Merge branch 'hiyouga:main' into pixtral-patch

Former-commit-id: 95330893c5cd290430a0a2a4e4afa87afab2eb88
2025-08-23 06:12:50 +08:00 · 2024-10-13 17:42:02 +08:00 · 2024-10-13 17:42:02 +08:00 · 8ea1c5c69e
commit 8ea1c5c69e
parent 5523a6fd2c 92de726102
17 changed files with 141 additions and 26 deletions
--- a/README.md
+++ b/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)**. 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.
@ -133,7 +135,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/

 [23/12/12] We supported fine-tuning the latest MoE model **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)** in our framework. See hardware requirement [here](#hardware-requirement).

-[23/12/01] We supported downloading pre-trained models and datasets from the **[ModelScope Hub](https://modelscope.cn/models)** for Chinese mainland users. See [this tutorial](#download-from-modelscope-hub) for usage.
+[23/12/01] We supported downloading pre-trained models and datasets from the **[ModelScope Hub](https://modelscope.cn/models)**. See [this tutorial](#download-from-modelscope-hub) for usage.

 [23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `neftune_noise_alpha: 5` argument to activate NEFTune.

@ -364,7 +366,7 @@ 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, openmind, quality

 > [!TIP]
 > Use `pip install --no-deps -e .` to resolve package conflicts.
@ -416,7 +418,7 @@ 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 either use datasets on HuggingFace / ModelScope / Modelers hub or load the dataset in local disk.

 > [!NOTE]
 > Please update `data/dataset_info.json` to use your custom dataset.
@ -484,6 +486,7 @@ 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 +511,7 @@ 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 +545,7 @@ 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 +566,7 @@ 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.

@ -585,6 +591,16 @@ 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.
--- a/README_zh.md
+++ b/README_zh.md
@ -76,6 +76,8 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272

 ## 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。

 [24/08/30] 我们支持了 **[Qwen2-VL](https://qwenlm.github.io/blog/qwen2-vl/)** 模型的微调。感谢 [@simonJJJ](https://github.com/simonJJJ) 的 PR。
@ -365,7 +367,7 @@ 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、openmind、quality

 > [!TIP]
 > 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。
@ -417,7 +419,7 @@ 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 / 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。
@ -485,6 +487,7 @@ 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 \
@ -509,6 +512,7 @@ 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 \
@ -542,6 +546,7 @@ 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 \
@ -562,6 +567,7 @@ 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。

