| 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) |
|
|
