.12.attention.wo.scales": "model-00001-of-00002.safetensors",
119
+ "model.layers.12.attention.wqkv.bias": "model-00001-of-00002.safetensors",
120
+ "model.layers.12.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
121
+ "model.layers.12.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
122
+ "model.layers.12.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
123
+ "model.layers.12.attention.wqkv.scales": "model-00001-of-00002.safetensors",
124
+ "model.layers.12.attention_norm.weight": "model-00001-of-00002.safetensors",
125
+ "model.layers.12.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
126
+ "model.layers.12.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
127
+ "model.layers.12.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
128
+ "model.layers.12.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
129
+ "model.layers.12.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
130
+ "model.layers.12.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
131
+ "model.layers.12.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
132
+ "model.layers.12.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
133
+ "model.layers.12.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
134
+ "model.layers.12.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
135
+ "model.layers.12.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
136
+ "model.layers.12.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
137
+ "model.layers.12.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
138
+ "model.layers.12.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
139
+ "model.layers.12.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
140
+ "model.layers.12.ffn_norm.weight": "model-00001-of-00002.safetensors",
141
+ "model.layers.13.attention.wo.bias": "model-00001-of-00002.safetensors",
142
+ "model.layers.13.attention.wo.g_idx": "model-00001-of-00002.safetensors",
143
+ "model.layers.13.attention.wo.qweight": "model-00001-of-00002.safetensors",
144
+ "model.layers.13.attention.wo.qzeros": "model-00001-of-00002.safetensors",
145
+ "model.layers.13.attention.wo.scales": "model-00001-of-00002.safetensors",
146
+ "model.layers.13.attention.wqkv.bias": "model-00001-of-00002.safetensors",
147
+ "model.layers.13.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
148
+ "model.layers.13.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
149
+ "model.layers.13.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
150
+ "model.layers.13.attention.wqkv.scales": "model-00001-of-00002.safetensors",
151
+ "model.layers.13.attention_norm.weight": "model-00001-of-00002.safetensors",
152
+ "model.layers.13.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
153
+ "model.layers.13.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
154
+ "model.layers.13.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
155
+ "model.layers.13.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
156
+ "model.layers.13.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
157
+ "model.layers.13.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
158
+ "model.layers.13.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
159
+ "model.layers.13.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
160
+ "model.layers.13.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
161
+ "model.layers.13.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
162
+ "model.layers.13.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
163
+ "model.layers.13.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
164
+ "model.layers.13.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
165
+ "model.layers.13.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
166
+ "model.layers.13.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
167
+ "model.layers.13.ffn_norm.weight": "model-00001-of-00002.safetensors",
168
+ "model.layers.14.attention.wo.bias": "model-00001-of-00002.safetensors",
169
+ "model.layers.14.attention.wo.g_idx": "model-00001-of-00002.safetensors",
170
+ "model.layers.14.attention.wo.qweight": "model-00001-of-00002.safetensors",
171
+ "model.layers.14.attention.wo.qzeros": "model-00001-of-00002.safetensors",
172
+ "model.layers.14.attention.wo.scales": "model-00001-of-00002.safetensors",
173
+ "model.layers.14.attention.wqkv.bias": "model-00001-of-00002.safetensors",
174
+ "model.layers.14.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
175
+ "model.layers.14.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
176
+ "model.layers.14.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
177
+ "model.layers.14.attention.wqkv.scales": "model-00001-of-00002.safetensors",
178
+ "model.layers.14.attention_norm.weight": "model-00001-of-00002.safetensors",
179
+ "model.layers.14.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
180
+ "model.layers.14.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
181
+ "model.layers.14.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
182
+ "model.layers.14.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
183
+ "model.layers.14.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
184
+ "model.layers.14.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
185
+ "model.layers.14.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
186
+ "model.layers.14.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
187
+ "model.layers.14.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
188
+ "model.layers.14.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
189
+ "model.layers.14.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
190
+ "model.layers.14.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
191
+ "model.layers.14.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
192
+ "model.layers.14.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
193
+ "model.layers.14.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
194
+ "model.layers.14.ffn_norm.weight": "model-00001-of-00002.safetensors",
195
+ "model.layers.15.attention.wo.bias": "model-00001-of-00002.safetensors",
196
+ "model.layers.15.attention.wo.g_idx": "model-00001-of-00002.safetensors",
197
+ "model.layers.15.attention.wo.qweight": "model-00001-of-00002.safetensors",
198
+ "model.layers.15.attention.wo.qzeros": "model-00001-of-00002.safetensors",
199
+ "model.layers.15.attention.wo.scales": "model-00001-of-00002.safetensors",
200
+ "model.layers.15.attention.wqkv.bias": "model-00001-of-00002.safetensors",
201
+ "model.layers.15.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
202
+ "model.layers.15.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
203
+ "model.layers.15.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
204
+ "model.layers.15.attention.wqkv.scales": "model-00001-of-00002.safetensors",
205
+ "model.layers.15.attention_norm.weight": "model-00001-of-00002.safetensors",
206
+ "model.layers.15.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
207
+ "model.layers.15.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
208
+ "model.layers.15.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
209
+ "model.layers.15.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
210
+ "model.layers.15.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
211
+ "model.layers.15.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
212
+ "model.layers.15.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
213
+ "model.layers.15.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
214
+ "model.layers.15.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
215
+ "model.layers.15.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
216
+ "model.layers.15.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
217
+ "model.layers.15.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
218
+ "model.layers.15.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
219
+ "model.layers.15.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
220
+ "model.layers.15.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
221
+ "model.layers.15.ffn_norm.weight": "model-00001-of-00002.safetensors",
222
+ "model.layers.16.attention.wo.bias": "model-00001-of-00002.safetensors",
223
+ "model.layers.16.attention.wo.g_idx": "model-00001-of-00002.safetensors",
224
+ "model.layers.16.attention.wo.qweight": "model-00001-of-00002.safetensors",
225
+ "model.layers.16.attention.wo.qzeros": "model-00001-of-00002.safetensors",
226
+ "model.layers.16.attention.wo.scales": "model-00001-of-00002.safetensors",
227
+ "model.layers.16.attention.wqkv.bias": "model-00001-of-00002.safetensors",
228
+ "model.layers.16.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
229
+ "model.layers.16.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
230
+ "model.layers.16.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
231
+ "model.layers.16.attention.wqkv.scales": "model-00001-of-00002.safetensors",
232
+ "model.layers.16.attention_norm.weight": "model-00001-of-00002.safetensors",
233
+ "model.layers.16.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
234
+ "model.layers.16.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
235
+ "model.layers.16.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
236
+ "model.layers.16.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
237
+ "model.layers.16.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
238
+ "model.layers.16.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
239
+ "model.layers.16.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
240
+ "model.layers.16.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
241
+ "model.layers.16.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
242
+ "model.layers.16.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
243
+ "model.layers.16.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
244
+ "model.layers.16.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
245
+ "model.layers.16.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
246
+ "model.layers.16.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
247
+ "model.layers.16.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
248
+ "model.layers.16.ffn_norm.weight": "model-00001-of-00002.safetensors",
249
+ "model.layers.17.attention.wo.bias": "model-00001-of-00002.safetensors",
250
+ "model.layers.17.attention.wo.g_idx": "model-00001-of-00002.safetensors",
251
+ "model.layers.17.attention.wo.qweight": "model-00001-of-00002.safetensors",
252
+ "model.layers.17.attention.wo.qzeros": "model-00001-of-00002.safetensors",
253
+ "model.layers.17.attention.wo.scales": "model-00001-of-00002.safetensors",
254
+ "model.layers.17.attention.wqkv.bias": "model-00001-of-00002.safetensors",
255
+ "model.layers.17.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
256
+ "model.layers.17.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
257
+ "model.layers.17.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
258
+ "model.layers.17.attention.wqkv.scales": "model-00001-of-00002.safetensors",
259
+ "model.layers.17.attention_norm.weight": "model-00001-of-00002.safetensors",
260
+ "model.layers.17.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
261
+ "model.layers.17.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
262
+ "model.layers.17.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
263
+ "model.layers.17.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
264
+ "model.layers.17.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
265
+ "model.layers.17.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
266
+ "model.layers.17.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
267
+ "model.layers.17.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
268
+ "model.layers.17.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
269
+ "model.layers.17.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
270
+ "model.layers.17.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
271
+ "model.layers.17.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
272
+ "model.layers.17.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
273
+ "model.layers.17.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
274
+ "model.layers.17.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
275
+ "model.layers.17.ffn_norm.weight": "model-00001-of-00002.safetensors",
276
+ "model.layers.18.attention.wo.bias": "model-00001-of-00002.safetensors",
277
+ "model.layers.18.attention.wo.g_idx": "model-00001-of-00002.safetensors",
278
+ "model.layers.18.attention.wo.qweight": "model-00001-of-00002.safetensors",
279
+ "model.layers.18.attention.wo.qzeros": "model-00001-of-00002.safetensors",
280
+ "model.layers.18.attention.wo.scales": "model-00001-of-00002.safetensors",
281
+ "model.layers.18.attention.wqkv.bias": "model-00001-of-00002.safetensors",
282
+ "model.layers.18.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
283
+ "model.layers.18.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
284
+ "model.layers.18.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
285
+ "model.layers.18.attention.wqkv.scales": "model-00001-of-00002.safetensors",
286
+ "model.layers.18.attention_norm.weight": "model-00001-of-00002.safetensors",
287
+ "model.layers.18.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
288
+ "model.layers.18.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
289
+ "model.layers.18.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
290
+ "model.layers.18.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
291
+ "model.layers.18.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
292
+ "model.layers.18.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
293
+ "model.layers.18.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
294
+ "model.layers.18.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
295
+ "model.layers.18.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
296
+ "model.layers.18.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
297
+ "model.layers.18.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
298
+ "model.layers.18.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
299
+ "model.layers.18.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
300
+ "model.layers.18.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
301
+ "model.layers.18.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
302
+ "model.layers.18.ffn_norm.weight": "model-00001-of-00002.safetensors",
303
+ "model.layers.19.attention.wo.bias": "model-00001-of-00002.safetensors",
304
+ "model.layers.19.attention.wo.g_idx": "model-00001-of-00002.safetensors",
305
+ "model.layers.19.attention.wo.qweight": "model-00001-of-00002.safetensors",
306
+ "model.layers.19.attention.wo.qzeros": "model-00001-of-00002.safetensors",
307
+ "model.layers.19.attention.wo.scales": "model-00001-of-00002.safetensors",
308
+ "model.layers.19.attention.wqkv.bias": "model-00001-of-00002.safetensors",