@ -586,6 +592,16 @@ 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。
--- a/data/dataset_info.json
+++ b/data/dataset_info.json
@ -54,7 +54,8 @@
  },
  "alpaca_en": {
    "hf_hub_url": "llamafactory/alpaca_en",
-    "ms_hub_url": "llamafactory/alpaca_en"
+    "ms_hub_url": "llamafactory/alpaca_en",
+    "om_hub_url": "HaM/alpaca_en"
  },
  "alpaca_zh": {
    "hf_hub_url": "llamafactory/alpaca_zh",
@ -66,7 +67,8 @@
  },
  "alpaca_gpt4_zh": {
    "hf_hub_url": "llamafactory/alpaca_gpt4_zh",
-    "ms_hub_url": "llamafactory/alpaca_gpt4_zh"
+    "ms_hub_url": "llamafactory/alpaca_gpt4_zh",
+    "om_hub_url": "State_Cloud/alpaca-gpt4-data-zh"
  },
  "glaive_toolcall_en": {
    "hf_hub_url": "llamafactory/glaive_toolcall_en",
--- a/docker/docker-cuda/docker-compose.yml
+++ b/docker/docker-cuda/docker-compose.yml
@ -16,6 +16,7 @@ services:
    volumes:
      - ../../hf_cache:/root/.cache/huggingface
      - ../../ms_cache:/root/.cache/modelscope
+      - ../../om_cache:/root/.cache/openmind
      - ../../data:/app/data
      - ../../output:/app/output
    ports:
--- a/docker/docker-npu/docker-compose.yml
+++ b/docker/docker-npu/docker-compose.yml
@ -10,6 +10,7 @@ services:
    volumes:
      - ../../hf_cache:/root/.cache/huggingface
      - ../../ms_cache:/root/.cache/modelscope
+      - ../../om_cache:/root/.cache/openmind
      - ../../data:/app/data
      - ../../output:/app/output
      - /usr/local/dcmi:/usr/local/dcmi
--- a/docker/docker-rocm/docker-compose.yml
+++ b/docker/docker-rocm/docker-compose.yml
@ -15,6 +15,7 @@ services:
    volumes:
      - ../../hf_cache:/root/.cache/huggingface
      - ../../ms_cache:/root/.cache/modelscope
+      - ../../om_cache:/root/.cache/openmind
      - ../../data:/app/data
      - ../../output:/app/output
      - ../../saves:/app/saves
--- a/requirements.txt
+++ b/requirements.txt
@ -3,7 +3,7 @@ datasets>=2.16.0,<=2.21.0
 accelerate>=0.30.1,<=0.34.2
 peft>=0.11.1,<=0.12.0
 trl>=0.8.6,<=0.9.6
-gradio>=4.0.0
+gradio>=4.0.0,<5.0.0
 pandas>=2.0.0
 scipy
 einops
--- a/setup.py
+++ b/setup.py
@ -54,12 +54,13 @@ extra_require = {
    "gptq": ["optimum>=1.17.0", "auto-gptq>=0.5.0"],
    "awq": ["autoawq"],
    "aqlm": ["aqlm[gpu]>=1.1.0"],
-    "vllm": ["vllm>=0.4.3,<=0.6.2"],
+    "vllm": ["vllm>=0.4.3,<=0.6.3"],
    "galore": ["galore-torch"],
    "badam": ["badam>=1.2.1"],
    "adam-mini": ["adam-mini"],
    "qwen": ["transformers_stream_generator"],
    "modelscope": ["modelscope"],
+    "openmind": ["openmind"],
    "dev": ["ruff", "pytest"],
 }

--- a/src/llamafactory/init.py
+++ b/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
--- a/src/llamafactory/data/loader.py
+++ b/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 ["hf_hub", "ms_hub", "om_hub"]:
        data_path = dataset_attr.dataset_name
        data_name = dataset_attr.subset
        data_dir = dataset_attr.folder
@ -102,6 +102,23 @@ def _load_single_dataset(
        )
        if isinstance(dataset, MsDataset):
            dataset = dataset.to_hf_dataset()
+
+    elif dataset_attr.load_from == "om_hub":
+        require_version("openmind>=0.8.0", "To fix: pip install openmind>=0.8.0")
+        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=dataset_attr.split,
+            cache_dir=cache_dir,
+            token=model_args.om_hub_token,
+            streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
+        )
    else:
        dataset = load_dataset(
            path=data_path,
--- a/src/llamafactory/data/parser.py
+++ b/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_modelscope, use_openmind


@dataclass
@ -30,7 +30,7 @@ class DatasetAttr:
    """

    # basic configs
-    load_from: Literal["hf_hub", "ms_hub", "script", "file"]
+    load_from: Literal["hf_hub", "ms_hub", "om_hub", "script", "file"]
    dataset_name: str
    formatting: Literal["alpaca", "sharegpt"] = "alpaca"
    ranking: bool = False
@ -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_modelscope():
+                load_from = "ms_hub"
+            elif use_openmind():
+                load_from = "om_hub"
+            else:
+                load_from = "hf_hub"
            dataset_attr = DatasetAttr(load_from, dataset_name=name)
            dataset_list.append(dataset_attr)
            continue
@ -108,10 +113,13 @@ def get_dataset_list(dataset_names: Optional[Sequence[str]], dataset_dir: str) -

        has_hf_url = "hf_hub_url" in dataset_info[name]
        has_ms_url = "ms_hub_url" in dataset_info[name]
+        has_om_url = "om_hub_url" in dataset_info[name]

