| from __future__ import annotations |
|
|
| from typing import Callable, TYPE_CHECKING |
|
|
| if TYPE_CHECKING: |
| from torch import Tensor |
|
|
| from .base import MmprojModel, ModelBase, gguf |
|
|
| from .llama import LlamaModel |
|
|
|
|
| @ModelBase.register("CogVLMForCausalLM") |
| @ModelBase.example("THUDM/cogvlm2-llama3-chat-19B", "THUDM/cogvlm-chat-hf") |
| class CogVLMVisionModel(MmprojModel): |
|
|
| def set_gguf_parameters(self): |
| super().set_gguf_parameters() |
| self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6)) |
| self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.COGVLM) |
|
|
| @classmethod |
| def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: |
| name, gen = item |
|
|
| if not name.startswith("model.vision."): |
| return None |
|
|
| return super().filter_tensors(item) |
|
|
|
|
| @ModelBase.register("CogVLMForCausalLM") |
| @ModelBase.example("THUDM/cogvlm2-llama3-chat-19B", "THUDM/cogvlm-chat-hf") |
| class CogVLMModel(LlamaModel): |
| model_arch = gguf.MODEL_ARCH.COGVLM |
|
|