309
+ "model.layers.19.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
310
+ "model.layers.19.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
311
+ "model.layers.19.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
312
+ "model.layers.19.attention.wqkv.scales": "model-00001-of-00002.safetensors",
313
+ "model.layers.19.attention_norm.weight": "model-00001-of-00002.safetensors",
314
+ "model.layers.19.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
315
+ "model.layers.19.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
316
+ "model.layers.19.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
317
+ "model.layers.19.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
318
+ "model.layers.19.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
319
+ "model.layers.19.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
320
+ "model.layers.19.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
321
+ "model.layers.19.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
322
+ "model.layers.19.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
323
+ "model.layers.19.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
324
+ "model.layers.19.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
325
+ "model.layers.19.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
326
+ "model.layers.19.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
327
+ "model.layers.19.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
328
+ "model.layers.19.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
329
+ "model.layers.19.ffn_norm.weight": "model-00001-of-00002.safetensors",
330
+ "model.layers.2.attention.wo.bias": "model-00001-of-00002.safetensors",
331
+ "model.layers.2.attention.wo.g_idx": "model-00001-of-00002.safetensors",
332
+ "model.layers.2.attention.wo.qweight": "model-00001-of-00002.safetensors",
333
+ "model.layers.2.attention.wo.qzeros": "model-00001-of-00002.safetensors",
334
+ "model.layers.2.attention.wo.scales": "model-00001-of-00002.safetensors",
335
+ "model.layers.2.attention.wqkv.bias": "model-00001-of-00002.safetensors",
336
+ "model.layers.2.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
337
+ "model.layers.2.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
338
+ "model.layers.2.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
339
+ "model.layers.2.attention.wqkv.scales": "model-00001-of-00002.safetensors",
340
+ "model.layers.2.attention_norm.weight": "model-00001-of-00002.safetensors",
341
+ "model.layers.2.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
342
+ "model.layers.2.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
343
+ "model.layers.2.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
344
+ "model.layers.2.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
345
+ "model.layers.2.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
346
+ "model.layers.2.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
347
+ "model.layers.2.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
348
+ "model.layers.2.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
349
+ "model.layers.2.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
350
+ "model.layers.2.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
351
+ "model.layers.2.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
352
+ "model.layers.2.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
353
+ "model.layers.2.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
354
+ "model.layers.2.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
355
+ "model.layers.2.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
356
+ "model.layers.2.ffn_norm.weight": "model-00001-of-00002.safetensors",
357
+ "model.layers.20.attention.wo.bias": "model-00001-of-00002.safetensors",
358
+ "model.layers.20.attention.wo.g_idx": "model-00001-of-00002.safetensors",
359
+ "model.layers.20.attention.wo.qweight": "model-00001-of-00002.safetensors",
360
+ "model.layers.20.attention.wo.qzeros": "model-00001-of-00002.safetensors",
361
+ "model.layers.20.attention.wo.scales": "model-00001-of-00002.safetensors",
362
+ "model.layers.20.attention.wqkv.bias": "model-00001-of-00002.safetensors",
363
+ "model.layers.20.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
364
+ "model.layers.20.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
365
+ "model.layers.20.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
366
+ "model.layers.20.attention.wqkv.scales": "model-00001-of-00002.safetensors",
367
+ "model.layers.20.attention_norm.weight": "model-00001-of-00002.safetensors",
368
+ "model.layers.20.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
369
+ "model.layers.20.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
370
+ "model.layers.20.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
371
+ "model.layers.20.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
372
+ "model.layers.20.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
373
+ "model.layers.20.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
374
+ "model.layers.20.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
375
+ "model.layers.20.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
376
+ "model.layers.20.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
377
+ "model.layers.20.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
378
+ "model.layers.20.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
379
+ "model.layers.20.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
380
+ "model.layers.20.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
381
+ "model.layers.20.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
382
+ "model.layers.20.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
383
+ "model.layers.20.ffn_norm.weight": "model-00001-of-00002.safetensors",
384
+ "model.layers.21.attention.wo.bias": "model-00001-of-00002.safetensors",
385
+ "model.layers.21.attention.wo.g_idx": "model-00001-of-00002.safetensors",
386
+ "model.layers.21.attention.wo.qweight": "model-00001-of-00002.safetensors",
387
+ "model.layers.21.attention.wo.qzeros": "model-00001-of-00002.safetensors",
388
+ "model.layers.21.attention.wo.scales": "model-00001-of-00002.safetensors",
389
+ "model.layers.21.attention.wqkv.bias": "model-00001-of-00002.safetensors",
390
+ "model.layers.21.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
391
+ "model.layers.21.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
392
+ "model.layers.21.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
393
+ "model.layers.21.attention.wqkv.scales": "model-00001-of-00002.safetensors",
394
+ "model.layers.21.attention_norm.weight": "model-00001-of-00002.safetensors",
395
+ "model.layers.21.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
396
+ "model.layers.21.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
397
+ "model.layers.21.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
398
+ "model.layers.21.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
399
+ "model.layers.21.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
400
+ "model.layers.21.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
401
+ "model.layers.21.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
402
+ "model.layers.21.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
403
+ "model.layers.21.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
404
+ "model.layers.21.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
405
+ "model.layers.21.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
406
+ "model.layers.21.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
407
+ "model.layers.21.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
408
+ "model.layers.21.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
409
+ "model.layers.21.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
410
+ "model.layers.21.ffn_norm.weight": "model-00001-of-00002.safetensors",
411
+ "model.layers.22.attention.wo.bias": "model-00001-of-00002.safetensors",
412
+ "model.layers.22.attention.wo.g_idx": "model-00001-of-00002.safetensors",
413
+ "model.layers.22.attention.wo.qweight": "model-00001-of-00002.safetensors",
414
+ "model.layers.22.attention.wo.qzeros": "model-00001-of-00002.safetensors",
415
+ "model.layers.22.attention.wo.scales": "model-00001-of-00002.safetensors",
416
+ "model.layers.22.attention.wqkv.bias": "model-00001-of-00002.safetensors",
417
+ "model.layers.22.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
418
+ "model.layers.22.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
419
+ "model.layers.22.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
420
+ "model.layers.22.attention.wqkv.scales": "model-00001-of-00002.safetensors",
421
+ "model.layers.22.attention_norm.weight": "model-00001-of-00002.safetensors",
422
+ "model.layers.22.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
423
+ "model.layers.22.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
424
+ "model.layers.22.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
425
+ "model.layers.22.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
426
+ "model.layers.22.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
427
+ "model.layers.22.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
428
+ "model.layers.22.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
429
+ "model.layers.22.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
430
+ "model.layers.22.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
431
+ "model.layers.22.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
432
+ "model.layers.22.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
433
+ "model.layers.22.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
434
+ "model.layers.22.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
435
+ "model.layers.22.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
436
+ "model.layers.22.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
437
+ "model.layers.22.ffn_norm.weight": "model-00001-of-00002.safetensors",
438
+ "model.layers.23.attention.wo.bias": "model-00001-of-00002.safetensors",
439
+ "model.layers.23.attention.wo.g_idx": "model-00001-of-00002.safetensors",
440
+ "model.layers.23.attention.wo.qweight": "model-00001-of-00002.safetensors",
441
+ "model.layers.23.attention.wo.qzeros": "model-00001-of-00002.safetensors",
442
+ "model.layers.23.attention.wo.scales": "model-00001-of-00002.safetensors",
443
+ "model.layers.23.attention.wqkv.bias": "model-00001-of-00002.safetensors",
444
+ "model.layers.23.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
445
+ "model.layers.23.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
446
+ "model.layers.23.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
447
+ "model.layers.23.attention.wqkv.scales": "model-00001-of-00002.safetensors",
448
+ "model.layers.23.attention_norm.weight": "model-00001-of-00002.safetensors",
449
+ "model.layers.23.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
450
+ "model.layers.23.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
451
+ "model.layers.23.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
452
+ "model.layers.23.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
453
+ "model.layers.23.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
454
+ "model.layers.23.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
455
+ "model.layers.23.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
456
+ "model.layers.23.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
457
+ "model.layers.23.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
458
+ "model.layers.23.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
459
+ "model.layers.23.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
460
+ "model.layers.23.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
461
+ "model.layers.23.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
462
+ "model.layers.23.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
463
+ "model.layers.23.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
464
+ "model.layers.23.ffn_norm.weight": "model-00001-of-00002.safetensors",
465
+ "model.layers.24.attention.wo.bias": "model-00001-of-00002.safetensors",
466
+ "model.layers.24.attention.wo.g_idx": "model-00001-of-00002.safetensors",
467
+ "model.layers.24.attention.wo.qweight": "model-00001-of-00002.safetensors",
468
+ "model.layers.24.attention.wo.qzeros": "model-00001-of-00002.safetensors",
469
+ "model.layers.24.attention.wo.scales": "model-00001-of-00002.safetensors",
470
+ "model.layers.24.attention.wqkv.bias": "model-00001-of-00002.safetensors",
471
+ "model.layers.24.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
472
+ "model.layers.24.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
473
+ "model.layers.24.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
474
+ "model.layers.24.attention.wqkv.scales": "model-00001-of-00002.safetensors",
475
+ "model.layers.24.attention_norm.weight": "model-00001-of-00002.safetensors",
476
+ "model.layers.24.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
477
+ "model.layers.24.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
478
+ "model.layers.24.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
479
+ "model.layers.24.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
480
+ "model.layers.24.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
481
+ "model.layers.24.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
482
+ "model.layers.24.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
483
+ "model.layers.24.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
484
+ "model.layers.24.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
485
+ "model.layers.24.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
486
+ "model.layers.24.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
487
+ "model.layers.24.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
488
+ "model.layers.24.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
489
+ "model.layers.24.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
490
+ "model.layers.24.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
491
+ "model.layers.24.ffn_norm.weight": "model-00001-of-00002.safetensors",
492
+ "model.layers.25.attention.wo.bias": "model-00001-of-00002.safetensors",
493
+ "model.layers.25.attention.wo.g_idx": "model-00001-of-00002.safetensors",
494
+ "model.layers.25.attention.wo.qweight": "model-00001-of-00002.safetensors",
495
+ "model.layers.25.attention.wo.qzeros": "model-00001-of-00002.safetensors",
496
+ "model.layers.25.attention.wo.scales": "model-00001-of-00002.safetensors",
497
+ "model.layers.25.attention.wqkv.bias": "model-00001-of-00002.safetensors",
498
+ "model.layers.25.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
499
+ "model.layers.25.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
500