-        if has_hf_url or has_ms_url:
-            if (use_modelscope() and has_ms_url) or (not has_hf_url):
+        if has_hf_url or has_ms_url or has_om_url:
+            if has_ms_url and (use_modelscope() or not has_hf_url):
                dataset_attr = DatasetAttr("ms_hub", dataset_name=dataset_info[name]["ms_hub_url"])
+            elif has_om_url and (use_openmind() or not has_hf_url):
+                dataset_attr = DatasetAttr("om_hub", dataset_name=dataset_info[name]["om_hub_url"])
            else:
                dataset_attr = DatasetAttr("hf_hub", dataset_name=dataset_info[name]["hf_hub_url"])
        elif "script_url" in dataset_info[name]:
--- a/src/llamafactory/extras/constants.py
+++ b/src/llamafactory/extras/constants.py
@ -107,6 +107,7 @@ VISION_MODELS = set()
 class DownloadSource(str, Enum):
    DEFAULT = "hf"
    MODELSCOPE = "ms"
+    OPENMIND = "om"


 def register_model_group(
@ -163,14 +164,17 @@ register_model_group(
        "Baichuan2-13B-Base": {
            DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-13B-Base",
            DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-13B-Base",
+            DownloadSource.OPENMIND: "Baichuan/Baichuan2_13b_base_pt",
        },
        "Baichuan2-7B-Chat": {
            DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-7B-Chat",
            DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-7B-Chat",
+            DownloadSource.OPENMIND: "Baichuan/Baichuan2_7b_chat_pt",
        },
        "Baichuan2-13B-Chat": {
            DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-13B-Chat",
            DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-13B-Chat",
+            DownloadSource.OPENMIND: "Baichuan/Baichuan2_13b_chat_pt",
        },
    },
    template="baichuan2",
@ -555,10 +559,12 @@ register_model_group(
        "Gemma-2-2B-Instruct": {
            DownloadSource.DEFAULT: "google/gemma-2-2b-it",
            DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-2b-it",
+            DownloadSource.OPENMIND: "LlamaFactory/gemma-2-2b-it",
        },
        "Gemma-2-9B-Instruct": {
            DownloadSource.DEFAULT: "google/gemma-2-9b-it",
            DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-9b-it",
+            DownloadSource.OPENMIND: "LlamaFactory/gemma-2-9b-it",
        },
        "Gemma-2-27B-Instruct": {
            DownloadSource.DEFAULT: "google/gemma-2-27b-it",
@ -578,6 +584,7 @@ register_model_group(
        "GLM-4-9B-Chat": {
            DownloadSource.DEFAULT: "THUDM/glm-4-9b-chat",
            DownloadSource.MODELSCOPE: "ZhipuAI/glm-4-9b-chat",
+            DownloadSource.OPENMIND: "LlamaFactory/glm-4-9b-chat",
        },
        "GLM-4-9B-1M-Chat": {
            DownloadSource.DEFAULT: "THUDM/glm-4-9b-chat-1m",
@ -632,6 +639,7 @@ register_model_group(
        "InternLM2.5-1.8B": {
            DownloadSource.DEFAULT: "internlm/internlm2_5-1_8b",
            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-1_8b",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-1_8b",
        },
        "InternLM2.5-7B": {
            DownloadSource.DEFAULT: "internlm/internlm2_5-7b",
@ -640,22 +648,27 @@ register_model_group(
        "InternLM2.5-20B": {
            DownloadSource.DEFAULT: "internlm/internlm2_5-20b",
            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-20b",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-20b",
        },
        "InternLM2.5-1.8B-Chat": {
            DownloadSource.DEFAULT: "internlm/internlm2_5-1_8b-chat",
            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-1_8b-chat",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-1_8b-chat",
        },
        "InternLM2.5-7B-Chat": {
            DownloadSource.DEFAULT: "internlm/internlm2_5-7b-chat",
            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-7b-chat",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-7b-chat",
