File size: 8,409 Bytes
fe309fc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 |
---
library_name: transformers
pipeline_tag: image-text-to-text
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
base_model:
- zai-org/GLM-4.5V
---
This tiny model is for debugging. It is randomly initialized with the config adapted from [zai-org/GLM-4.5V](https://huggingface.co/zai-org/GLM-4.5V).
### Example usage:
```python
import torch
from transformers import AutoProcessor, Glm4vMoeForConditionalGeneration
model_id = "tiny-random/glm-4.5v"
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"url": "https://upload.wikimedia.org/wikipedia/commons/f/fa/Grayscale_8bits_palette_sample_image.png"
},
{
"type": "text",
"text": "describe this image"
}
],
}
]
processor = AutoProcessor.from_pretrained(model_id)
model = Glm4vMoeForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt"
).to(model.device)
inputs.pop("token_type_ids", None)
generated_ids = model.generate(**inputs, max_new_tokens=16)
output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
print(output_text)
```
### Codes to create this repo:
```python
import json
from pathlib import Path
import accelerate
import torch
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoProcessor,
GenerationConfig,
Glm4vForConditionalGeneration,
Glm4vMoeForConditionalGeneration,
set_seed,
)
from transformers.models.glm4v_moe.modeling_glm4v_moe import Glm4vMoeTextTopkRouter
source_model_id = "zai-org/GLM-4.5V"
save_folder = "/tmp/tiny-random/glm-4.5v"
processor = AutoProcessor.from_pretrained(source_model_id, trust_remote_code=True)
processor.save_pretrained(save_folder)
with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f:
config_json = json.load(f)
config_json['text_config'].update({
"hidden_size": 32,
"head_dim": 32,
"intermediate_size": 128,
"first_k_dense_replace": 1,
"moe_intermediate_size": 64,
"num_attention_heads": 2,
"num_key_value_heads": 1,
"num_hidden_layers": 2, # one dense, one moe
"tie_word_embeddings": True,
})
config_json['text_config']['rope_scaling']['mrope_section'] = [2, 2, 4]
config_json['vision_config']['hidden_size'] = 64
config_json['vision_config']['depth'] = 2
config_json['vision_config']['num_heads'] = 2
config_json['vision_config']['intermediate_size'] = 128
config_json['vision_config']['out_hidden_size'] = config_json['text_config']['hidden_size']
with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
json.dump(config_json, f, indent=2)
config = AutoConfig.from_pretrained(
save_folder,
trust_remote_code=True,
)
print(config)
torch.set_default_dtype(torch.bfloat16)
model = Glm4vMoeForConditionalGeneration(config)
torch.set_default_dtype(torch.float32)
if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'):
model.generation_config = GenerationConfig.from_pretrained(
source_model_id, trust_remote_code=True,
)
set_seed(42)
model = model.cpu() # cpu is more stable for random initialization across machines
num_params = sum(p.numel() for p in model.parameters())
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
torch.nn.init.normal_(p, 0, 0.1)
print(name, p.shape, p.dtype, p.device, f'{p.numel() / num_params * 100: .2f}%')
for _, m in sorted(model.named_modules()):
if isinstance(m, Glm4vMoeTextTopkRouter):
assert 'e_score_correction_bias' in m.state_dict()
torch.nn.init.normal_(m.e_score_correction_bias, 0, 1)
model.save_pretrained(save_folder)
print(model)
```