+ "model.layers.25.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
501
+ "model.layers.25.attention.wqkv.scales": "model-00001-of-00002.safetensors",
502
+ "model.layers.25.attention_norm.weight": "model-00001-of-00002.safetensors",
503
+ "model.layers.25.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
504
+ "model.layers.25.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
505
+ "model.layers.25.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
506
+ "model.layers.25.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
507
+ "model.layers.25.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
508
+ "model.layers.25.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
509
+ "model.layers.25.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
510
+ "model.layers.25.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
511
+ "model.layers.25.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
512
+ "model.layers.25.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
513
+ "model.layers.25.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
514
+ "model.layers.25.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
515
+ "model.layers.25.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
516
+ "model.layers.25.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
517
+ "model.layers.25.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
518
+ "model.layers.25.ffn_norm.weight": "model-00001-of-00002.safetensors",
519
+ "model.layers.26.attention.wo.bias": "model-00001-of-00002.safetensors",
520
+ "model.layers.26.attention.wo.g_idx": "model-00001-of-00002.safetensors",
521
+ "model.layers.26.attention.wo.qweight": "model-00001-of-00002.safetensors",
522
+ "model.layers.26.attention.wo.qzeros": "model-00001-of-00002.safetensors",
523
+ "model.layers.26.attention.wo.scales": "model-00001-of-00002.safetensors",
524
+ "model.layers.26.attention.wqkv.bias": "model-00001-of-00002.safetensors",
525
+ "model.layers.26.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
526
+ "model.layers.26.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
527
+ "model.layers.26.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
528
+ "model.layers.26.attention.wqkv.scales": "model-00001-of-00002.safetensors",
529
+ "model.layers.26.attention_norm.weight": "model-00001-of-00002.safetensors",
530
+ "model.layers.26.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
531
+ "model.layers.26.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
532
+ "model.layers.26.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
533
+ "model.layers.26.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
534
+ "model.layers.26.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
535
+ "model.layers.26.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
536
+ "model.layers.26.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
537
+ "model.layers.26.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
538
+ "model.layers.26.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
539
+ "model.layers.26.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
540
+ "model.layers.26.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
541
+ "model.layers.26.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
542
+ "model.layers.26.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
543
+ "model.layers.26.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
544
+ "model.layers.26.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
545
+ "model.layers.26.ffn_norm.weight": "model-00001-of-00002.safetensors",
546
+ "model.layers.27.attention.wo.bias": "model-00001-of-00002.safetensors",
547
+ "model.layers.27.attention.wo.g_idx": "model-00001-of-00002.safetensors",
548
+ "model.layers.27.attention.wo.qweight": "model-00001-of-00002.safetensors",
549
+ "model.layers.27.attention.wo.qzeros": "model-00001-of-00002.safetensors",
550
+ "model.layers.27.attention.wo.scales": "model-00001-of-00002.safetensors",
551
+ "model.layers.27.attention.wqkv.bias": "model-00001-of-00002.safetensors",
552
+ "model.layers.27.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
553
+ "model.layers.27.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
554
+ "model.layers.27.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
555
+ "model.layers.27.attention.wqkv.scales": "model-00001-of-00002.safetensors",
556
+ "model.layers.27.attention_norm.weight": "model-00001-of-00002.safetensors",
557
+ "model.layers.27.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
558
+ "model.layers.27.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
559
+ "model.layers.27.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
560
+ "model.layers.27.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
561
+ "model.layers.27.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
562
+ "model.layers.27.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
563
+ "model.layers.27.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
564
+ "model.layers.27.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
565
+ "model.layers.27.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
566
+ "model.layers.27.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
567
+ "model.layers.27.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
568
+ "model.layers.27.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
569
+ "model.layers.27.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
570
+ "model.layers.27.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
571
+ "model.layers.27.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
572
+ "model.layers.27.ffn_norm.weight": "model-00001-of-00002.safetensors",
573
+ "model.layers.28.attention.wo.bias": "model-00001-of-00002.safetensors",
574
+ "model.layers.28.attention.wo.g_idx": "model-00001-of-00002.safetensors",
575
+ "model.layers.28.attention.wo.qweight": "model-00001-of-00002.safetensors",
576
+ "model.layers.28.attention.wo.qzeros": "model-00001-of-00002.safetensors",
577
+ "model.layers.28.attention.wo.scales": "model-00001-of-00002.safetensors",
578
+ "model.layers.28.attention.wqkv.bias": "model-00001-of-00002.safetensors",
579
+ "model.layers.28.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
580
+ "model.layers.28.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
581
+ "model.layers.28.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
582
+ "model.layers.28.attention.wqkv.scales": "model-00001-of-00002.safetensors",
583
+ "model.layers.28.attention_norm.weight": "model-00001-of-00002.safetensors",
584
+ "model.layers.28.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
585
+ "model.layers.28.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
586
+ "model.layers.28.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
587
+ "model.layers.28.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
588
+ "model.layers.28.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
589
+ "model.layers.28.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
590
+ "model.layers.28.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
591
+ "model.layers.28.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
592
+ "model.layers.28.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
593
+ "model.layers.28.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
594
+ "model.layers.28.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
595
+ "model.layers.28.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
596
+ "model.layers.28.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
597
+ "model.layers.28.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
598
+ "model.layers.28.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
599
+ "model.layers.28.ffn_norm.weight": "model-00001-of-00002.safetensors",
600
+ "model.layers.29.attention.wo.bias": "model-00001-of-00002.safetensors",
601
+ "model.layers.29.attention.wo.g_idx": "model-00001-of-00002.safetensors",
602
+ "model.layers.29.attention.wo.qweight": "model-00001-of-00002.safetensors",
603
+ "model.layers.29.attention.wo.qzeros": "model-00001-of-00002.safetensors",
604
+ "model.layers.29.attention.wo.scales": "model-00001-of-00002.safetensors",
605
+ "model.layers.29.attention.wqkv.bias": "model-00001-of-00002.safetensors",
606
+ "model.layers.29.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
607
+ "model.layers.29.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
608
+ "model.layers.29.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
609
+ "model.layers.29.attention.wqkv.scales": "model-00001-of-00002.safetensors",
610
+ "model.layers.29.attention_norm.weight": "model-00001-of-00002.safetensors",
611
+ "model.layers.29.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
612
+ "model.layers.29.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
613
+ "model.layers.29.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
614
+ "model.layers.29.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
615
+ "model.layers.29.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
616
+ "model.layers.29.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
617
+ "model.layers.29.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
618
+ "model.layers.29.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
619
+ "model.layers.29.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
620
+ "model.layers.29.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
621
+ "model.layers.29.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
622
+ "model.layers.29.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
623
+ "model.layers.29.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
624
+ "model.layers.29.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
625
+ "model.layers.29.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
626
+ "model.layers.29.ffn_norm.weight": "model-00001-of-00002.safetensors",
627
+ "model.layers.3.attention.wo.bias": "model-00001-of-00002.safetensors",
628
+ "model.layers.3.attention.wo.g_idx": "model-00001-of-00002.safetensors",
629
+ "model.layers.3.attention.wo.qweight": "model-00001-of-00002.safetensors",
630
+ "model.layers.3.attention.wo.qzeros": "model-00001-of-00002.safetensors",
631
+ "model.layers.3.attention.wo.scales": "model-00001-of-00002.safetensors",
632
+ "model.layers.3.attention.wqkv.bias": "model-00001-of-00002.safetensors",
633
+ "model.layers.3.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
634
+ "model.layers.3.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
635
+ "model.layers.3.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
636
+ "model.layers.3.attention.wqkv.scales": "model-00001-of-00002.safetensors",
637
+ "model.layers.3.attention_norm.weight": "model-00001-of-00002.safetensors",
638
+ "model.layers.3.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
639
+ "model.layers.3.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
640
+ "model.layers.3.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
641
+ "model.layers.3.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
642
+ "model.layers.3.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
643
+ "model.layers.3.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
644
+ "model.layers.3.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
645
+ "model.layers.3.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
646
+ "model.layers.3.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
647
+ "model.layers.3.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
648
+ "model.layers.3.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
649
+ "model.layers.3.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
650
+ "model.layers.3.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
651
+ "model.layers.3.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
652
+ "model.layers.3.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
653
+ "model.layers.3.ffn_norm.weight": "model-00001-of-00002.safetensors",
654
+ "model.layers.30.attention.wo.bias": "model-00001-of-00002.safetensors",
655
+ "model.layers.30.attention.wo.g_idx": "model-00001-of-00002.safetensors",
656
+ "model.layers.30.attention.wo.qweight": "model-00001-of-00002.safetensors",
657
+ "model.layers.30.attention.wo.qzeros": "model-00001-of-00002.safetensors",
658
+ "model.layers.30.attention.wo.scales": "model-00001-of-00002.safetensors",
659
+ "model.layers.30.attention.wqkv.bias": "model-00001-of-00002.safetensors",
660
+ "model.layers.30.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
661
+ "model.layers.30.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
662
+ "model.layers.30.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
663
+ "model.layers.30.attention.wqkv.scales": "model-00001-of-00002.safetensors",
664
+ "model.layers.30.attention_norm.weight": "model-00001-of-00002.safetensors",
665
+ "model.layers.30.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
666
+ "model.layers.30.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
667
+ "model.layers.30.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
668
+ "model.layers.30.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
669
+ "model.layers.30.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
670
+ "model.layers.30.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
671
+ "model.layers.30.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
672
+ "model.layers.30.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
673
+ "model.layers.30.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
674
+ "model.layers.30.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
675
+ "model.layers.30.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
676
+ "model.layers.30.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
677
+ "model.layers.30.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
678
+ "model.layers.30.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
679
+ "model.layers.30.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
680
+ "model.layers.30.ffn_norm.weight": "model-00001-of-00002.safetensors",
681
+ "model.layers.31.attention.wo.bias": "model-00001-of-00002.safetensors",
682
+ "model.layers.31.attention.wo.g_idx": "model-00001-of-00002.safetensors",
683
+ "model.layers.31.attention.wo.qweight": "model-00001-of-00002.safetensors",
684
+ "model.layers.31.attention.wo.qzeros": "model-00001-of-00002.safetensors",
685
+ "model.layers.31.attention.wo.scales": "model-00001-of-00002.safetensors",
686
+ "model.layers.31.attention.wqkv.bias": "model-00001-of-00002.safetensors",
687
+ "model.layers.31.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
688
+ "model.layers.31.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
689
+ "model.layers.31.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
690
+ "model.layers.31.attention.wqkv.scales": "model-00001-of-00002.safetensors",
691
