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Model: abetlen/Phi-3.5-vision-instruct-gguf
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2026-06-20 03:16:17 +08:00
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MIT License
Copyright (c) Microsoft Corporation.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE

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---
license: mit
---
work-in-progress does not currently work with main branch of llama.cpp

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import os
import typing
import argparse
import numpy as np
import torch
from gguf import *
from safetensors import safe_open
def k(raw_key: str, arch: str) -> str:
return raw_key.format(arch=arch)
class Args:
def __init__(self, model, output):
self.model = model
self.output = output
class SafetensorsIndexFile(typing.TypedDict):
weight_map: typing.Dict[str, str]
class SafetensorsIndex:
def __init__(self, index_file_path: str):
directory = os.path.dirname(index_file_path)
self.index = typing.cast(SafetensorsIndexFile, json.load(open(index_file_path)))
self.weight_map = self.index["weight_map"]
files = set(self.weight_map.values())
self.tensors = {file: safe_open(os.path.join(directory, file), framework="pt") for file in files}
def get_tensor(self, key: str) -> npt.NDArray[np.float32]:
# convert to float32 and cast to np array
return typing.cast(npt.NDArray[np.float32], self.tensors[self.weight_map[key]].get_tensor(key).to(torch.float32).numpy())
def main():
parser = argparse.ArgumentParser(description="Extract vision model from safetensors to GGUF")
parser.add_argument("--model", type=str, required=True, help="Input safetensors file")
parser.add_argument("--output", type=str, required=True, help="Output GGUF file")
args = parser.parse_args()
import pathlib
dir_model = pathlib.Path(args.model)
config = json.load(open(dir_model / "config.json"))
# tensors = safe_open(args.model, framework="np", device="cpu")
tensors = SafetensorsIndex((dir_model / "model.safetensors.index.json").as_posix())
ftype = 1 # fp16
# source https://github.com/huggingface/transformers/blob/87134662f73d5e89bb015531ddd1d4662371d317/src/transformers/models/clip/configuration_clip.py#L209
# hidden_size=768,
# intermediate_size=3072,
# projection_dim=512,
# num_hidden_layers=12,
# num_attention_heads=12,
# num_channels=3,
# image_size=224,
# patch_size=32,
# hidden_act="quick_gelu",
# layer_norm_eps=1e-5,
# attention_dropout=0.0,
# initializer_range=0.02,
# initializer_factor=1.0,
clip_vision_config = {
"hidden_size": 768,
"intermediate_size": 3072,
"projection_dim": 512,
"num_hidden_layers": 12,
"num_attention_heads": 12,
"num_channels": 3,
"image_size": 224,
"patch_size": 32,
"hidden_act": "quick_gelu",
"layer_norm_eps": 1e-5,
"attention_dropout": 0.0,
"initializer_range": 0.02,
"initializer_factor": 1.0,
}
# CLIP_VIT_LARGE_PATCH14_336_CONFIG = CLIPVisionConfig(
# attention_dropout=0.0,
# dropout=0.0,
# hidden_act="quick_gelu",
# hidden_size=1024,
# image_size=336,
# initializer_factor=1.0,
# initializer_range=0.02,
# intermediate_size=4096,
# layer_norm_eps=1e-05,
# num_attention_heads=16,
# num_channels=3,
# num_hidden_layers=24,
# patch_size=14,
# projection_dim=768
# )
clip_vision_config.update(dict(
attention_dropout=0.0,
dropout=0.0,
hidden_act="quick_gelu",
hidden_size=1024,
image_size=336,
initializer_factor=1.0,
initializer_range=0.02,
intermediate_size=4096,
layer_norm_eps=1e-05,
num_attention_heads=16,
num_channels=3,
num_hidden_layers=24,
patch_size=14,
projection_dim=768
))
fout = GGUFWriter(args.output, arch="clip")
fout.add_bool("clip.has_text_encoder", False)
fout.add_bool("clip.has_vision_encoder", True)
fout.add_bool("clip.has_llava_projector", True)
fout.add_file_type(ftype)
model_name = "microsoft/phi-3.5-vision-instruct"
fout.add_name(model_name)
fout.add_description("image encoder for " + model_name)
fout.add_string("clip.projector_type", "mlp")
# Vision model hparams
VISION = "clip.vision"
fout.add_uint32("clip.vision.image_size", clip_vision_config["image_size"])
fout.add_uint32("clip.vision.patch_size", clip_vision_config["patch_size"])
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), clip_vision_config["hidden_size"])
fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), clip_vision_config["intermediate_size"])
fout.add_uint32("clip.vision.projection_dim", clip_vision_config["projection_dim"])
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), clip_vision_config["num_attention_heads"])
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), clip_vision_config["layer_norm_eps"])
fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), clip_vision_config["num_hidden_layers"])
fout.add_array("clip.vision.image_mean", [0.48145466, 0.4578275, 0.40821073])
fout.add_array("clip.vision.image_std", [0.26862954, 0.26130258, 0.27577711])
fout.add_bool("clip.use_gelu", clip_vision_config["hidden_act"] != "quick_gelu")
# Vision model tensors
prefix = "model.vision_embed_tokens.img_processor.vision_model."
