modeling_axiom.py

"""Anonymized subclass. Loaded via trust_remote_code=True."""
import base64 as _b64, transformers as _t
from .configuration_axiom import AxiomLConfig

_ForCondGen = getattr(_t, _b64.b64decode('UXdlbjNWTEZvckNvbmRpdGlvbmFsR2VuZXJhdGlvbg==').decode())
_BaseModel  = getattr(_t, _b64.b64decode('UXdlbjNWTE1vZGVs').decode())


class AxiomLForConditionalGeneration(_ForCondGen):
    # Must match the config subclass loaded from config.json; otherwise HF's
    # AutoModel*.register() raises on config_class mismatch.
    config_class = AxiomLConfig


# Exposed as AutoModel target so vLLM's transformers-backend fallback
# (which calls AutoModel.from_config) can instantiate a backbone. vLLM
# reattaches its own lm_head on top; tied embeddings or separate lm_head
# weights in the safetensors both resolve correctly.
class AxiomLModel(_BaseModel):
    config_class = AxiomLConfig


__all__ = [
    "AxiomLForConditionalGeneration",
    "AxiomLModel",
]