| |
|
|
| from transformers.configuration_utils import PretrainedConfig |
|
|
|
|
| class BaichuanConfig(PretrainedConfig): |
| model_type = "baichuan" |
| keys_to_ignore_at_inference = ["past_key_values"] |
|
|
| def __init__( |
| self, |
| vocab_size=64000, |
| hidden_size=5120, |
| intermediate_size=13696, |
| num_hidden_layers=40, |
| num_attention_heads=40, |
| hidden_act="silu", |
| model_max_length=4096, |
| initializer_range=0.02, |
| rms_norm_eps=1e-6, |
| use_cache=True, |
| pad_token_id=0, |
| bos_token_id=1, |
| eos_token_id=2, |
| tie_word_embeddings=False, |
| gradient_checkpointing=False, |
| z_loss_weight=0, |
| **kwargs, |
| ): |
| self.vocab_size = vocab_size |
| self.model_max_length = model_max_length |
| 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.hidden_act = hidden_act |
| self.initializer_range = initializer_range |
| self.rms_norm_eps = rms_norm_eps |
| self.use_cache = use_cache |
| self.z_loss_weight = z_loss_weight |
| self.gradient_checkpointing = (gradient_checkpointing,) |
| super().__init__( |
| pad_token_id=pad_token_id, |
| bos_token_id=bos_token_id, |
| eos_token_id=eos_token_id, |
| tie_word_embeddings=tie_word_embeddings, |
| **kwargs, |
| ) |
|
|