+ "model.layers.31.attention_norm.weight": "model-00001-of-00002.safetensors",
692
+ "model.layers.31.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
693
+ "model.layers.31.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
694
+ "model.layers.31.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
695
+ "model.layers.31.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
696
+ "model.layers.31.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
697
+ "model.layers.31.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
698
+ "model.layers.31.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
699
+ "model.layers.31.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
700
+ "model.layers.31.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
701
+ "model.layers.31.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
702
+ "model.layers.31.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
703
+ "model.layers.31.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
704
+ "model.layers.31.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
705
+ "model.layers.31.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
706
+ "model.layers.31.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
707
+ "model.layers.31.ffn_norm.weight": "model-00001-of-00002.safetensors",
708
+ "model.layers.4.attention.wo.bias": "model-00001-of-00002.safetensors",
709
+ "model.layers.4.attention.wo.g_idx": "model-00001-of-00002.safetensors",
710
+ "model.layers.4.attention.wo.qweight": "model-00001-of-00002.safetensors",
711
+ "model.layers.4.attention.wo.qzeros": "model-00001-of-00002.safetensors",
712
+ "model.layers.4.attention.wo.scales": "model-00001-of-00002.safetensors",
713
+ "model.layers.4.attention.wqkv.bias": "model-00001-of-00002.safetensors",
714
+ "model.layers.4.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
715
+ "model.layers.4.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
716
+ "model.layers.4.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
717
+ "model.layers.4.attention.wqkv.scales": "model-00001-of-00002.safetensors",
718
+ "model.layers.4.attention_norm.weight": "model-00001-of-00002.safetensors",
719
+ "model.layers.4.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
720
+ "model.layers.4.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
721
+ "model.layers.4.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
722
+ "model.layers.4.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
723
+ "model.layers.4.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
724
+ "model.layers.4.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
725
+ "model.layers.4.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
726
+ "model.layers.4.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
727
+ "model.layers.4.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
728
+ "model.layers.4.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
729
+ "model.layers.4.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
730
+ "model.layers.4.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
731
+ "model.layers.4.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
732
+ "model.layers.4.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
733
+ "model.layers.4.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
734
+ "model.layers.4.ffn_norm.weight": "model-00001-of-00002.safetensors",
735
+ "model.layers.5.attention.wo.bias": "model-00001-of-00002.safetensors",
736
+ "model.layers.5.attention.wo.g_idx": "model-00001-of-00002.safetensors",
737
+ "model.layers.5.attention.wo.qweight": "model-00001-of-00002.safetensors",
738
+ "model.layers.5.attention.wo.qzeros": "model-00001-of-00002.safetensors",
739
+ "model.layers.5.attention.wo.scales": "model-00001-of-00002.safetensors",
740
+ "model.layers.5.attention.wqkv.bias": "model-00001-of-00002.safetensors",
741
+ "model.layers.5.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
742
+ "model.layers.5.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
743
+ "model.layers.5.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
744
+ "model.layers.5.attention.wqkv.scales": "model-00001-of-00002.safetensors",
745
+ "model.layers.5.attention_norm.weight": "model-00001-of-00002.safetensors",
746
+ "model.layers.5.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
747
+ "model.layers.5.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
748
+ "model.layers.5.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
749
+ "model.layers.5.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
750
+ "model.layers.5.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
751
+ "model.layers.5.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
752
+ "model.layers.5.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
753
+ "model.layers.5.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
754
+ "model.layers.5.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
755
+ "model.layers.5.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
756
+ "model.layers.5.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
757
+ "model.layers.5.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
758
+ "model.layers.5.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
759
+ "model.layers.5.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
760
+ "model.layers.5.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
761
+ "model.layers.5.ffn_norm.weight": "model-00001-of-00002.safetensors",
762
+ "model.layers.6.attention.wo.bias": "model-00001-of-00002.safetensors",
763
+ "model.layers.6.attention.wo.g_idx": "model-00001-of-00002.safetensors",
764
+ "model.layers.6.attention.wo.qweight": "model-00001-of-00002.safetensors",
765
+ "model.layers.6.attention.wo.qzeros": "model-00001-of-00002.safetensors",
766
+ "model.layers.6.attention.wo.scales": "model-00001-of-00002.safetensors",
767
+ "model.layers.6.attention.wqkv.bias": "model-00001-of-00002.safetensors",
768
+ "model.layers.6.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
769
+ "model.layers.6.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
770
+ "model.layers.6.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
771
+ "model.layers.6.attention.wqkv.scales": "model-00001-of-00002.safetensors",
772
+ "model.layers.6.attention_norm.weight": "model-00001-of-00002.safetensors",
773
+ "model.layers.6.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
774
+ "model.layers.6.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
775
+ "model.layers.6.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
776
+ "model.layers.6.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
777
+ "model.layers.6.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
778
+ "model.layers.6.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
779
+ "model.layers.6.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
780
+ "model.layers.6.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
781
+ "model.layers.6.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
782
+ "model.layers.6.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
783
+ "model.layers.6.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
784
+ "model.layers.6.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
785
+ "model.layers.6.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
786
+ "model.layers.6.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
787
+ "model.layers.6.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
788
+ "model.layers.6.ffn_norm.weight": "model-00001-of-00002.safetensors",
789
+ "model.layers.7.attention.wo.bias": "model-00001-of-00002.safetensors",
790
+ "model.layers.7.attention.wo.g_idx": "model-00001-of-00002.safetensors",
791
+ "model.layers.7.attention.wo.qweight": "model-00001-of-00002.safetensors",
792
+ "model.layers.7.attention.wo.qzeros": "model-00001-of-00002.safetensors",
793
+ "model.layers.7.attention.wo.scales": "model-00001-of-00002.safetensors",
794
+ "model.layers.7.attention.wqkv.bias": "model-00001-of-00002.safetensors",
795
+ "model.layers.7.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
796
+ "model.layers.7.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
797
+ "model.layers.7.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
798
+ "model.layers.7.attention.wqkv.scales": "model-00001-of-00002.safetensors",
799
+ "model.layers.7.attention_norm.weight": "model-00001-of-00002.safetensors",
800
+ "model.layers.7.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
801
+ "model.layers.7.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
802
+ "model.layers.7.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
803
+ "model.layers.7.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
804
+ "model.layers.7.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
805
+ "model.layers.7.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
806
+ "model.layers.7.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
807
+ "model.layers.7.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
808
+ "model.layers.7.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
809
+ "model.layers.7.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
810
+ "model.layers.7.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
811
+ "model.layers.7.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
812
+ "model.layers.7.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
813
+ "model.layers.7.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
814
+ "model.layers.7.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
815
+ "model.layers.7.ffn_norm.weight": "model-00001-of-00002.safetensors",
816
+ "model.layers.8.attention.wo.bias": "model-00001-of-00002.safetensors",
817
+ "model.layers.8.attention.wo.g_idx": "model-00001-of-00002.safetensors",
818
+ "model.layers.8.attention.wo.qweight": "model-00001-of-00002.safetensors",
819
+ "model.layers.8.attention.wo.qzeros": "model-00001-of-00002.safetensors",
820
+ "model.layers.8.attention.wo.scales": "model-00001-of-00002.safetensors",
821
+ "model.layers.8.attention.wqkv.bias": "model-00001-of-00002.safetensors",
822
+ "model.layers.8.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
823
+ "model.layers.8.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
824
+ "model.layers.8.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
825
+ "model.layers.8.attention.wqkv.scales": "model-00001-of-00002.safetensors",
826
+ "model.layers.8.attention_norm.weight": "model-00001-of-00002.safetensors",
827
+ "model.layers.8.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
828
+ "model.layers.8.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
829
+ "model.layers.8.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
830
+ "model.layers.8.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
831
+ "model.layers.8.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
832
+ "model.layers.8.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
833
+ "model.layers.8.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
834
+ "model.layers.8.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
835
+ "model.layers.8.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
836
+ "model.layers.8.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
837
+ "model.layers.8.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
838
+ "model.layers.8.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
839
+ "model.layers.8.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
840
+ "model.layers.8.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
841
+ "model.layers.8.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
842
+ "model.layers.8.ffn_norm.weight": "model-00001-of-00002.safetensors",
843
+ "model.layers.9.attention.wo.bias": "model-00001-of-00002.safetensors",
844
+ "model.layers.9.attention.wo.g_idx": "model-00001-of-00002.safetensors",
845
+ "model.layers.9.attention.wo.qweight": "model-00001-of-00002.safetensors",
846
+ "model.layers.9.attention.wo.qzeros": "model-00001-of-00002.safetensors",
847
+ "model.layers.9.attention.wo.scales": "model-00001-of-00002.safetensors",
848
+ "model.layers.9.attention.wqkv.bias": "model-00001-of-00002.safetensors",
849
+ "model.layers.9.attention.wqkv.g_idx": "model-00001-of-00002.safetensors",
850
+ "model.layers.9.attention.wqkv.qweight": "model-00001-of-00002.safetensors",
851
+ "model.layers.9.attention.wqkv.qzeros": "model-00001-of-00002.safetensors",
852
+ "model.layers.9.attention.wqkv.scales": "model-00001-of-00002.safetensors",
853
+ "model.layers.9.attention_norm.weight": "model-00001-of-00002.safetensors",
854
+ "model.layers.9.feed_forward.w1.bias": "model-00001-of-00002.safetensors",
855
+ "model.layers.9.feed_forward.w1.g_idx": "model-00001-of-00002.safetensors",
856
+ "model.layers.9.feed_forward.w1.qweight": "model-00001-of-00002.safetensors",
857
+ "model.layers.9.feed_forward.w1.qzeros": "model-00001-of-00002.safetensors",
858
+ "model.layers.9.feed_forward.w1.scales": "model-00001-of-00002.safetensors",
859
+ "model.layers.9.feed_forward.w2.bias": "model-00001-of-00002.safetensors",
860
+ "model.layers.9.feed_forward.w2.g_idx": "model-00001-of-00002.safetensors",
861
+ "model.layers.9.feed_forward.w2.qweight": "model-00001-of-00002.safetensors",
862
+ "model.layers.9.feed_forward.w2.qzeros": "model-00001-of-00002.safetensors",
863
+ "model.layers.9.feed_forward.w2.scales": "model-00001-of-00002.safetensors",
864
+ "model.layers.9.feed_forward.w3.bias": "model-00001-of-00002.safetensors",
865
+ "model.layers.9.feed_forward.w3.g_idx": "model-00001-of-00002.safetensors",
866
+ "model.layers.9.feed_forward.w3.qweight": "model-00001-of-00002.safetensors",
867
+ "model.layers.9.feed_forward.w3.qzeros": "model-00001-of-00002.safetensors",
868
+ "model.layers.9.feed_forward.w3.scales": "model-00001-of-00002.safetensors",
869
+ "model.layers.9.ffn_norm.weight": "model-00001-of-00002.safetensors",
870
+ "model.norm.weight": "model-00001-of-00002.safetensors",
871
+ "model.tok_embeddings.weight": "model-00001-of-00002.safetensors",
872
+ "output.weight": "model-00002-of-00002.safetensors"
873
+ }
874
+ }
modeling_internlm2.py ADDED
@@ -0,0 +1,1391 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # This code is based on transformers/src/transformers/models/llama/modeling_llama.py
4
+ #
5
+ # Licensed under the Apache License, Version 2.0 (the "License");
6
+ # you may not use this file except in compliance with the License.
7
+ # You may obtain a copy of the License at
8
+ #
9
+ # http://www.apache.org/licenses/LICENSE-2.0
10
+ #
11
+ # Unless required by applicable law or agreed to in writing, software
12
+ # distributed under the License is distributed on an "AS IS" BASIS,
13
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14
+ # See the License for the specific language governing permissions and
15
+ # limitations under the License.
16
+ """ PyTorch InternLM2 model."""
