from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from torch import Tensor
from .base import ModelBase, gguf
from .qwen import Qwen2MoeModel
@ModelBase.register("Dots1ForCausalLM")
@ModelBase.example("rednote-hilab/dots.llm1.inst")
class Dots1Model(Qwen2MoeModel):
model_arch = gguf.MODEL_ARCH.DOTS1
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.hparams["num_experts"] = self.hparams["n_routed_experts"]
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
self.gguf_writer.add_expert_shared_count(self.hparams["n_shared_experts"])
self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
if "shared_experts" in name:
yield from ModelBase.modify_tensors(self, data_torch, name, bid)
else:
yield from super().modify_tensors(data_torch, name, bid)