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42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
from __future__ import annotations
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from typing import Sequence
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from .base import gguf
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from .llava import LlavaVisionModel
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class PixtralModel(LlavaVisionModel):
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model_name = "Pixtral"
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hf_arch = ""
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is_mistral_format = True
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PIXTRAL)
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self.gguf_writer.add_vision_attention_layernorm_eps(
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self.find_hparam(["norm_eps"])
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)
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self.gguf_writer.add_rope_freq_base(self.find_vparam(["rope_theta"]))
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self.gguf_writer.add_vision_use_silu(True)
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# spatial_merge_size
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if self.find_vparam(["mm_projector_id"], optional=True) == "patch_merge":
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self.gguf_writer.add_vision_spatial_merge_size(
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self.find_vparam(["spatial_merge_size"])
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)
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def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
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if name == "vision_language_adapter.w_in.weight":
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return "mm.1.weight"
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elif name == "vision_language_adapter.w_in.bias":
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return "mm.1.bias"
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elif name == "vision_language_adapter.w_out.weight":
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return "mm.2.weight"
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elif name == "vision_language_adapter.w_out.bias":
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return "mm.2.bias"
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return super().map_tensor_name(name, try_suffixes)
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