17
+ import math
18
+ import queue
19
+ import threading
20
+ import warnings
21
+ from typing import List, Optional, Tuple, Union
22
+
23
+ import torch
24
+ import torch.nn.functional as F
25
+ import torch.utils.checkpoint
26
+ from einops import rearrange
27
+ from torch import nn
28
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
29
+ from transformers.activations import ACT2FN
30
+ from transformers.modeling_outputs import (
31
+ BaseModelOutputWithPast,
32
+ CausalLMOutputWithPast,
33
+ SequenceClassifierOutputWithPast,
34
+ )
35
+ from transformers.modeling_utils import PreTrainedModel
36
+ from transformers.utils import (
37
+ add_start_docstrings,
38
+ add_start_docstrings_to_model_forward,
39
+ logging,
40
+ replace_return_docstrings,
41
+ )
42
+
43
+ try:
44
+ from transformers.generation.streamers import BaseStreamer
45
+ except: # noqa # pylint: disable=bare-except
46
+ BaseStreamer = None
47
+
48
+ from .configuration_internlm2 import InternLM2Config
49
+
50
+ logger = logging.get_logger(__name__)
51
+
52
+ _CONFIG_FOR_DOC = "InternLM2Config"
53
+
54
+ flash_attn_func, flash_attn_varlen_func = None, None
55
+ pad_input, index_first_axis, unpad_input = None, None, None
56
+ def _import_flash_attn():
57
+ global flash_attn_func, flash_attn_varlen_func
58
+ global pad_input, index_first_axis, unpad_input
59
+ try:
60
+ from flash_attn import flash_attn_func as _flash_attn_func, flash_attn_varlen_func as _flash_attn_varlen_func
61
+ from flash_attn.bert_padding import pad_input as _pad_input, index_first_axis as _index_first_axis, unpad_input as _unpad_input
62
+ flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
63
+ pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
64
+ except ImportError:
65
+ raise ImportError("flash_attn is not installed.")
66
+
67
+ # Copied from transformers.models.llama.modeling_llama._get_unpad_data
68
+ def _get_unpad_data(attention_mask):
69
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
70
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
71
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
72
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
73
+ return (
74
+ indices,
75
+ cu_seqlens,
76
+ max_seqlen_in_batch,
77
+ )
78
+
79
+
80
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
81
+ def _make_causal_mask(
82
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
83
+ ):
84
+ """
85
+ Make causal mask used for bi-directional self-attention.
86
+ """
87
+ bsz, tgt_len = input_ids_shape
88
+ mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
89
+ mask_cond = torch.arange(mask.size(-1), device=device)
90
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
91
+ mask = mask.to(dtype)
92
+
93
+ if past_key_values_length > 0:
94
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
95
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
96
+
97
+
98
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
99
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
100
+ """
101
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
102
+ """
103
+ bsz, src_len = mask.size()
104
+ tgt_len = tgt_len if tgt_len is not None else src_len
105
+
106
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
107
+
108
+ inverted_mask = 1.0 - expanded_mask
109
+
110
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
111
+
112
+
113
+ # Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
114
+ class InternLM2RMSNorm(nn.Module):
115
+ def __init__(self, hidden_size, eps=1e-6):
116
+ """
117
+ InternLM2RMSNorm is equivalent to T5LayerNorm
118
+ """
119
+ super().__init__()
120
+ self.weight = nn.Parameter(torch.ones(hidden_size))
121
+ self.variance_epsilon = eps
122
+
123
+ def forward(self, hidden_states):
124
+ input_dtype = hidden_states.dtype
125
+ hidden_states = hidden_states.to(torch.float32)
126
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
127
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
128
+ return self.weight * hidden_states.to(input_dtype)
129
+
130
+
131
+ # Copied from transformers.model.llama.modeling_llama.LlamaRotaryEmbedding with Llama->InternLM2
132
+ class InternLM2RotaryEmbedding(nn.Module):
133
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
134
+ super().__init__()
135
+
136
+ self.dim = dim
137
+ self.max_position_embeddings = max_position_embeddings
138
+ self.base = base
139
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
140
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
141
+
142
+ # Build here to make `torch.jit.trace` work.
143
+ self._set_cos_sin_cache(
144
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
145
+ )
146
+
147
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
148
+ self.max_seq_len_cached = seq_len
149
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
150
+
151
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
152
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
153
+ emb = torch.cat((freqs, freqs), dim=-1)
154
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
155
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
156
+
157
+ def forward(self, x, seq_len=None):
158
+ # x: [bs, num_attention_heads, seq_len, head_size]
159
+ if seq_len > self.max_seq_len_cached:
160
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.float32)
161
+
162
+ return (
163
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
164
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
165
+ )
166
+
167
+
168
+ # Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
169
+ class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
170
+ """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
171
+
172
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
173
+ self.scaling_factor = scaling_factor
174
+ super().__init__(dim, max_position_embeddings, base, device)
175
+
176
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
177
+ self.max_seq_len_cached = seq_len
178
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
179
+ t = t / self.scaling_factor
180
+
181
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
182
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
183
+ emb = torch.cat((freqs, freqs), dim=-1)
184
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
185
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
186
+
187
+
188
+ # Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
189
+ class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
190
+ """InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
191
+ Credits to the Reddit users /u/bloc97 and /u/emozilla.
192
+ """
193
+
194
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
195
+ self.scaling_factor = scaling_factor
196
+ super().__init__(dim, max_position_embeddings, base, device)
197
+
198
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
199
+ self.max_seq_len_cached = seq_len
200
+
201
+ if seq_len > self.max_position_embeddings:
202
+ base = self.base * (
203
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
204
+ ) ** (self.dim / (self.dim - 2))
205
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
206
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
207
+
208
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
209
+
210
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
211
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
212
+ emb = torch.cat((freqs, freqs), dim=-1)
213
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
214
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
215
+
216
+
217
+ # Copied from transformers.model.llama.modeling_llama.rotate_half
218
+ def rotate_half(x):
219
+ """Rotates half the hidden dims of the input."""
220
+ x1 = x[..., : x.shape[-1] // 2]
221
+ x2 = x[..., x.shape[-1] // 2 :]
222
+ return torch.cat((-x2, x1), dim=-1)
223
+
224
+
225
+ # Copied from transformers.model.llama.modeling_llama.apply_rotary_pos_emb
226
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
227
+ """Applies Rotary Position Embedding to the query and key tensors."""
228
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
229
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
230
+ q_embed = (q * cos) + (rotate_half(q) * sin)
231
+ k_embed = (k * cos) + (rotate_half(k) * sin)
232
+ return q_embed, k_embed
233
+
234
+
235
+ class InternLM2MLP(nn.Module):
236
+ def __init__(self, config):
237
+ super().__init__()
238
+ self.config = config
239
+ self.hidden_size = config.hidden_size
240
+ self.intermediate_size = config.intermediate_size
241
+ self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
242
+ self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
243
+ self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
244
+ self.act_fn = ACT2FN[config.hidden_act]
245
+
246
+ def forward(self, x):
247
+ down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))
248
+
249
+ return down_proj
250
+
251
+
252
+ # Copied from transformers.model.llama.modeling_llama.repeat_kv
253
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
254
+ """
255
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
256
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
257
+ """
258
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
259
+ if n_rep == 1:
260
+ return hidden_states
261
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
262
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
263
+
264
+
265
+ # Modified from transformers.model.llama.modeling_llama.LlamaAttention
266
+ class InternLM2Attention(nn.Module):
267
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
268
+
269
+ def __init__(self, config: InternLM2Config):
270
+ super().__init__()
271
+ self.config = config
272
+ self.hidden_size = config.hidden_size
273
+ self.num_heads = config.num_attention_heads
274
+ self.head_dim = self.hidden_size // self.num_heads
275
+ self.num_key_value_heads = config.num_key_value_heads
276
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
277
+ self.max_position_embeddings = config.max_position_embeddings
278
+ self.is_causal = True
279
+
280
+ if (self.head_dim * self.num_heads) != self.hidden_size:
281
+ raise ValueError(
282
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
283
+ f" and `num_heads`: {self.num_heads})."
284
+ )
285
+
286
+ self.wqkv = nn.Linear(
287
+ self.hidden_size,
288
+ (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
289
+ bias=config.bias,
290
+ )
291
+
292
+ self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
293
+ self._init_rope()
294
+
295
+ def _init_rope(self):
296
+ if self.config.rope_scaling is None:
297
+ self.rotary_emb = InternLM2RotaryEmbedding(
298
+ self.head_dim,
299
+ max_position_embeddings=self.max_position_embeddings,
300
+ base=self.config.rope_theta,
301
+ )
302
+ else:
303
+ scaling_type = self.config.rope_scaling["type"]
304
+ scaling_factor = self.config.rope_scaling["factor"]
305
+ if scaling_type == "dynamic":
306
+ self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
307
+ self.head_dim,
308
+ max_position_embeddings=self.max_position_embeddings,
309
+ base=self.config.rope_theta,
310
+ scaling_factor=scaling_factor,
311
+ )
312
+ elif scaling_type == "linear":
313
+ self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
314
+ self.head_dim,
315
+ max_position_embeddings=self.max_position_embeddings,
316
+ base=self.config.rope_theta,
317
+ scaling_factor=scaling_factor,
318
+ )
319
+ else:
320
+ raise ValueError("Currently we only support rotary embedding's type being 'dynamic' or 'linear'.")
321
+ return self.rotary_emb
322
+
323
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
324
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
325
+
326
+ def forward(
327
+ self,
328
+ hidden_states: torch.Tensor,
329
+ attention_mask: Optional[torch.Tensor] = None,
330
+ position_ids: Optional[torch.LongTensor] = None,
331
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
332
+ output_attentions: bool = False,
333
+ use_cache: bool = False,
334
+ **kwargs,
335
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
336
+ if "padding_mask" in kwargs:
337
+ warnings.warn(
338
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. "
339
+ "Please make sure use `attention_mask` instead.`"
340
+ )
341
+
342
+ bsz, q_len, _ = hidden_states.size()
343
+
344
+ qkv_states = self.wqkv(hidden_states)
345
+
346
+ qkv_states = rearrange(
347
+ qkv_states,
348
+ "b q (h gs d) -> b q h gs d",
349
+ gs=2 + self.num_key_value_groups,
350
+ d=self.head_dim,
351
+ )
352
+
353
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
354
+ query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
355
+ key_states = qkv_states[..., -2, :]
356
+ value_states = qkv_states[..., -1, :]
357
+
358
+ query_states = query_states.transpose(1, 2)
359
+ key_states = key_states.transpose(1, 2)
360
+ value_states = value_states.transpose(1, 2)
361
+
362
+ kv_seq_len = key_states.shape[-2]
363
+ if past_key_value is not None:
364
+ kv_seq_len += past_key_value[0].shape[-2]
365
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
366
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
367
+
368
+ if past_key_value is not None:
369
+ # reuse k, v, self_attention
370
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
371
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
372
+
373
+ past_key_value = (key_states, value_states) if use_cache else None
374
+
375
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
376
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
377
+
378
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
379
+
380
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
381
+ raise ValueError(
382
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
383
+ f" {attn_weights.size()}"
384
+ )
385
+
386
+ if attention_mask is not None:
387
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
388
+ raise ValueError(
389
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
390
+ )
391
+ attn_weights = attn_weights + attention_mask
392
+
393
+ # upcast attention to fp32
394
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
395
+ attn_output = torch.matmul(attn_weights, value_states)
396
+
397
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
398
+ raise ValueError(
399
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
400
+ f" {attn_output.size()}"
401
+ )
402
+
403
+ attn_output = attn_output.transpose(1, 2).contiguous()
404
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
405
+
406
+ attn_output = self.wo(attn_output)
407
+
408
+ if not output_attentions:
409
+ attn_weights = None
410
+
411
+ return attn_output, attn_weights, past_key_value
412
+
413
+
414
+ # Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
415
+ class InternLM2FlashAttention2(InternLM2Attention):
416
+ """
417
+ InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
418
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
419
+ flash attention and deal with padding tokens in case the input contains any of them.
