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| """Vedika-Code-Pro-v1 model configuration.""" |
|
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| from transformers import PretrainedConfig |
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| class VedikaCodeProV1Config(PretrainedConfig): |
| """Configuration class for Vedika-Code-Pro-v1 models.""" |
| |
| model_type = "vedika_code_pro_v1" |
| keys_to_ignore_at_inference = ["past_key_values"] |
| |
| def __init__( |
| self, |
| vocab_size=129280, |
| hidden_size=7168, |
| moe_intermediate_size=3072, |
| num_hidden_layers=61, |
| num_hash_layers=3, |
| num_attention_heads=128, |
| num_key_value_heads=1, |
| n_routed_experts=384, |
| n_shared_experts=1, |
| num_experts_per_tok=6, |
| scoring_func="sqrtsoftplus", |
| routed_scaling_factor=2.5, |
| swiglu_limit=10.0, |
| q_lora_rank=1536, |
| head_dim=512, |
| qk_rope_head_dim=64, |
| o_groups=16, |
| o_lora_rank=1024, |
| sliding_window=128, |
| rope_theta=10000.0, |
| rope_scaling=None, |
| compress_rope_theta=160000.0, |
| compress_ratios=None, |
| rms_norm_eps=1e-6, |
| max_batch_size=4, |
| max_position_embeddings=1048576, |
| hc_mult=4, |
| hc_sinkhorn_iters=20, |
| hc_eps=1e-6, |
| index_n_heads=64, |
| index_head_dim=128, |
| index_topk=1024, |
| attention_dropout=0.0, |
| initializer_range=0.02, |
| tie_word_embeddings=False, |
| bos_token_id=0, |
| eos_token_id=1, |
| **kwargs, |
| ): |
| self.vocab_size = vocab_size |
| self.hidden_size = hidden_size |
| self.moe_intermediate_size = moe_intermediate_size |
| self.num_hidden_layers = num_hidden_layers |
| self.num_hash_layers = num_hash_layers |
| self.num_attention_heads = num_attention_heads |
| self.num_key_value_heads = num_key_value_heads |
| self.n_routed_experts = n_routed_experts |
| self.n_shared_experts = n_shared_experts |
| self.num_experts_per_tok = num_experts_per_tok |
| self.scoring_func = scoring_func |
| self.routed_scaling_factor = routed_scaling_factor |
| self.swiglu_limit = swiglu_limit |
| self.q_lora_rank = q_lora_rank |
| self.head_dim = head_dim |
| self.qk_rope_head_dim = qk_rope_head_dim |
| self.o_groups = o_groups |
| self.o_lora_rank = o_lora_rank |
| self.sliding_window = sliding_window |
| self.rope_theta = rope_theta |
| self.rope_scaling = rope_scaling if rope_scaling is not None else {} |
| self.compress_rope_theta = compress_rope_theta |
| self.compress_ratios = compress_ratios if compress_ratios is not None else [] |
| self.rms_norm_eps = rms_norm_eps |
| self.max_batch_size = max_batch_size |
| self.max_position_embeddings = max_position_embeddings |
| self.hc_mult = hc_mult |
| self.hc_sinkhorn_iters = hc_sinkhorn_iters |
| self.hc_eps = hc_eps |
| self.index_n_heads = index_n_heads |
| self.index_head_dim = index_head_dim |
| self.index_topk = index_topk |
| self.attention_dropout = attention_dropout |
| self.initializer_range = initializer_range |
| self.tie_word_embeddings = tie_word_embeddings |
| self.num_nextn_predict_layers = kwargs.get("num_nextn_predict_layers", 1) |
| |
| super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) |
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|
