| | |
| | |
| | |
| | |
| |
|
| | from transformers import PretrainedConfig |
| |
|
| |
|
| | class QWenConfig(PretrainedConfig): |
| | model_type = "qwen" |
| | keys_to_ignore_at_inference = ["past_key_values"] |
| |
|
| | def __init__( |
| | self, |
| | vocab_size=151936, |
| | hidden_size=4096, |
| | num_hidden_layers=32, |
| | num_attention_heads=32, |
| | emb_dropout_prob=0.0, |
| | attn_dropout_prob=0.0, |
| | layer_norm_epsilon=1e-6, |
| | initializer_range=0.02, |
| | max_position_embeddings=8192, |
| | scale_attn_weights=True, |
| | use_cache=True, |
| | bf16=False, |
| | fp16=False, |
| | fp32=False, |
| | kv_channels=128, |
| | rotary_pct=1.0, |
| | rotary_emb_base=10000, |
| | use_dynamic_ntk=True, |
| | use_logn_attn=True, |
| | use_flash_attn="auto", |
| | intermediate_size=22016, |
| | no_bias=True, |
| | tie_word_embeddings=False, |
| | **kwargs, |
| | ): |
| | self.vocab_size = vocab_size |
| | self.hidden_size = hidden_size |
| | self.intermediate_size = intermediate_size |
| | self.num_hidden_layers = num_hidden_layers |
| | self.num_attention_heads = num_attention_heads |
| | self.emb_dropout_prob = emb_dropout_prob |
| | self.attn_dropout_prob = attn_dropout_prob |
| | 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.max_position_embeddings = max_position_embeddings |
| | 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.no_bias = no_bias |
| | super().__init__( |
| | tie_word_embeddings=tie_word_embeddings, |
| | **kwargs |
| | ) |
| |
|