420
+ """
421
+
422
+ def forward(
423
+ self,
424
+ hidden_states: torch.Tensor,
425
+ attention_mask: Optional[torch.LongTensor] = None,
426
+ position_ids: Optional[torch.LongTensor] = None,
427
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
428
+ output_attentions: bool = False,
429
+ use_cache: bool = False,
430
+ **kwargs,
431
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
432
+ # InternLM2FlashAttention2 attention does not support output_attentions
433
+ if "padding_mask" in kwargs:
434
+ warnings.warn(
435
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. "
436
+ "Please make sure use `attention_mask` instead.`"
437
+ )
438
+
439
+ # overwrite attention_mask with padding_mask
440
+ attention_mask = kwargs.pop("padding_mask")
441
+
442
+ output_attentions = False
443
+
444
+ bsz, q_len, _ = hidden_states.size()
445
+
446
+ qkv_states = self.wqkv(hidden_states)
447
+
448
+ qkv_states = rearrange(
449
+ qkv_states,
450
+ "b q (h gs d) -> b q h gs d",
451
+ gs=2 + self.num_key_value_groups,
452
+ d=self.head_dim,
453
+ )
454
+
455
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
456
+ query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
457
+ key_states = qkv_states[..., -2, :]
458
+ value_states = qkv_states[..., -1, :]
459
+
460
+ query_states = query_states.transpose(1, 2)
461
+ key_states = key_states.transpose(1, 2)
462
+ value_states = value_states.transpose(1, 2)
463
+
464
+ kv_seq_len = key_states.shape[-2]
465
+ if past_key_value is not None:
466
+ kv_seq_len += past_key_value[0].shape[-2]
467
+
468
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
469
+
470
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
471
+
472
+ if past_key_value is not None:
473
+ # reuse k, v, self_attention
474
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
475
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
476
+
477
+ past_key_value = (key_states, value_states) if use_cache else None
478
+
479
+ query_states = query_states.transpose(1, 2)
480
+ key_states = key_states.transpose(1, 2)
481
+ value_states = value_states.transpose(1, 2)
482
+
483
+ attn_output = self._flash_attention_forward(
484
+ query_states, key_states, value_states, attention_mask, q_len
485
+ )
486
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
487
+ attn_output = self.wo(attn_output)
488
+
489
+ if not output_attentions:
490
+ attn_weights = None
491
+
492
+ return attn_output, attn_weights, past_key_value
493
+
494
+ def _flash_attention_forward(
495
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
496
+ ):
497
+ """
498
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
499
+ first unpad the input, then computes the attention scores and pad the final attention scores.
500
+
501
+ Args:
502
+ query_states (`torch.Tensor`):
503
+ Input query states to be passed to Flash Attention API
504
+ key_states (`torch.Tensor`):
505
+ Input key states to be passed to Flash Attention API
506
+ value_states (`torch.Tensor`):
507
+ Input value states to be passed to Flash Attention API
508
+ attention_mask (`torch.Tensor`):
509
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
510
+ position of padding tokens and 1 for the position of non-padding tokens.
511
+ dropout (`int`, *optional*):
512
+ Attention dropout
513
+ softmax_scale (`float`, *optional*):
514
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
515
+ """
516
+ # Contains at least one padding token in the sequence
517
+ causal = self.is_causal and query_length != 1
518
+ if attention_mask is not None:
519
+ batch_size = query_states.shape[0]
520
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._unpad_input(
521
+ query_states, key_states, value_states, attention_mask, query_length
522
+ )
523
+
524
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
525
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
526
+
527
+ attn_output_unpad = flash_attn_varlen_func(
528
+ query_states,
529
+ key_states,
530
+ value_states,
531
+ cu_seqlens_q=cu_seqlens_q,
532
+ cu_seqlens_k=cu_seqlens_k,
533
+ max_seqlen_q=max_seqlen_in_batch_q,
534
+ max_seqlen_k=max_seqlen_in_batch_k,
535
+ dropout_p=dropout,
536
+ softmax_scale=softmax_scale,
537
+ causal=causal,
538
+ )
539
+
540
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
541
+ else:
542
+ attn_output = flash_attn_func(
543
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
544
+ )
545
+
546
+ return attn_output
547
+
548
+ def _unpad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
549
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
550
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
551
+
552
+ key_layer = index_first_axis(
553
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
554
+ )
555
+ value_layer = index_first_axis(
556
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
557
+ )
558
+
559
+ if query_length == kv_seq_len:
560
+ query_layer = index_first_axis(
561
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
562
+ )
563
+ cu_seqlens_q = cu_seqlens_k
564
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
565
+ indices_q = indices_k
566
+ elif query_length == 1:
567
+ max_seqlen_in_batch_q = 1
568
+ cu_seqlens_q = torch.arange(
569
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
570
+ ) # There is a memcpy here, that is very bad.
571
+ indices_q = cu_seqlens_q[:-1]
572
+ query_layer = query_layer.squeeze(1)
573
+ else:
574
+ # The -q_len: slice assumes left padding.
575
+ attention_mask = attention_mask[:, -query_length:]
576
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
577
+
578
+ return (
579
+ query_layer,
580
+ key_layer,
581
+ value_layer,
582
+ indices_q.to(torch.int64),
583
+ (cu_seqlens_q, cu_seqlens_k),
584
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
585
+ )
586
+
587
+ INTERNLM2_ATTENTION_CLASSES = {
588
+ "eager": InternLM2Attention,
589
+ "flash_attention_2": InternLM2FlashAttention2,
590
+ }
591
+
592
+ # Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
593
+ class InternLM2DecoderLayer(nn.Module):
594
+ def __init__(self, config: InternLM2Config):
595
+ super().__init__()
596
+ self.hidden_size = config.hidden_size
597
+
598
+ self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config)
599
+
600
+ self.feed_forward = InternLM2MLP(config)
601
+ self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
602
+ self.ffn_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
603
+
604
+ def forward(
605
+ self,
606
+ hidden_states: torch.Tensor,
607
+ attention_mask: Optional[torch.Tensor] = None,
608
+ position_ids: Optional[torch.LongTensor] = None,
609
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
610
+ output_attentions: Optional[bool] = False,
611
+ use_cache: Optional[bool] = False,
612
+ **kwargs,
613
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
614
+ """
615
+ Args:
616
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
617
+ attention_mask (`torch.FloatTensor`, *optional*):
618
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
619
+ query_sequence_length, key_sequence_length)` if default attention is used.
620
+ output_attentions (`bool`, *optional*):
621
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
622
+ returned tensors for more detail.
623
+ use_cache (`bool`, *optional*):
624
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
625
+ (see `past_key_values`).
626
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
627
+ """
628
+ if "padding_mask" in kwargs:
629
+ warnings.warn(
630
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. "
631
+ "Please make sure use `attention_mask` instead.`"
632
+ )
633
+
634
+ residual = hidden_states
635
+
636
+ hidden_states = self.attention_norm(hidden_states)
637
+
638
+ # Self Attention
639
+ hidden_states, self_attn_weights, present_key_value = self.attention(
640
+ hidden_states=hidden_states,
641
+ attention_mask=attention_mask,
642
+ position_ids=position_ids,
643
+ past_key_value=past_key_value,
644
+ output_attentions=output_attentions,
645
+ use_cache=use_cache,
646
+ **kwargs,
647
+ )
648
+ hidden_states = residual + hidden_states
649
+
650
+ # Fully Connected
651
+ residual = hidden_states
652
+ hidden_states = self.ffn_norm(hidden_states)
653
+ hidden_states = self.feed_forward(hidden_states)
654
+ hidden_states = residual + hidden_states
655
+
656
+ outputs = (hidden_states,)
657
+
658
+ if output_attentions:
659
+ outputs += (self_attn_weights,)
660
+
661
+ if use_cache:
662
+ outputs += (present_key_value,)
663
+
664
+ return outputs
665
+
666
+
667
+ InternLM2_START_DOCSTRING = r"""
668
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
669
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
670
+ etc.)
671
+
672
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
673
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
674
+ and behavior.
675
+
676
+ Parameters:
677
+ config ([`InternLM2Config`]):
678
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
679
+ load the weights associated with the model, only the configuration. Check out the
680
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
681
+ """
682
+
683
+
684
+ # Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
685
+ @add_start_docstrings(
686
+ "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
687
+ InternLM2_START_DOCSTRING,
688
+ )
689
+ class InternLM2PreTrainedModel(PreTrainedModel):
690
+ config_class = InternLM2Config
691
+ base_model_prefix = "model"
692
+ supports_gradient_checkpointing = True
693
+ _no_split_modules = ["InternLM2DecoderLayer"]
694
+ _skip_keys_device_placement = "past_key_values"
695
+
696
+ def _init_weights(self, module):
697
+ std = self.config.initializer_range
698
+ if isinstance(module, nn.Linear):
699
+ module.weight.data.normal_(mean=0.0, std=std)
700
+ if module.bias is not None:
701
+ module.bias.data.zero_()
702
+ elif isinstance(module, nn.Embedding):
703
+ module.weight.data.normal_(mean=0.0, std=std)
704
+ if module.padding_idx is not None:
705
+ module.weight.data[module.padding_idx].zero_()
706
+
707
+
708
+ InternLM2_INPUTS_DOCSTRING = r"""
709
+ Args:
710
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
711
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
712
+ it.
713
+
714
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
715
+ [`PreTrainedTokenizer.__call__`] for details.
716
+
717
+ [What are input IDs?](../glossary#input-ids)
718
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
719
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
720
+
721
+ - 1 for tokens that are **not masked**,
722
+ - 0 for tokens that are **masked**.
723
+
724
+ [What are attention masks?](../glossary#attention-mask)
725
+
726
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
727
+ [`PreTrainedTokenizer.__call__`] for details.
728
+
729
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
730
+ `past_key_values`).
731
+
732
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
733
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
734
+ information on the default strategy.