        },
        "InternLM2.5-7B-1M-Chat": {
            DownloadSource.DEFAULT: "internlm/internlm2_5-7b-chat-1m",
            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-7b-chat-1m",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-7b-chat-1m",
        },
        "InternLM2.5-20B-Chat": {
            DownloadSource.DEFAULT: "internlm/internlm2_5-20b-chat",
            DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-20b-chat",
+            DownloadSource.OPENMIND: "Intern/internlm2_5-20b-chat",
        },
    },
    template="intern2",
@ -756,6 +769,7 @@ register_model_group(
        "Llama-3-8B-Chinese-Chat": {
            DownloadSource.DEFAULT: "shenzhi-wang/Llama3-8B-Chinese-Chat",
            DownloadSource.MODELSCOPE: "LLM-Research/Llama3-8B-Chinese-Chat",
+            DownloadSource.OPENMIND: "LlamaFactory/Llama3-Chinese-8B-Instruct",
        },
        "Llama-3-70B-Chinese-Chat": {
            DownloadSource.DEFAULT: "shenzhi-wang/Llama3-70B-Chinese-Chat",
@ -960,6 +974,7 @@ register_model_group(
        "MiniCPM3-4B-Chat": {
            DownloadSource.DEFAULT: "openbmb/MiniCPM3-4B",
            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM3-4B",
+            DownloadSource.OPENMIND: "LlamaFactory/MiniCPM3-4B",
        },
    },
    template="cpm3",
@ -1421,14 +1436,17 @@ register_model_group(
        "Qwen2-0.5B-Instruct": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-0.5B-Instruct",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-0.5B-Instruct",
+            DownloadSource.OPENMIND: "LlamaFactory/Qwen2-0.5B-Instruct",
        },
        "Qwen2-1.5B-Instruct": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-1.5B-Instruct",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-1.5B-Instruct",
+            DownloadSource.OPENMIND: "LlamaFactory/Qwen2-1.5B-Instruct",
        },
        "Qwen2-7B-Instruct": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-7B-Instruct",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-7B-Instruct",
+            DownloadSource.OPENMIND: "LlamaFactory/Qwen2-7B-Instruct",
        },
        "Qwen2-72B-Instruct": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-72B-Instruct",
@ -1711,10 +1729,12 @@ register_model_group(
        "Qwen2-VL-2B-Instruct": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-2B-Instruct",
+            DownloadSource.OPENMIND: "LlamaFactory/Qwen2-VL-2B-Instruct",
        },
        "Qwen2-VL-7B-Instruct": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct",
            DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-7B-Instruct",
+            DownloadSource.OPENMIND: "LlamaFactory/Qwen2-VL-7B-Instruct",
        },
        "Qwen2-VL-72B-Instruct": {
            DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct",
@ -1813,10 +1833,12 @@ register_model_group(
        "TeleChat-7B-Chat": {
            DownloadSource.DEFAULT: "Tele-AI/telechat-7B",
            DownloadSource.MODELSCOPE: "TeleAI/telechat-7B",
+            DownloadSource.OPENMIND: "TeleAI/TeleChat-7B-pt",
        },
        "TeleChat-12B-Chat": {
            DownloadSource.DEFAULT: "Tele-AI/TeleChat-12B",
            DownloadSource.MODELSCOPE: "TeleAI/TeleChat-12B",
+            DownloadSource.OPENMIND: "TeleAI/TeleChat-12B-pt",
        },
        "TeleChat-12B-v2-Chat": {
            DownloadSource.DEFAULT: "Tele-AI/TeleChat-12B-v2",
@ -2035,6 +2057,7 @@ register_model_group(
        "Yi-1.5-6B-Chat": {
            DownloadSource.DEFAULT: "01-ai/Yi-1.5-6B-Chat",
            DownloadSource.MODELSCOPE: "01ai/Yi-1.5-6B-Chat",
+            DownloadSource.OPENMIND: "LlamaFactory/Yi-1.5-6B-Chat",
        },
        "Yi-1.5-9B-Chat": {
            DownloadSource.DEFAULT: "01-ai/Yi-1.5-9B-Chat",
--- a/src/llamafactory/extras/misc.py
+++ b/src/llamafactory/extras/misc.py
@ -231,18 +231,35 @@ def torch_gc() -> None:
        torch.cuda.empty_cache()


