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Running
on
Zero
| from transformers.configuration_utils import PretrainedConfig | |
| class StockLlamaConfig(PretrainedConfig): | |
| model_type = "stockllama" | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| def __init__( | |
| self, | |
| vocab_size=32000, | |
| hidden_size=4096, | |
| intermediate_size=11008, | |
| num_hidden_layers=32, | |
| num_attention_heads=32, | |
| num_key_value_heads=None, | |
| hidden_act="silu", | |
| max_position_embeddings=2048, | |
| term_number=4, | |
| initializer_range=0.02, | |
| rms_norm_eps=1e-6, | |
| use_cache=True, | |
| pad_token_id=None, | |
| bos_token_id=1, | |
| eos_token_id=2, | |
| pretraining_tp=1, | |
| tie_word_embeddings=False, | |
| rope_theta=10000.0, | |
| rope_scaling=None, | |
| attention_bias=False, | |
| attention_dropout=0.0, | |
| mlp_bias=False, | |
| head_dim=None, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.max_position_embeddings = max_position_embeddings | |
| self.term_number = term_number | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| if num_key_value_heads is None: | |
| num_key_value_heads = num_attention_heads | |
| self.num_key_value_heads = num_key_value_heads | |
| self.hidden_act = hidden_act | |
| self.initializer_range = initializer_range | |
| self.rms_norm_eps = rms_norm_eps | |
| self.pretraining_tp = pretraining_tp | |
| self.use_cache = use_cache | |
| self.rope_theta = rope_theta | |
| self.rope_scaling = rope_scaling | |
| self.attention_bias = attention_bias | |
| self.attention_dropout = attention_dropout | |
| self.mlp_bias = mlp_bias | |
| self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads | |
| 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, | |
| ) | |