72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
# Copyright (c) Alibaba Cloud.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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from transformers import PretrainedConfig
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class QWenConfig(PretrainedConfig):
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model_type = "qwen"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=151936,
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hidden_size=4096,
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num_hidden_layers=32,
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num_attention_heads=32,
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emb_dropout_prob=0.0,
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attn_dropout_prob=0.0,
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layer_norm_epsilon=1e-6,
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initializer_range=0.02,
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max_position_embeddings=8192,
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scale_attn_weights=True,
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use_cache=True,
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bf16=False,
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fp16=False,
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fp32=False,
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kv_channels=128,
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rotary_pct=1.0,
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rotary_emb_base=10000,
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use_dynamic_ntk=True,
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use_logn_attn=True,
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use_flash_attn="auto",
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intermediate_size=22016,
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no_bias=True,
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tie_word_embeddings=False,
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use_cache_quantization=False,
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use_cache_kernel=False,
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softmax_in_fp32=False,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.emb_dropout_prob = emb_dropout_prob
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self.attn_dropout_prob = attn_dropout_prob
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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self.scale_attn_weights = scale_attn_weights
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self.use_cache = use_cache
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self.max_position_embeddings = max_position_embeddings
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self.bf16 = bf16
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self.fp16 = fp16
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self.fp32 = fp32
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self.kv_channels = kv_channels
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self.rotary_pct = rotary_pct
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self.rotary_emb_base = rotary_emb_base
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self.use_dynamic_ntk = use_dynamic_ntk
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self.use_logn_attn = use_logn_attn
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self.use_flash_attn = use_flash_attn
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self.no_bias = no_bias
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self.use_cache_quantization = use_cache_quantization
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self.use_cache_kernel = use_cache_kernel
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self.softmax_in_fp32 = softmax_in_fp32
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super().__init__(
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tie_word_embeddings=tie_word_embeddings,
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**kwargs
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)
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