class FlexOlmoConfig(PretrainedConfig):
model_type = "flex_olmo"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=100352,
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=4096,
initializer_range=0.02,
rms_norm_eps=1e-06,
use_cache=True,
pad_token_id=100277,
bos_token_id=None,
eos_token_id=100257,
tie_word_embeddings=False,
rope_theta=500000.0,
rope_parameters=None,
attention_bias=False,
attention_dropout=0.0,
num_experts_per_tok=5,
num_experts=7,
output_router_logits=False,
router_aux_loss_coef=0.01,
norm_topk_prob=False,
**kwargs,
):
if "architectures" not in kwargs:
kwargs["architectures"] = ["FlexOlmoForCausalLM"]
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,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
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.use_cache = use_cache
self.rope_theta = rope_theta
# Try to set `rope_scaling` if available, otherwise use `rope_parameters`
rope_scaling = kwargs.pop("rope_scaling", None)
self.rope_parameters = rope_scaling or rope_parameters
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.output_router_logits = output_router_logits
self.router_aux_loss_coef = router_aux_loss_coef
self.norm_topk_prob = norm_topk_prob
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, move it to 'rope_type'.
if self.rope_parameters is not None and "type" in self.rope_parameters:
self.rope_parameters["rope_type"] = self.rope_parameters["type"]