-def try_download_model_from_ms(model_args: "ModelArguments") -> str:
-    if not use_modelscope() or os.path.exists(model_args.model_name_or_path):
+def try_download_model_from_other_hub(model_args: "ModelArguments") -> str:
+    if (not use_modelscope() and not use_openmind()) or os.path.exists(model_args.model_name_or_path):
        return model_args.model_name_or_path

-    try:
+    if use_modelscope():
+        require_version("modelscope>=1.11.0", "To fix: pip install modelscope>=1.11.0")
        from modelscope import snapshot_download

        revision = "master" 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 modelscope via `pip install modelscope -U`")
+        return snapshot_download(
+            model_args.model_name_or_path,
+            revision=revision,
+            cache_dir=model_args.cache_dir,
+        )
+
+    if use_openmind():
+        require_version("openmind>=0.8.0", "To fix: pip install openmind>=0.8.0")
+        from openmind.utils.hub import snapshot_download
+
+        return snapshot_download(
+            model_args.model_name_or_path,
+            revision=model_args.model_revision,
+            cache_dir=model_args.cache_dir,
+        )


 def use_modelscope() -> bool:
    return os.environ.get("USE_MODELSCOPE_HUB", "0").lower() in ["true", "1"]
+
+
+def use_openmind() -> bool:
+    return os.environ.get("USE_OPENMIND_HUB", "0").lower() in ["true", "1"]
--- a/src/llamafactory/hparams/model_args.py
+++ b/src/llamafactory/hparams/model_args.py
@ -267,6 +267,10 @@ class ModelArguments(QuantizationArguments, ProcessorArguments, ExportArguments,
        default=None,
        metadata={"help": "Auth token to log in with ModelScope Hub."},
    )
+    om_hub_token: Optional[str] = field(
+        default=None,
+        metadata={"help": "Auth token to log in with Modelers Hub."},
+    )
    print_param_status: bool = field(
        default=False,
        metadata={"help": "For debugging purposes, print the status of the parameters in the model."},
--- a/src/llamafactory/hparams/parser.py
+++ b/src/llamafactory/hparams/parser.py
@ -123,7 +123,7 @@ def _check_extra_dependencies(
        require_version("mixture-of-depth>=1.1.6", "To fix: pip install mixture-of-depth>=1.1.6")

    if model_args.infer_backend == "vllm":
-        require_version("vllm>=0.4.3,<=0.6.2", "To fix: pip install vllm>=0.4.3,<=0.6.2")
+        require_version("vllm>=0.4.3,<=0.6.3", "To fix: pip install vllm>=0.4.3,<=0.6.3")

    if finetuning_args.use_galore:
        require_version("galore_torch", "To fix: pip install galore_torch")
--- a/src/llamafactory/model/loader.py
+++ b/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,
--- a/src/llamafactory/webui/common.py
+++ b/src/llamafactory/webui/common.py
@ -31,7 +31,7 @@ from ..extras.constants import (
    DownloadSource,
 )
 from ..extras.logging import get_logger
-from ..extras.misc import use_modelscope
+from ..extras.misc import use_modelscope, use_openmind
 from ..extras.packages import is_gradio_available


@ -109,9 +109,16 @@ def get_model_path(model_name: str) -> str:
        use_modelscope()
        and path_dict.get(DownloadSource.MODELSCOPE)
        and model_path == path_dict.get(DownloadSource.DEFAULT)
-    ):  # replace path
+    ):  # replace hf path with ms path
        model_path = path_dict.get(DownloadSource.MODELSCOPE)

+    if (
+        use_openmind()
+        and path_dict.get(DownloadSource.OPENMIND)
+        and model_path == path_dict.get(DownloadSource.DEFAULT)
+    ):  # replace hf path with om path
+        model_path = path_dict.get(DownloadSource.OPENMIND)
+
    return model_path