### Printing the model:
```text
Glm4vMoeForConditionalGeneration(
(model): Glm4vMoeModel(
(visual): Glm4vMoeVisionModel(
(embeddings): Glm4vMoeVisionEmbeddings(
(position_embedding): Embedding(576, 64)
)
(patch_embed): Glm4vMoeVisionPatchEmbed(
(proj): Conv3d(3, 64, kernel_size=(2, 14, 14), stride=(2, 14, 14))
)
(rotary_pos_emb): Glm4vMoeVisionRotaryEmbedding()
(blocks): ModuleList(
(0-1): 2 x Glm4vMoeVisionBlock(
(norm1): Glm4vMoeRMSNorm((64,), eps=1e-05)
(norm2): Glm4vMoeRMSNorm((64,), eps=1e-05)
(attn): Glm4vMoeVisionAttention(
(qkv): Linear(in_features=64, out_features=192, bias=False)
(proj): Linear(in_features=64, out_features=64, bias=False)
)
(mlp): Glm4vMoeisionMlp(
(gate_proj): Linear(in_features=64, out_features=32, bias=False)
(up_proj): Linear(in_features=64, out_features=32, bias=False)
(down_proj): Linear(in_features=32, out_features=64, bias=False)
(act_fn): SiLU()
)
)
)
(merger): Glm4vMoeVisionPatchMerger(
(proj): Linear(in_features=32, out_features=32, bias=False)
(post_projection_norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True)
(gate_proj): Linear(in_features=32, out_features=128, bias=False)
(up_proj): Linear(in_features=32, out_features=128, bias=False)
(down_proj): Linear(in_features=128, out_features=32, bias=False)
(act1): GELU(approximate='none')
(act_fn): SiLU()
)
(post_conv_layernorm): Glm4vMoeRMSNorm((64,), eps=1e-05)
(downsample): Conv2d(64, 32, kernel_size=(2, 2), stride=(2, 2))
(post_layernorm): Glm4vMoeRMSNorm((64,), eps=1e-05)
)
(language_model): Glm4vMoeTextModel(
(embed_tokens): Embedding(151552, 32, padding_idx=151329)
(layers): ModuleList(
(0): Glm4vMoeTextDecoderLayer(
(self_attn): Glm4vMoeTextAttention(
(q_proj): Linear(in_features=32, out_features=64, bias=True)
(k_proj): Linear(in_features=32, out_features=32, bias=True)
(v_proj): Linear(in_features=32, out_features=32, bias=True)
(o_proj): Linear(in_features=64, out_features=32, bias=False)
)
(mlp): Glm4vMoeTextMLP(
(gate_proj): Linear(in_features=32, out_features=128, bias=False)
(up_proj): Linear(in_features=32, out_features=128, bias=False)
(down_proj): Linear(in_features=128, out_features=32, bias=False)
(act_fn): SiLU()
)
(input_layernorm): Glm4vMoeTextRMSNorm((32,), eps=1e-05)
(post_attention_layernorm): Glm4vMoeTextRMSNorm((32,), eps=1e-05)
)
(1): Glm4vMoeTextDecoderLayer(
(self_attn): Glm4vMoeTextAttention(
(q_proj): Linear(in_features=32, out_features=64, bias=True)
(k_proj): Linear(in_features=32, out_features=32, bias=True)
(v_proj): Linear(in_features=32, out_features=32, bias=True)
(o_proj): Linear(in_features=64, out_features=32, bias=False)
)
(mlp): Glm4vMoeTextMoE(
(experts): ModuleList(
(0-127): 128 x Glm4vMoeTextMLP(
(gate_proj): Linear(in_features=32, out_features=64, bias=False)
(up_proj): Linear(in_features=32, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=32, bias=False)
(act_fn): SiLU()
)
)
(gate): Glm4vMoeTextTopkRouter()
(shared_experts): Glm4vMoeTextMLP(
(gate_proj): Linear(in_features=32, out_features=64, bias=False)
(up_proj): Linear(in_features=32, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=32, bias=False)
(act_fn): SiLU()
)
)
(input_layernorm): Glm4vMoeTextRMSNorm((32,), eps=1e-05)
(post_attention_layernorm): Glm4vMoeTextRMSNorm((32,), eps=1e-05)
)
)
(norm): Glm4vMoeRMSNorm((32,), eps=1e-05)
(rotary_emb): Glm4vMoeTextRotaryEmbedding()
)
)
(lm_head): Linear(in_features=32, out_features=151552, bias=False)
)
``` |