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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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attribute_map = { |
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"hidden_size": "n_embd", |
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"num_attention_heads": "n_head", |
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"max_position_embeddings": "n_positions", |
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"num_hidden_layers": "n_layer", |
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} |
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def __init__( |
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self, |
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vocab_size=151851, |
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n_embd=4096, |
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n_layer=32, |
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n_head=32, |
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n_inner=None, |
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embd_pdrop=0.0, |
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attn_pdrop=0.0, |
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layer_norm_epsilon=1e-5, |
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initializer_range=0.02, |
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scale_attn_weights=True, |
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use_cache=True, |
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eos_token_id=151643, |
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apply_residual_connection_post_layernorm=False, |
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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=False, |
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use_logn_attn=False, |
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use_flash_attn=True, |
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ffn_hidden_size=22016, |
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no_bias=True, |
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tie_word_embeddings=False, |
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**kwargs, |
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): |
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self.eos_token_id = eos_token_id |
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super().__init__( |
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eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs |
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) |
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self.vocab_size = vocab_size |
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self.n_embd = n_embd |
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self.n_layer = n_layer |
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self.n_head = n_head |
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self.n_inner = n_inner |
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self.embd_pdrop = embd_pdrop |
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self.attn_pdrop = attn_pdrop |
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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.apply_residual_connection_post_layernorm = ( |
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apply_residual_connection_post_layernorm |
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) |
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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.ffn_hidden_size = ffn_hidden_size |
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self.no_bias = no_bias |
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self.tie_word_embeddings = tie_word_embeddings |
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