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68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from torch import Tensor
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from .base import ModelBase, TextModel, gguf
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from .deepseek import DeepseekV2Model
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from .llama import LlamaModel
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@ModelBase.register(
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"Mistral3ForConditionalGeneration",
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"Ministral3ForCausalLM",
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)
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class Mistral3Model(TextModel):
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class Ministral3Model(LlamaModel):
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model_arch = gguf.MODEL_ARCH.MISTRAL3
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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rope_params = self.rope_parameters
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if self.hparams.get("model_type") == "ministral3":
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assert rope_params, "ministral3 must have 'rope_parameters' config"
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assert rope_params["rope_type"] == "yarn", "ministral3 rope_type must be 'yarn'"
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self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"])
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self.gguf_writer.add_attn_temperature_scale(rope_params["llama_4_scaling_beta"])
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class Mistral4Model(DeepseekV2Model):
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model_arch = gguf.MODEL_ARCH.MISTRAL4
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skip_mtp = False # model contains no MTP layers, so no need to skip
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merge_expert = False # experts are already stacked as 3D
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def modify_tensors(self, data_torch, name, bid):
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if name.endswith(".down_proj") or name.endswith(".gate_up_proj"):
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name = name + ".weight"
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yield from super().modify_tensors(data_torch, name, bid)
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model_arch = gguf.MODEL_ARCH.MISTRAL3 # unused
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impl: TextModel
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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if self.hparams.get("model_type") == "mistral4":
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self.impl = Mistral3Model.Mistral4Model(*args, **kwargs)
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else:
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self.impl = Mistral3Model.Ministral3Model(*args, **kwargs)
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def set_vocab(self):
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self.impl.set_vocab()
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def set_gguf_parameters(self):
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self.impl.set_gguf_parameters()
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
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yield from self.impl.modify_tensors(data_torch, name, bid)
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def prepare_tensors(self):
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self.impl.prepare_tensors()
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def write_vocab(self):
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self.impl.write_vocab()
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def write(self):
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self.impl.write()
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