735
+
736
+ - 1 indicates the head is **not masked**,
737
+ - 0 indicates the head is **masked**.
738
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
739
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
740
+ config.n_positions - 1]`.
741
+
742
+ [What are position IDs?](../glossary#position-ids)
743
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or
744
+ when `config.use_cache=True`):
745
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
746
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
747
+ `(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)`.
748
+
749
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
750
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
751
+
752
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
753
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
754
+ of shape `(batch_size, sequence_length)`.
755
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
756
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
757
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
758
+ model's internal embedding lookup matrix.
759
+ use_cache (`bool`, *optional*):
760
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
761
+ `past_key_values`).
762
+ output_attentions (`bool`, *optional*):
763
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
764
+ tensors for more detail.
765
+ output_hidden_states (`bool`, *optional*):
766
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
767
+ more detail.
768
+ return_dict (`bool`, *optional*):
769
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
770
+ """
771
+
772
+
773
+ # Modified from transformers.model.llama.modeling_llama.LlamaModel
774
+ @add_start_docstrings(
775
+ "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
776
+ InternLM2_START_DOCSTRING,
777
+ )
778
+ class InternLM2Model(InternLM2PreTrainedModel):
779
+ """
780
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]
781
+
782
+ Args:
783
+ config: InternLM2Config
784
+ """
785
+
786
+ _auto_class = "AutoModel"
787
+
788
+ def __init__(self, config: InternLM2Config):
789
+ super().__init__(config)
790
+ self.padding_idx = config.pad_token_id
791
+ self.vocab_size = config.vocab_size
792
+ self.config = config
793
+
794
+ self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
795
+
796
+ self.layers = nn.ModuleList([InternLM2DecoderLayer(config) for _ in range(config.num_hidden_layers)])
797
+ self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
798
+
799
+ self.gradient_checkpointing = False
800
+ # Initialize weights and apply final processing
801
+ self.post_init()
802
+
803
+ def get_input_embeddings(self):
804
+ return self.tok_embeddings
805
+
806
+ def set_input_embeddings(self, value):
807
+ self.tok_embeddings = value
808
+
809
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
810
+ # create causal mask
811
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
812
+ combined_attention_mask = None
813
+ if input_shape[-1] > 1:
814
+ combined_attention_mask = _make_causal_mask(
815
+ input_shape,
816
+ inputs_embeds.dtype,
817
+ device=inputs_embeds.device,
818
+ past_key_values_length=past_key_values_length,
819
+ )
820
+
821
+ if attention_mask is not None:
822
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
823
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
824
+ inputs_embeds.device
825
+ )
826
+ combined_attention_mask = (
827
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
828
+ )
829
+
830
+ return combined_attention_mask
831
+
832
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
833
+ def forward(
834
+ self,
835
+ input_ids: torch.LongTensor = None,
836
+ attention_mask: Optional[torch.Tensor] = None,
837
+ position_ids: Optional[torch.LongTensor] = None,
838
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
839
+ inputs_embeds: Optional[torch.FloatTensor] = None,
840
+ use_cache: Optional[bool] = None,
841
+ output_attentions: Optional[bool] = None,
842
+ output_hidden_states: Optional[bool] = None,
843
+ return_dict: Optional[bool] = None,
844
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
845
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
846
+ output_hidden_states = (
847
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
848
+ )
849
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
850
+
851
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
852
+
853
+ if self.config.attn_implementation == "flash_attention_2":
854
+ _import_flash_attn()
855
+
856
+ # retrieve input_ids and inputs_embeds
857
+ if input_ids is not None and inputs_embeds is not None:
858
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
859
+ elif input_ids is not None:
860
+ batch_size, seq_length = input_ids.shape[:2]
861
+ elif inputs_embeds is not None:
862
+ batch_size, seq_length = inputs_embeds.shape[:2]
863
+ else:
864
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
865
+
866
+ seq_length_with_past = seq_length
867
+ past_key_values_length = 0
868
+ if past_key_values is not None:
869
+ past_key_values_length = past_key_values[0][0].shape[2]
870
+ seq_length_with_past = seq_length_with_past + past_key_values_length
871
+
872
+ if position_ids is None:
873
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
874
+ position_ids = torch.arange(
875
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
876
+ )
877
+ position_ids = position_ids.unsqueeze(0)
878
+
879
+ if inputs_embeds is None:
880
+ inputs_embeds = self.tok_embeddings(input_ids)
881
+
882
+ if self.config.attn_implementation == "flash_attention_2":
883
+ # 2d mask is passed through the layers
884
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
885
+ else:
886
+ if attention_mask is None:
887
+ attention_mask = torch.ones(
888
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
889
+ )
890
+ attention_mask = self._prepare_decoder_attention_mask(
891
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
892
+ )
893
+
894
+ # embed positions
895
+ hidden_states = inputs_embeds
896
+
897
+ if self.gradient_checkpointing and self.training:
898
+ if use_cache:
899
+ logger.warning_once(
900
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
901
+ )
902
+ use_cache = False
903
+
904
+ # decoder layers
905
+ all_hidden_states = () if output_hidden_states else None
906
+ all_self_attns = () if output_attentions else None
907
+ next_decoder_cache = () if use_cache else None
908
+
909
+ for idx, decoder_layer in enumerate(self.layers):
910
+ if output_hidden_states:
911
+ all_hidden_states += (hidden_states,)
912
+
913
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
914
+
915
+ if self.gradient_checkpointing and self.training:
916
+
917
+ def create_custom_forward(module):
918
+ def custom_forward(*inputs):
919
+ # None for past_key_value
920
+ return module(*inputs, output_attentions, None)
921
+
922
+ return custom_forward
923
+
924
+ layer_outputs = torch.utils.checkpoint.checkpoint(
925
+ create_custom_forward(decoder_layer),
926
+ hidden_states,
927
+ attention_mask,
928
+ position_ids,
929
+ None,
930
+ )
931
+ else:
932
+ layer_outputs = decoder_layer(
933
+ hidden_states,
934
+ attention_mask=attention_mask,
935
+ position_ids=position_ids,
936
+ past_key_value=past_key_value,
937
+ output_attentions=output_attentions,
938
+ use_cache=use_cache,
939
+ )
940
+
941
+ hidden_states = layer_outputs[0]
942
+
943
+ if use_cache:
944
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
945
+
946
+ if output_attentions:
947
+ all_self_attns += (layer_outputs[1],)
948
+
949
+ hidden_states = self.norm(hidden_states)
950
+
951
+ # add hidden states from the last decoder layer
952
+ if output_hidden_states:
953
+ all_hidden_states += (hidden_states,)
954
+
955
+ next_cache = next_decoder_cache if use_cache else None
956
+ if not return_dict:
957
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
958
+ return BaseModelOutputWithPast(
959
+ last_hidden_state=hidden_states,
960
+ past_key_values=next_cache,
961
+ hidden_states=all_hidden_states,
962
+ attentions=all_self_attns,
963
+ )
964
+
965
+
966
+ # Modified from transformers.model.llama.modeling_llama.LlamaForCausalLM
967
+ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
968
+ _auto_class = "AutoModelForCausalLM"
969
+
970
+ _tied_weights_keys = ["output.weight"]
971
+
972
+ def __init__(self, config):
973
+ super().__init__(config)
974
+ self.model = InternLM2Model(config)
975
+ self.vocab_size = config.vocab_size
976
+ self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
977
+
978
+ # Initialize weights and apply final processing
979
+ self.post_init()
980
+
981
+ def get_input_embeddings(self):
982
+ return self.model.tok_embeddings
983
+
984
+ def set_input_embeddings(self, value):
985
+ self.model.tok_embeddings = value
986
+
987
+ def get_output_embeddings(self):
988
+ return self.output
989
+
990
+ def set_output_embeddings(self, new_embeddings):
991
+ self.output = new_embeddings
992
+
993
+ def set_decoder(self, decoder):
994
+ self.model = decoder
995
+
996
+ def get_decoder(self):
997
+ return self.model
998
+
999
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
1000
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
1001
+ def forward(
1002
+ self,
1003
+ input_ids: torch.LongTensor = None,
1004
+ attention_mask: Optional[torch.Tensor] = None,
1005
+ position_ids: Optional[torch.LongTensor] = None,
1006
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1007
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1008
+ labels: Optional[torch.LongTensor] = None,
1009
+ use_cache: Optional[bool] = None,
1010
+ output_attentions: Optional[bool] = None,
1011
+ output_hidden_states: Optional[bool] = None,
1012
+ return_dict: Optional[bool] = None,
1013
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1014
+ r"""
1015
+ Args:
1016
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1017
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1018
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1019
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1020
+
1021
+ Returns:
1022
+
1023
+ Example:
1024
+
1025
+ ```python
1026
+ >>> from transformers import AutoTokenizer, InternLM2ForCausalLM
1027
+
1028
+ >>> model = InternLM2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
1029
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
1030
+
1031
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
1032
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
1033
+
1034
+ >>> # Generate
1035
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1036
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1037
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
1038
+ ```"""
1039
+
1040
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1041
+ output_hidden_states = (
1042
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1043
+ )
1044
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1045
+
1046
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1047
+ outputs = self.model(
1048
+ input_ids=input_ids,
1049
+ attention_mask=attention_mask,
1050
+ position_ids=position_ids,
1051
+ past_key_values=past_key_values,
1052
+ inputs_embeds=inputs_embeds,
1053
+ use_cache=use_cache,
1054
+ output_attentions=output_attentions,
1055
+ output_hidden_states=output_hidden_states,
1056
+ return_dict=return_dict,
1057
+ )
1058
+
1059
+ hidden_states = outputs[0]
1060
+ logits = self.output(hidden_states)
1061
+ logits = logits.float()
1062
+
1063
+ loss = None
1064
+ if labels is not None:
1065
+ # Shift so that tokens < n predict n
1066
