1. add modelers hub support

Former-commit-id: 24ebe187e360753666b768685a0dcc78054bb702

README.md

@@ -75,6 +75,8 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 
 ## Changelog
 
+[24/10/09] We supported downloading pre-trained models and datasets from the **[Modelers Hub](https://modelers.cn/models)** for Chinese mainland users. See [this tutorial](#download-from-modelers-hub) for usage.
+
 [24/09/19] We support fine-tuning the **[Qwen2.5](https://qwenlm.github.io/blog/qwen2.5/)** models.
 
 [24/08/30] We support fine-tuning the **[Qwen2-VL](https://qwenlm.github.io/blog/qwen2-vl/)** models. Thank [@simonJJJ](https://github.com/simonJJJ)'s PR.
@@ -363,7 +365,7 @@ cd LLaMA-Factory
 pip install -e ".[torch,metrics]"
 ```
 
-Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, badam, adam-mini, qwen, modelscope, quality
+Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, badam, adam-mini, qwen, modelscope, quality, openmind
 
 > [!TIP]
 > Use `pip install --no-deps -e .` to resolve package conflicts.
@@ -415,7 +417,7 @@ Download the pre-built Docker images: [32GB](http://mirrors.cn-central-221.ovaij
 
 ### Data Preparation
 
-Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope hub or load the dataset in local disk.
+Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can use datasets on HuggingFace hub, ModelScope hub, modelers hub or load the dataset in local disk.
 
 > [!NOTE]
 > Please update `data/dataset_info.json` to use your custom dataset.
@@ -483,6 +485,7 @@ docker build -f ./docker/docker-cuda/Dockerfile \
 docker run -dit --gpus=all \
     -v ./hf_cache:/root/.cache/huggingface \
     -v ./ms_cache:/root/.cache/modelscope \
+    -v ./om_cache:/root/.cache/openmind \
     -v ./data:/app/data \
     -v ./output:/app/output \
     -p 7860:7860 \
@@ -507,6 +510,7 @@ docker build -f ./docker/docker-npu/Dockerfile \
 docker run -dit \
     -v ./hf_cache:/root/.cache/huggingface \
     -v ./ms_cache:/root/.cache/modelscope \
+    -v ./om_cache:/root/.cache/openmind \
     -v ./data:/app/data \
     -v ./output:/app/output \
     -v /usr/local/dcmi:/usr/local/dcmi \
@@ -540,6 +544,7 @@ docker build -f ./docker/docker-rocm/Dockerfile \
 docker run -dit \
     -v ./hf_cache:/root/.cache/huggingface \
     -v ./ms_cache:/root/.cache/modelscope \
+    -v ./om_cache:/root/.cache/openmind \
     -v ./data:/app/data \
     -v ./output:/app/output \
     -v ./saves:/app/saves \
@@ -560,6 +565,7 @@ docker exec -it llamafactory bash
 
 - `hf_cache`: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory.
 - `ms_cache`: Similar to Hugging Face cache but for ModelScope users.
+- `om_cache`: Similar to Hugging Face cache but for Modelers users.
 - `data`: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
 - `output`: Set export dir to this location so that the merged result can be accessed directly on the host machine.
 
@@ -584,6 +590,16 @@ export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows
 
 Train the model by specifying a model ID of the ModelScope Hub as the `model_name_or_path`. You can find a full list of model IDs at [ModelScope Hub](https://modelscope.cn/models), e.g., `LLM-Research/Meta-Llama-3-8B-Instruct`.
 
+### Download from Modelers Hub
+
+You can also use Modelers Hub to download models and datasets.
+
+```bash
+export USE_OPENMIND_HUB=1 # `set USE_OPENMIND_HUB=1` for Windows
+```
+
+Train the model by specifying a model ID of the Modelers Hub as the `model_name_or_path`. You can find a full list of model IDs at [Modelers Hub](https://modelers.cn/models), e.g., `TeleAI/TeleChat-7B-pt`.
+
 ### Use W&B Logger
 
 To use [Weights & Biases](https://wandb.ai) for logging experimental results, you need to add the following arguments to yaml files.

README_zh.md

@@ -75,6 +75,7 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
 </details>
 
 ## 更新日志
+[24/10/09] 我们支持了从 **[魔乐社区](https://modelers.cn/models)** 下载预训练模型和数据集。详细用法请参照 [此教程](#从魔乐社区下载)。
 
 [24/09/19] 我们支持了 **[Qwen2.5](https://qwenlm.github.io/blog/qwen2.5/)** 模型的微调。
 
@@ -364,7 +365,7 @@ cd LLaMA-Factory
 pip install -e ".[torch,metrics]"
 ```
 
-可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、galore、badam、adam-mini、qwen、modelscope、quality
+可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、galore、badam、adam-mini、qwen、modelscope、quality、openmind
 
 > [!TIP]
 > 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。
@@ -416,7 +417,7 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
 
