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# Copyright 2025 Veda Labs. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Vedika-Code-Pro-v1 model configuration."""
from transformers import PretrainedConfig
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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