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from transformers import pipeline
from huggingface_hub import create_repo, upload_folder, snapshot_download
import torch
import transformers
from transformers import AutoModelForCausalLM
import os
from pathlib import Path
model_id = 'Qwen/Qwen-VL-Chat'
new_repo_id = 'yujiepan/qwen-vl-tiny-random'
def replace_in_file(file_path, old: str, new: str):
with open(file_path, 'r', encoding='utf-8') as f:
visual_code = f.read()
visual_code = visual_code.replace(old, new)
with open(file_path, 'w', encoding='utf-8') as f:
f.write(visual_code)
def download_modeling_codes():
snapshot_download(repo_id=model_id, allow_patterns='*.py',
local_dir='./qwen_vl_tiny_random', local_dir_use_symlinks=False)
# The hard coded "128" is changed for smaller model size.
replace_in_file('./qwen_vl_tiny_random/visual.py',
'num_heads=output_dim // 128,', 'num_heads=output_dim // 4,')
def create_config():
from qwen_vl_tiny_random.configuration_qwen import QWenConfig
config = QWenConfig()
config.fp16 = True
config.hidden_size = 8
config.intermediate_size = 16
config.kv_channels = 4
config.num_attention_heads = 2
config.num_hidden_layers = 2
config.seq_length = 2048
config.visual = {
"heads": 2,
"image_size": 448,
"image_start_id": 151857,
"layers": 2,
"mlp_ratio": 1.0,
"output_dim": 8,
"patch_size": 14,
"width": 8,
}
print(config)
return config
def create_model(config):
from qwen_vl_tiny_random.modeling_qwen import QWenLMHeadModel, QWenModel
from qwen_vl_tiny_random.configuration_qwen import QWenConfig
from transformers import AutoModelForCausalLM, AutoConfig, AutoModel
AutoConfig.register("qwen", QWenConfig)
AutoModel.register(QWenConfig, QWenModel)
AutoModelForCausalLM.register(QWenConfig, QWenLMHeadModel)
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
model.generation_config = transformers.GenerationConfig.from_pretrained(
model_id, trust_remote_code=True)
return model
def try_inference(model, tokenizer):
model = model.cuda()
query = tokenizer.from_list_format([
{'image': 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg'},
{'text': '这是什么'},
])
response, history = model.chat(tokenizer, query=query, history=None)
print(response)
download_modeling_codes()
config = create_config()
model = create_model(config)
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_id, trust_remote_code=True)
try_inference(model, tokenizer)
model.save_pretrained('./qwen_vl_tiny_random/')
tokenizer.save_pretrained('./qwen_vl_tiny_random/')
create_repo(new_repo_id, exist_ok=True)
upload_folder(repo_id=new_repo_id, folder_path='./qwen_vl_tiny_random/',
ignore_patterns='__pycache__')
model = transformers.AutoModelForCausalLM.from_pretrained(
new_repo_id, trust_remote_code=True).cuda()
tokenizer = transformers.AutoTokenizer.from_pretrained(
new_repo_id, trust_remote_code=True)
try_inference(model, tokenizer)
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