This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from zai-org/GLM-OCR.
| File path | Size |
|---|---|
| model.safetensors | 3.8MB |
Example usage:
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor
model_id = "tiny-random/glm-ocr"
model = AutoModelForImageTextToText.from_pretrained(
model_id, dtype=torch.bfloat16, device_map="cuda",
)
processor = AutoProcessor.from_pretrained(model_id)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
},
{"type": "text", "text": "Describe this image."},
],
}
]
# Preparation for inference
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], skip_special_tokens=False)
print(output_text)
Codes to create this repo:
import json
from copy import deepcopy
from pathlib import Path
import accelerate
import torch
import torch.nn as nn
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoProcessor,
GenerationConfig,
GlmOcrForConditionalGeneration,
set_seed,
)
source_model_id = "zai-org/GLM-OCR"
save_folder = "/tmp/tiny-random/glm-ocr"
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: dict = json.load(f)
config_json['text_config'].update({
"head_dim": 32,
"hidden_size": 8,
"intermediate_size": 64,
"num_attention_heads": 8,
"num_hidden_layers": 2,
"num_key_value_heads": 4,
"rope_parameters": {
"rope_type": "default",
"mrope_section": [4, 4, 8],
"partial_rotary_factor": 1.0,
"rope_theta": 10000,
},
})
config_json['vision_config'].update({
"hidden_size": 32,
"depth": 2,
"num_heads": 1,
"intermediate_size": 64,
"out_hidden_size": 8,
})
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 = GlmOcrForConditionalGeneration(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,
)
model.generation_config.do_sample = True
print(model.generation_config)
model = model.cpu()
set_seed(42)
n_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.numel() / n_params * 100, '%')
# MTP
set_seed(42)
config = config.get_text_config()
model.model.language_model.layers.append(nn.ModuleDict(dict(
shared_head=nn.ModuleDict(dict(
norm=nn.RMSNorm(config.hidden_size),
head=deepcopy(model.model.language_model.embed_tokens),
)),
embed_tokens=deepcopy(model.model.language_model.embed_tokens),
eh_proj=nn.Linear(config.hidden_size * 2,
config.hidden_size, bias=False),
enorm=nn.RMSNorm(config.hidden_size),
hnorm=nn.RMSNorm(config.hidden_size),
input_layernorm=nn.RMSNorm(config.hidden_size),
post_mlp_layernorm=nn.RMSNorm(config.hidden_size),
post_attention_layernorm=nn.RMSNorm(config.hidden_size),
post_self_attn_layernorm=nn.RMSNorm(config.hidden_size),
self_attn=deepcopy(model.model.language_model.layers[1].self_attn),
mlp=deepcopy(model.model.language_model.layers[1].mlp),
)))
# for i in range(1, len(model.model.language_model.layers)):
# model.model.language_model.layers[i].mlp.gate.e_score_correction_bias = torch.rand_like(
# model.model.language_model.layers[i].mlp.gate.e_score_correction_bias).float()
model.save_pretrained(save_folder)
print(model)
Printing the model:
GlmOcrForConditionalGeneration(
(model): GlmOcrModel(
(visual): GlmOcrVisionModel(
(patch_embed): GlmOcrVisionPatchEmbed(
(proj): Conv3d(3, 32, kernel_size=(2, 14, 14), stride=(2, 14, 14))
)
(rotary_pos_emb): GlmOcrVisionRotaryEmbedding()
(blocks): ModuleList(
(0-1): 2 x GlmOcrVisionBlock(
(norm1): GlmOcrRMSNorm((32,), eps=1e-05)
(norm2): GlmOcrRMSNorm((32,), eps=1e-05)
(attn): GlmOcrVisionAttention(
(qkv): Linear(in_features=32, out_features=96, bias=True)
(proj): Linear(in_features=32, out_features=32, bias=True)
(q_norm): GlmOcrRMSNorm((32,), eps=1e-05)
(k_norm): GlmOcrRMSNorm((32,), eps=1e-05)
)
(mlp): GlmOcrVisionMlp(
(gate_proj): Linear(in_features=32, out_features=64, bias=True)
(up_proj): Linear(in_features=32, out_features=64, bias=True)
(down_proj): Linear(in_features=64, out_features=32, bias=True)
(act_fn): SiLUActivation()
)
)
)
(merger): GlmOcrVisionPatchMerger(
(proj): Linear(in_features=8, out_features=8, bias=False)
(post_projection_norm): LayerNorm((8,), eps=1e-05, elementwise_affine=True)
(gate_proj): Linear(in_features=8, out_features=24, bias=False)
(up_proj): Linear(in_features=8, out_features=24, bias=False)
(down_proj): Linear(in_features=24, out_features=8, bias=False)
(act1): GELU(approximate='none')
(act_fn): SiLUActivation()
)
(downsample): Conv2d(32, 8, kernel_size=(2, 2), stride=(2, 2))
(post_layernorm): GlmOcrRMSNorm((32,), eps=1e-05)
)
(language_model): GlmOcrTextModel(
(embed_tokens): Embedding(59392, 8, padding_idx=59246)
(layers): ModuleList(
(0-1): 2 x GlmOcrTextDecoderLayer(
(self_attn): GlmOcrTextAttention(
(q_proj): Linear(in_features=8, out_features=256, bias=False)
(k_proj): Linear(in_features=8, out_features=128, bias=False)
(v_proj): Linear(in_features=8, out_features=128, bias=False)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
)
(mlp): GlmOcrTextMLP(
(gate_up_proj): Linear(in_features=8, out_features=128, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(activation_fn): SiLUActivation()
)
(input_layernorm): GlmOcrRMSNorm((8,), eps=1e-05)
(post_attention_layernorm): GlmOcrRMSNorm((8,), eps=1e-05)
(post_self_attn_layernorm): GlmOcrRMSNorm((8,), eps=1e-05)
(post_mlp_layernorm): GlmOcrRMSNorm((8,), eps=1e-05)
)
(2): ModuleDict(
(shared_head): ModuleDict(
(norm): RMSNorm((8,), eps=None, elementwise_affine=True)
(head): Embedding(59392, 8, padding_idx=59246)
)
(embed_tokens): Embedding(59392, 8, padding_idx=59246)
(eh_proj): Linear(in_features=16, out_features=8, bias=False)
(enorm): RMSNorm((8,), eps=None, elementwise_affine=True)
(hnorm): RMSNorm((8,), eps=None, elementwise_affine=True)
(input_layernorm): RMSNorm((8,), eps=None, elementwise_affine=True)
(post_mlp_layernorm): RMSNorm((8,), eps=None, elementwise_affine=True)
(post_attention_layernorm): RMSNorm((8,), eps=None, elementwise_affine=True)
(post_self_attn_layernorm): RMSNorm((8,), eps=None, elementwise_affine=True)
(self_attn): GlmOcrTextAttention(
(q_proj): Linear(in_features=8, out_features=256, bias=False)
(k_proj): Linear(in_features=8, out_features=128, bias=False)
(v_proj): Linear(in_features=8, out_features=128, bias=False)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
)
(mlp): GlmOcrTextMLP(
(gate_up_proj): Linear(in_features=8, out_features=128, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(activation_fn): SiLUActivation()
)
)
)
(norm): GlmOcrRMSNorm((8,), eps=1e-05)
(rotary_emb): GlmOcrTextRotaryEmbedding()
)
)
(lm_head): Linear(in_features=8, out_features=59392, bias=False)
)