 ### 数据准备
 
-关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。你可以使用 HuggingFace / ModelScope 上的数据集或加载本地数据集。
+关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。你可以使用 HuggingFace , ModelScope 或者 Modelers 上的数据集或加载本地数据集。
 
 > [!NOTE]
 > 使用自定义数据集时，请更新 `data/dataset_info.json` 文件。
@@ -484,6 +485,7 @@ docker build -f ./docker/docker-cuda/Dockerfile \
 docker run -dit --gpus=all \
     -v ./hf_cache:/root/.cache/huggingface \
     -v ./ms_cache:/root/.cache/modelscope \
+    -v ./om_cache:/root/.cache/openmind \
     -v ./data:/app/data \
     -v ./output:/app/output \
     -p 7860:7860 \
@@ -508,6 +510,7 @@ docker build -f ./docker/docker-npu/Dockerfile \
 docker run -dit \
     -v ./hf_cache:/root/.cache/huggingface \
     -v ./ms_cache:/root/.cache/modelscope \
+    -v ./om_cache:/root/.cache/openmind \
     -v ./data:/app/data \
     -v ./output:/app/output \
     -v /usr/local/dcmi:/usr/local/dcmi \
@@ -541,6 +544,7 @@ docker build -f ./docker/docker-rocm/Dockerfile \
 docker run -dit \
     -v ./hf_cache:/root/.cache/huggingface \
     -v ./ms_cache:/root/.cache/modelscope \
+    -v ./om_cache:/root/.cache/openmind \
     -v ./data:/app/data \
     -v ./output:/app/output \
     -v ./saves:/app/saves \
@@ -561,6 +565,7 @@ docker exec -it llamafactory bash
 
 - `hf_cache`：使用宿主机的 Hugging Face 缓存文件夹，允许更改为新的目录。
 - `ms_cache`：类似 Hugging Face 缓存文件夹，为 ModelScope 用户提供。
+- `om_cache`：类似 Hugging Face 缓存文件夹，为 Modelers 用户提供。
 - `data`：宿主机中存放数据集的文件夹路径。
 - `output`：将导出目录设置为该路径后，即可在宿主机中访问导出后的模型。
 
@@ -585,6 +590,17 @@ export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
 
 将 `model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔搭社区](https://modelscope.cn/models)查看所有可用的模型，例如 `LLM-Research/Meta-Llama-3-8B-Instruct`。
 
+### 从魔乐社区下载
+
+您也可以通过下述方法使用魔乐社区，在魔乐社区上下载数据集和模型。
+
+```bash
+export USE_OPENMIND_HUB=1 # Windows 使用 `set USE_OPENMIND_HUB=1`
+```
+
+将 `model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔乐社区](https://modelers.cn/models)查看所有可用的模型，例如 `TeleAI/TeleChat-7B-pt`。
+
+
 ### 使用 W&B 面板
 
 若要使用 [Weights & Biases](https://wandb.ai) 记录实验数据，请在 yaml 文件中添加下面的参数。

src/llamafactory/__init__.py

@@ -38,6 +38,7 @@ Force check imports: FORCE_CHECK_IMPORTS=1
 Force using torchrun: FORCE_TORCHRUN=1
 Set logging verbosity: LLAMAFACTORY_VERBOSITY=WARN
 Use modelscope: USE_MODELSCOPE_HUB=1
+Use openmind: USE_OPENMIND_HUB=1
 """
 
 from .extras.env import VERSION

src/llamafactory/data/loader.py

@@ -53,7 +53,7 @@ def _load_single_dataset(
     """
     logger.info("Loading dataset {}...".format(dataset_attr))
     data_path, data_name, data_dir, data_files = None, None, None, None
-    if dataset_attr.load_from in ["hf_hub", "ms_hub"]:
+    if dataset_attr.load_from in ["om_hub", "hf_hub", "ms_hub"]:
         data_path = dataset_attr.dataset_name
         data_name = dataset_attr.subset
         data_dir = dataset_attr.folder
@@ -84,7 +84,24 @@ def _load_single_dataset(
     else:
         raise NotImplementedError("Unknown load type: {}.".format(dataset_attr.load_from))
 
-    if dataset_attr.load_from == "ms_hub":
+    if dataset_attr.load_from == "om_hub":
+        try:
+            from openmind import OmDataset
+            from openmind.utils.hub import OM_DATASETS_CACHE
+            cache_dir = model_args.cache_dir or OM_DATASETS_CACHE
+            dataset = OmDataset.load_dataset(
+                path=data_path,
+                name=data_name,
+                data_dir=data_dir,
+                data_files=data_files,
+                split=data_args.split,
+                cache_dir=cache_dir,
+                token=model_args.ms_hub_token,
+                streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
+            )
+        except ImportError:
+            raise ImportError("Please install openmind via `pip install openmind -U`")
+    elif dataset_attr.load_from == "ms_hub":
         require_version("modelscope>=1.11.0", "To fix: pip install modelscope>=1.11.0")
         from modelscope import MsDataset
         from modelscope.utils.config_ds import MS_DATASETS_CACHE

src/llamafactory/data/parser.py

@@ -20,7 +20,7 @@ from typing import Any, Dict, List, Literal, Optional, Sequence
 from transformers.utils import cached_file
 
 from ..extras.constants import DATA_CONFIG
-from ..extras.misc import use_modelscope
+from ..extras.misc import use_openmind, use_modelscope
 
