Create upload_model.py

upload_model.py

@@ -0,0 +1,95 @@
+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)