+ shift_logits = logits[..., :-1, :].contiguous()
1067
+ shift_labels = labels[..., 1:].contiguous()
1068
+ # Flatten the tokens
1069
+ loss_fct = CrossEntropyLoss()
1070
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1071
+ shift_labels = shift_labels.view(-1)
1072
+ # Enable model parallelism
1073
+ shift_labels = shift_labels.to(shift_logits.device)
1074
+ loss = loss_fct(shift_logits, shift_labels)
1075
+
1076
+ if not return_dict:
1077
+ output = (logits,) + outputs[1:]
1078
+ return (loss,) + output if loss is not None else output
1079
+
1080
+ return CausalLMOutputWithPast(
1081
+ loss=loss,
1082
+ logits=logits,
1083
+ past_key_values=outputs.past_key_values,
1084
+ hidden_states=outputs.hidden_states,
1085
+ attentions=outputs.attentions,
1086
+ )
1087
+
1088
+ def prepare_inputs_for_generation(
1089
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
1090
+ ):
1091
+ if past_key_values is not None:
1092
+ past_length = past_key_values[0][0].shape[2]
1093
+
1094
+ # Some generation methods already pass only the last input ID
1095
+ if input_ids.shape[1] > past_length:
1096
+ remove_prefix_length = past_length
1097
+ else:
1098
+ # Default to old behavior: keep only final ID
1099
+ remove_prefix_length = input_ids.shape[1] - 1
1100
+
1101
+ input_ids = input_ids[:, remove_prefix_length:]
1102
+
1103
+ position_ids = kwargs.get("position_ids", None)
1104
+ if attention_mask is not None and position_ids is None:
1105
+ # create position_ids on the fly for batch generation
1106
+ position_ids = attention_mask.long().cumsum(-1) - 1
1107
+ position_ids.masked_fill_(attention_mask == 0, 1)
1108
+ if past_key_values:
1109
+ position_ids = position_ids[:, -input_ids.shape[1] :]
1110
+
1111
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1112
+ if inputs_embeds is not None and past_key_values is None:
1113
+ model_inputs = {"inputs_embeds": inputs_embeds}
1114
+ else:
1115
+ model_inputs = {"input_ids": input_ids}
1116
+
1117
+ model_inputs.update(
1118
+ {
1119
+ "position_ids": position_ids,
1120
+ "past_key_values": past_key_values,
1121
+ "use_cache": kwargs.get("use_cache"),
1122
+ "attention_mask": attention_mask,
1123
+ }
1124
+ )
1125
+ return model_inputs
1126
+
1127
+ @staticmethod
1128
+ def _reorder_cache(past_key_values, beam_idx):
1129
+ reordered_past = ()
1130
+ for layer_past in past_key_values:
1131
+ reordered_past += (
1132
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
1133
+ )
1134
+ return reordered_past
1135
+
1136
+ def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = [], meta_instruction=""):
1137
+ if tokenizer.add_bos_token:
1138
+ prompt = ""
1139
+ else:
1140
+ prompt = tokenizer.bos_token
1141
+ if meta_instruction:
1142
+ prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
1143
+ for record in history:
1144
+ prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
1145
+ prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
1146
+ return tokenizer([prompt], return_tensors="pt")
1147
+
1148
+ @torch.no_grad()
1149
+ def chat(
1150
+ self,
1151
+ tokenizer,
1152
+ query: str,
1153
+ history: List[Tuple[str, str]] = [],
1154
+ streamer: Optional[BaseStreamer] = None,
1155
+ max_new_tokens: int = 1024,
1156
+ do_sample: bool = True,
1157
+ temperature: float = 0.8,
1158
+ top_p: float = 0.8,
1159
+ meta_instruction: str = "You are an AI assistant whose name is InternLM (书生·浦语).\n"
1160
+ "- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
1161
+ "- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.",
1162
+ **kwargs,
1163
+ ):
1164
+ inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
1165
+ inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
1166
+ # also add end-of-assistant token in eos token id to avoid unnecessary generation
1167
+ eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(["<|im_end|>"])[0]]
1168
+ outputs = self.generate(
1169
+ **inputs,
1170
+ streamer=streamer,
1171
+ max_new_tokens=max_new_tokens,
1172
+ do_sample=do_sample,
1173
+ temperature=temperature,
1174
+ top_p=top_p,
1175
+ eos_token_id=eos_token_id,
1176
+ **kwargs,
1177
+ )
1178
+ outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :]
1179
+ response = tokenizer.decode(outputs, skip_special_tokens=True)
1180
+ response = response.split("<|im_end|>")[0]
1181
+ history = history + [(query, response)]
1182
+ return response, history
1183
+
1184
+ @torch.no_grad()
1185
+ def stream_chat(
1186
+ self,
1187
+ tokenizer,
1188
+ query: str,
1189
+ history: List[Tuple[str, str]] = [],
1190
+ max_new_tokens: int = 1024,
1191
+ do_sample: bool = True,
1192
+ temperature: float = 0.8,
1193
+ top_p: float = 0.8,
1194
+ **kwargs,
1195
+ ):
1196
+ """
1197
+ Return a generator in format: (response, history)
1198
+ Eg.
1199
+ ('你好，有什么可以帮助您的吗', [('你好', '你好，有什么可以帮助您的吗')])
1200
+ ('你好，有什么可以帮助您的吗？', [('你好', '你好，有什么可以帮助您的吗？')])
1201
+ """
1202
+ if BaseStreamer is None:
1203
+ raise ModuleNotFoundError(
1204
+ "The version of `transformers` is too low. Please make sure "
1205
+ "that you have installed `transformers>=4.28.0`."
1206
+ )
1207
+
1208
+ response_queue = queue.Queue(maxsize=20)
1209
+
1210
+ class ChatStreamer(BaseStreamer):
1211
+ def __init__(self, tokenizer) -> None:
1212
+ super().__init__()
1213
+ self.tokenizer = tokenizer
1214
+ self.queue = response_queue
1215
+ self.query = query
1216
+ self.history = history
1217
+ self.response = ""
1218
+ self.cache = []
1219
+ self.received_inputs = False
1220
+ self.queue.put((self.response, history + [(self.query, self.response)]))
1221
+
1222
+ def put(self, value):
1223
+ if len(value.shape) > 1 and value.shape[0] > 1:
1224
+ raise ValueError("ChatStreamer only supports batch size 1")
1225
+ elif len(value.shape) > 1:
1226
+ value = value[0]
1227
+
1228
+ if not self.received_inputs:
1229
+ # The first received value is input_ids, ignore here
1230
+ self.received_inputs = True
1231
+ return
1232
+
1233
+ self.cache.extend(value.tolist())
1234
+ token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
1235
+ if token.strip() != "<|im_end|>":
1236
+ self.response = self.response + token
1237
+ history = self.history + [(self.query, self.response)]
1238
+ self.queue.put((self.response, history))
1239
+ self.cache = []
1240
+ else:
1241
+ self.end()
1242
+
1243
+ def end(self):
1244
+ self.queue.put(None)
1245
+
1246
+ def stream_producer():
1247
+ return self.chat(
1248
+ tokenizer=tokenizer,
1249
+ query=query,
1250
+ streamer=ChatStreamer(tokenizer=tokenizer),
1251
+ history=history,
1252
+ max_new_tokens=max_new_tokens,
1253
+ do_sample=do_sample,
1254
+ temperature=temperature,
1255
+ top_p=top_p,
1256
+ **kwargs,
1257
+ )
1258
+
1259
+ def consumer():
1260
+ producer = threading.Thread(target=stream_producer)
1261
+ producer.start()
1262
+ while True:
1263
+ res = response_queue.get()
1264
+ if res is None:
1265
+ return
1266
+ yield res
1267
+
1268
+ return consumer()
1269
+
1270
+
1271
+ # Copied from transformers.model.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
1272
+ @add_start_docstrings(
1273
+ """
1274
+ The InternLM2 Model transformer with a sequence classification head on top (linear layer).
1275
+
1276
+ [`InternLM2ForSequenceClassification`] uses the last token in order to do the classification,
1277
+ as other causal models (e.g. GPT-2) do.
1278
+
1279
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1280
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1281
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1282
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1283
+ each row of the batch).
1284
+ """,
1285
+ InternLM2_START_DOCSTRING,
1286
+ )
1287
+ class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
1288
+ def __init__(self, config):
1289
+ super().__init__(config)
1290
+ self.num_labels = config.num_labels
1291
+ self.model = InternLM2Model(config)
1292
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1293
+
1294
+ # Initialize weights and apply final processing
1295
+ self.post_init()
1296
+
1297
+ def get_input_embeddings(self):
1298
+ return self.model.tok_embeddings
1299
+
1300
+ def set_input_embeddings(self, value):
1301
+ self.model.tok_embeddings = value
1302
+
1303
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
1304
+ def forward(
1305
+ self,
1306
+ input_ids: torch.LongTensor = None,
1307
+ attention_mask: Optional[torch.Tensor] = None,
1308
+ position_ids: Optional[torch.LongTensor] = None,
1309
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1310
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1311
+ labels: Optional[torch.LongTensor] = None,
1312
+ use_cache: Optional[bool] = None,
1313
+ output_attentions: Optional[bool] = None,
1314
+ output_hidden_states: Optional[bool] = None,
1315
+ return_dict: Optional[bool] = None,
1316
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1317
+ r"""
1318
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1319
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1320
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1321
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1322
+ """
1323
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1324
+
1325
+ transformer_outputs = self.model(
1326
+ input_ids,
1327
+ attention_mask=attention_mask,
1328
+ position_ids=position_ids,
1329
+ past_key_values=past_key_values,
1330
+ inputs_embeds=inputs_embeds,
1331
+ use_cache=use_cache,
1332
+ output_attentions=output_attentions,
1333
+ output_hidden_states=output_hidden_states,
1334
+ return_dict=return_dict,
1335
+ )
1336
+ hidden_states = transformer_outputs[0]
1337
+ logits = self.score(hidden_states)
1338
+
1339
+ if input_ids is not None:
1340
+ batch_size = input_ids.shape[0]
1341
+ else:
1342
+ batch_size = inputs_embeds.shape[0]
1343
+
1344
+ if self.config.pad_token_id is None and batch_size != 1:
1345
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
1346
+ if self.config.pad_token_id is None:
1347
+ sequence_lengths = -1
1348
+ else:
1349
+ if input_ids is not None:
1350
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
1351
+ logits.device
1352
+ )
1353
+ else:
1354
+ sequence_lengths = -1
1355
+
1356
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1357
+
1358
+ loss = None
1359
+ if labels is not None:
1360
+ labels = labels.to(logits.device)
1361
+ if self.config.problem_type is None:
1362
+ if self.num_labels == 1:
1363
+ self.config.problem_type = "regression"
1364
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1365
+ self.config.problem_type = "single_label_classification"
1366
+ else:
1367
+ self.config.problem_type = "multi_label_classification"
1368
+
1369
+ if self.config.problem_type == "regression":
1370
+ loss_fct = MSELoss()
1371
+ if self.num_labels == 1:
1372
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1373
+ else:
1374
+ loss = loss_fct(pooled_logits, labels)
1375
+ elif self.config.problem_type == "single_label_classification":
1376
+ loss_fct = CrossEntropyLoss()
1377
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1378
+ elif self.config.problem_type == "multi_label_classification":
1379
+ loss_fct = BCEWithLogitsLoss()
1380
+ loss = loss_fct(pooled_logits, labels)
1381
+ if not return_dict:
1382
+ output = (pooled_logits,) + transformer_outputs[1:]
1383
+ return ((loss,) + output) if loss is not None else output
1384
+
1385
+ return SequenceClassifierOutputWithPast(
1386
+ loss=loss,
1387
+ logits=pooled_logits,
1388
+ past_key_values=transformer_outputs.past_key_values,
1389
+ hidden_states=transformer_outputs.hidden_states,
1390
+ attentions=transformer_outputs.attentions,
1391
+ )