 
 @dataclass
@@ -98,7 +98,12 @@ def get_dataset_list(dataset_names: Optional[Sequence[str]], dataset_dir: str) -
     dataset_list: List["DatasetAttr"] = []
     for name in dataset_names:
         if dataset_info is None: # dataset_dir is ONLINE
-            load_from = "ms_hub" if use_modelscope() else "hf_hub"
+            if use_openmind():
+                load_from = "om_hub"
+            if use_modelscope():
+                load_from = "ms_hub"
+            if load_from is None:
+                load_from = "hf_hub"
             dataset_attr = DatasetAttr(load_from, dataset_name=name)
             dataset_list.append(dataset_attr)
             continue
@@ -106,11 +111,14 @@ def get_dataset_list(dataset_names: Optional[Sequence[str]], dataset_dir: str) -
         if name not in dataset_info:
             raise ValueError("Undefined dataset {} in {}.".format(name, DATA_CONFIG))
 
+        has_om_url = "om_hub_url" in dataset_info[name]
         has_hf_url = "hf_hub_url" in dataset_info[name]
         has_ms_url = "ms_hub_url" in dataset_info[name]
 
-        if has_hf_url or has_ms_url:
+        if has_om_url or has_hf_url or has_ms_url:
-            if (use_modelscope() and has_ms_url) or (not has_hf_url):
+            if (use_openmind() and has_om_url) or (not has_hf_url):
+                dataset_attr = DatasetAttr("om_hub", dataset_name=dataset_info[name]["om_hub_url"])
+            elif (use_modelscope() and has_ms_url) or (not has_hf_url):
                 dataset_attr = DatasetAttr("ms_hub", dataset_name=dataset_info[name]["ms_hub_url"])
             else:
                 dataset_attr = DatasetAttr("hf_hub", dataset_name=dataset_info[name]["hf_hub_url"])

src/llamafactory/extras/misc.py

@@ -231,10 +231,21 @@ def torch_gc() -> None:
         torch.cuda.empty_cache()
 
 
-def try_download_model_from_ms(model_args: "ModelArguments") -> str:
+def try_download_model_from_other_hub(model_args: "ModelArguments") -> str:
-    if not use_modelscope() or os.path.exists(model_args.model_name_or_path):
+    if (not use_openmind() and not use_modelscope()) or os.path.exists(model_args.model_name_or_path):
         return model_args.model_name_or_path
 
+    if use_openmind():
+        try:
+            import openmind
+            from openmind.utils.hub import snapshot_download
+
+            revision = "main" if model_args.model_revision == "main" else model_args.model_revision
+            return snapshot_download(model_args.model_name_or_path, revision=revision, cache_dir=model_args.cache_dir)
+        except ImportError:
+            raise ImportError("Please install openmind and openmind_hub via `pip install openmind -U`")
+
+    if use_modelscope():
         try:
             from modelscope import snapshot_download
 
@@ -243,6 +254,8 @@ def try_download_model_from_ms(model_args: "ModelArguments") -> str:
         except ImportError:
             raise ImportError("Please install modelscope via `pip install modelscope -U`")
 
+def use_openmind() -> bool:
+    return os.environ.get("USE_OPENMIND_HUB", "0").lower() in ["true", "1"]
 
 def use_modelscope() -> bool:
     return os.environ.get("USE_MODELSCOPE_HUB", "0").lower() in ["true", "1"]

src/llamafactory/model/loader.py

@@ -19,7 +19,7 @@ from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForVision2Se
 from trl import AutoModelForCausalLMWithValueHead
 
 from ..extras.logging import get_logger
-from ..extras.misc import count_parameters, skip_check_imports, try_download_model_from_ms
+from ..extras.misc import count_parameters, skip_check_imports, try_download_model_from_other_hub
 from .adapter import init_adapter
 from .model_utils.liger_kernel import apply_liger_kernel
 from .model_utils.misc import register_autoclass
@@ -50,7 +50,7 @@ def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
     Note: including inplace operation of model_args.
     """
     skip_check_imports()
-    model_args.model_name_or_path = try_download_model_from_ms(model_args)
+    model_args.model_name_or_path = try_download_model_from_other_hub(model_args)
     return {
         "trust_remote_code": True,
         "cache_dir": model_args.cache_dir,