fout.add_tensor(
"v.class_embd",
tensors.get_tensor(f"{prefix}embeddings.class_embedding").astype(np.float32),
)
fout.add_tensor(
"v.patch_embd.weight",
tensors.get_tensor(f"{prefix}embeddings.patch_embedding.weight")
.reshape(clip_vision_config["hidden_size"], 3, clip_vision_config["patch_size"], clip_vision_config["patch_size"])
.astype(np.float16),
)
fout.add_tensor(
"v.position_embd.weight",
tensors.get_tensor(f"{prefix}embeddings.position_embedding.weight").astype(np.float16),
)
fout.add_tensor(
"v.sub_gn",
tensors.get_tensor("model.vision_embed_tokens.sub_GN").astype(np.float32),
)
fout.add_tensor(
"v.glb_gn",
tensors.get_tensor("model.vision_embed_tokens.glb_GN").astype(np.float32),
)
fout.add_tensor(
"mm.0.weight",
tensors.get_tensor("model.vision_embed_tokens.img_projection.0.weight").astype(np.float16),
)
fout.add_tensor(
"mm.0.bias",
tensors.get_tensor("model.vision_embed_tokens.img_projection.0.bias").astype(np.float32),
)
fout.add_tensor(
"mm.2.weight",
tensors.get_tensor("model.vision_embed_tokens.img_projection.2.weight").astype(np.float16),
)
fout.add_tensor(
"mm.2.bias",
tensors.get_tensor("model.vision_embed_tokens.img_projection.2.bias").astype(np.float32),
)
for i in range(clip_vision_config["num_hidden_layers"]):
# attention norm
fout.add_tensor(
f"v.blk.{i}.attn_norm.weight",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.layer_norm1.weight").astype(np.float32),
)
fout.add_tensor(
f"v.blk.{i}.attn_norm.bias",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.layer_norm1.bias").astype(np.float32),
)
fout.add_tensor(
f"v.blk.{i}.ffn_norm.weight",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.layer_norm2.weight").astype(np.float32),
)
fout.add_tensor(
f"v.blk.{i}.ffn_norm.bias",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.layer_norm2.bias").astype(np.float32),
)
# feed forward
fout.add_tensor(
f"v.blk.{i}.ffn_down.weight",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.mlp.fc1.weight").astype(np.float16),
)
fout.add_tensor(
f"v.blk.{i}.ffn_down.bias",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.mlp.fc1.bias").astype(np.float32),
)
fout.add_tensor(
f"v.blk.{i}.ffn_up.weight",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.mlp.fc2.weight").astype(np.float16),
)
fout.add_tensor(
f"v.blk.{i}.ffn_up.bias",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.mlp.fc2.bias").astype(np.float32),
)
# attention
fout.add_tensor(
f"v.blk.{i}.attn_k.weight",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.self_attn.k_proj.weight").astype(np.float16),
)
fout.add_tensor(
f"v.blk.{i}.attn_k.bias",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.self_attn.k_proj.bias").astype(np.float32),
)
fout.add_tensor(
f"v.blk.{i}.attn_out.weight",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.self_attn.out_proj.weight").astype(np.float16),
)
fout.add_tensor(
f"v.blk.{i}.attn_out.bias",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.self_attn.out_proj.bias").astype(np.float32),
)
fout.add_tensor(
f"v.blk.{i}.attn_q.weight",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.self_attn.q_proj.weight").astype(np.float16),
)
fout.add_tensor(
f"v.blk.{i}.attn_q.bias",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.self_attn.q_proj.bias").astype(np.float32),
)
fout.add_tensor(
f"v.blk.{i}.attn_v.weight",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.self_attn.v_proj.weight").astype(np.float16),
)
fout.add_tensor(
f"v.blk.{i}.attn_v.bias",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.self_attn.v_proj.bias").astype(np.float32),
)
# layer norm
fout.add_tensor(
f"v.blk.{i}.ln1.weight",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.layer_norm1.weight").astype(np.float32),
)
fout.add_tensor(
f"v.blk.{i}.ln1.bias",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.layer_norm1.bias").astype(np.float32),
)
fout.add_tensor(
f"v.blk.{i}.ln2.weight",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.layer_norm2.weight").astype(np.float32),
)
fout.add_tensor(
f"v.blk.{i}.ln2.bias",
tensors.get_tensor(f"{prefix}encoder.layers.{i}.layer_norm2.bias").astype(np.float32),
)
fout.add_tensor(
"v.post_ln.weight",
tensors.get_tensor(f"{prefix}post_layernorm.weight").astype(np.float32),
)
fout.add_tensor(
"v.post_ln.bias",
tensors.get_tensor(f"{prefix}post_layernorm.bias").astype(np.float32),
)
fout.add_tensor(
"v.pre_ln.weight",
tensors.get_tensor(f"{prefix}pre_layrnorm.weight").astype(np.float32),
)
fout.add_tensor(
"v.pre_ln.bias",
tensors.get_tensor(f"{prefix}pre_layrnorm.bias").astype(np.float32),
)
fout.write_header_to_file()
fout.write_kv_data_to_file()
fout.write_tensors_to_file()
fout.close()
if __name__ == "__main__":
main()