Upload folder using huggingface_hub (#1)

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.ipynb_checkpoints/upload-checkpoint.py

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+import os
+from modelscope.hub.api import HubApi
+from modelscope.hub.constants import ModelVisibility
+
+# ==================== 请先在这里配置您的信息 ====================
+YOUR_ACCESS_TOKEN = "ms-229ecb55-7004-421e-86c9-9f16d75ef549"  # 从 https://modelscope.cn/my/myaccesstoken 获取
+YOUR_USERNAME = "SLAIAITP"
+MODEL_NAME = "DeepSeek-V4-Flash-OR"        # 例如: my-bert-model
+LOCAL_MODEL_PATH = "./"  # 本地模型文件夹的绝对路径
+# ===========================================================
+
+# 1. 初始化并登录
+api = HubApi()
+api.login(access_token=YOUR_ACCESS_TOKEN)
+
+# 2. 组合出完整的模型ID
+repo_id = f"{YOUR_USERNAME}/{MODEL_NAME}"
+
+# 3. 检查模型仓库是否存在，如果不存在则创建一个新的公开仓库
+print(f"正在检查仓库: {repo_id}")
+try:
+    api.get_model(repo_id)
+    print("仓库已存在，将直接上传文件。")
+except Exception:
+    print(f"仓库不存在，正在创建新仓库: {repo_id}")
+    api.create_model(
+        model_id=repo_id,
+        visibility=ModelVisibility.PUBLIC,  # 或使用 ModelVisibility.PRIVATE
+        license='apache-2.0'               # 许可证类型可自行更换
+    )
+    print("仓库创建成功。")
+
+# 4. 上传整个文件夹到模型仓库
+print(f"正在从 {LOCAL_MODEL_PATH} 上传文件...")
+api.upload_folder(
+    repo_id=repo_id,
+    folder_path=LOCAL_MODEL_PATH,
+    commit_message="首次通过Python SDK上传模型",
+    # repo_type='model'  # 默认为 'model'，如果是数据集可改为 'dataset'
+    # 其他可选参数:
+    # allow_patterns=["*.json", "*.bin"],  # 仅上传特定类型文件
+    # ignore_patterns=["*.log", "*.tmp"],  # 忽略特定类型文件
+    # max_workers=8,                       # 并发上传线程数
+)
+print("✅ 模型上传完成！")

.mv

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+Revision:master,CreatedAt:1785469136

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tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "legacy": true,
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+    "normalized": true,
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+    "single_word": false
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+  "tokenizer_class": "PreTrainedTokenizerFast"
+}