| """HuggingFace Transformers config for ExpIvme-DiffusionConversate-v1-Instruct.""" |
|
|
| from transformers import PretrainedConfig |
|
|
|
|
| class ExpIvmeDiffusionConfig(PretrainedConfig): |
| model_type = "expivme_diffusion" |
|
|
| def __init__(self, vocab_size=16004, hidden_dim=896, n_layers=12, n_heads=14, |
| context_len=1024, ffn_mult=4.0, rope_theta=10_000.0, norm_eps=1e-5, |
| tie_embeddings=True, dropout=0.0, mask_token_id=16000, |
| user_token_id=16001, assistant_token_id=16002, endturn_token_id=16003, **kwargs): |
| self.vocab_size = vocab_size |
| self.hidden_dim = hidden_dim |
| self.n_layers = n_layers |
| self.n_heads = n_heads |
| self.context_len = context_len |
| self.ffn_mult = ffn_mult |
| self.rope_theta = rope_theta |
| self.norm_eps = norm_eps |
| self.dropout = dropout |
| self.mask_token_id = mask_token_id |
| self.user_token_id = user_token_id |
| self.assistant_token_id = assistant_token_id |
| self.endturn_token_id = endturn_token_id |
| self.max_position_embeddings = context_len |
| self.num_hidden_layers = n_layers |
| self.num_attention_heads = n_heads |
| self.hidden_size = hidden_dim |
| kwargs.setdefault("tie_word_embeddings", tie_embeddings) |
| super().__init__(**kwargs) |
|
|
| @property |
| def head_dim(self): |
| return self.hidden_dim // self.n_heads |
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|
