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