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| from transformers import PretrainedConfig |
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| class QWenConfig(PretrainedConfig): |
| model_type = "qwen" |
| keys_to_ignore_at_inference = ["past_key_values"] |
| attribute_map = { |
| "hidden_size": "n_embd", |
| "num_attention_heads": "n_head", |
| "max_position_embeddings": "n_positions", |
| "num_hidden_layers": "n_layer", |
| } |
|
|
| def __init__( |
| self, |
| vocab_size=151851, |
| n_embd=4096, |
| n_layer=32, |
| n_head=32, |
| n_inner=None, |
| embd_pdrop=0.0, |
| attn_pdrop=0.0, |
| layer_norm_epsilon=1e-5, |
| initializer_range=0.02, |
| scale_attn_weights=True, |
| use_cache=True, |
| eos_token_id=151643, |
| apply_residual_connection_post_layernorm=False, |
| bf16=False, |
| fp16=False, |
| fp32=False, |
| kv_channels=128, |
| rotary_pct=1.0, |
| rotary_emb_base=10000, |
| use_dynamic_ntk=False, |
| use_logn_attn=False, |
| use_flash_attn=True, |
| ffn_hidden_size=22016, |
| no_bias=True, |
| tie_word_embeddings=False, |
| **kwargs, |
| ): |
| self.eos_token_id = eos_token_id |
| super().__init__( |
| eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs |
| ) |
|
|
| self.vocab_size = vocab_size |
| self.n_embd = n_embd |
| self.n_layer = n_layer |
| self.n_head = n_head |
| self.n_inner = n_inner |
| self.embd_pdrop = embd_pdrop |
| self.attn_pdrop = attn_pdrop |
| self.layer_norm_epsilon = layer_norm_epsilon |
| self.initializer_range = initializer_range |
| self.scale_attn_weights = scale_attn_weights |
| self.use_cache = use_cache |
| self.apply_residual_connection_post_layernorm = ( |
| apply_residual_connection_post_layernorm |
| ) |
| self.bf16 = bf16 |
| self.fp16 = fp16 |
| self.fp32 = fp32 |
| self.kv_channels = kv_channels |
| self.rotary_pct = rotary_pct |
| self.rotary_emb_base = rotary_emb_base |
| self.use_dynamic_ntk = use_dynamic_ntk |
| self.use_logn_attn = use_logn_attn |
| self.use_flash_attn = use_flash_attn |
| self.ffn_hidden_size = ffn_hidden_size |
| self.no_bias = no_bias |
| self.tie_word_embeddings = tie_word_embeddings |
|
|
