Merge remote-tracking branch 'origin' into fix/trust_remote_code_

docker/Dockerfile.ubuntu

@@ -65,6 +65,8 @@ RUN sh /tmp/install.sh {version_args} && \
     pip config set install.trusted-host mirrors.aliyun.com && \
     cp /tmp/resources/ubuntu2204.aliyun /etc/apt/sources.list
 
+RUN pip install --no-cache-dir omegaconf==2.3.0 && pip cache purge
+
 ENV SETUPTOOLS_USE_DISTUTILS=stdlib
 ENV VLLM_USE_MODELSCOPE=True
 ENV LMDEPLOY_USE_MODELSCOPE=True

modelscope/__init__.py

@@ -56,7 +56,7 @@ if TYPE_CHECKING:
             AutoModelForPreTraining, AutoModelForTextEncoding,
             AutoImageProcessor, BatchFeature, Qwen2VLForConditionalGeneration,
             T5EncoderModel, Qwen2_5_VLForConditionalGeneration, LlamaModel,
-            LlamaPreTrainedModel, LlamaForCausalLM)
+            LlamaPreTrainedModel, LlamaForCausalLM, hf_pipeline)
     else:
         print(
             'transformer is not installed, please install it if you want to use related modules'

modelscope/cli/cli.py

@@ -40,9 +40,6 @@ def run_cmd():
     if not hasattr(args, 'func'):
         parser.print_help()
         exit(1)
-    if args.token is not None:
-        api = HubApi()
-        api.login(args.token)
     cmd = args.func(args)
     cmd.execute()
 

modelscope/cli/download.py

@@ -3,6 +3,7 @@ import os
 from argparse import ArgumentParser
 
 from modelscope.cli.base import CLICommand
+from modelscope.hub.api import HubApi
 from modelscope.hub.constants import DEFAULT_MAX_WORKERS
 from modelscope.hub.file_download import (dataset_file_download,
                                           model_file_download)
@@ -54,16 +55,21 @@ class DownloadCMD(CLICommand):
             default='model',
             help="Type of repo to download from (defaults to 'model').",
         )
+        parser.add_argument(
+            '--token',
+            type=str,
+            default=None,
+            help='Optional. Access token to download controlled entities.')
         parser.add_argument(
             '--revision',
             type=str,
             default=None,
-            help='Revision of the model.')
+            help='Revision of the entity (e.g., model).')
         parser.add_argument(
             '--cache_dir',
             type=str,
             default=None,
-            help='Cache directory to save model.')
+            help='Cache directory to save entity (e.g., model).')
         parser.add_argument(
             '--local_dir',
             type=str,
@@ -118,6 +124,10 @@ class DownloadCMD(CLICommand):
                                     % self.args.repo_type)
         if not self.args.model and not self.args.dataset:
             raise Exception('Model or dataset must be set.')
+        cookies = None
+        if self.args.token is not None:
+            api = HubApi()
+            cookies = api.get_cookies(access_token=self.args.token)
         if self.args.model:
             if len(self.args.files) == 1:  # download single file
                 model_file_download(
@@ -125,7 +135,8 @@ class DownloadCMD(CLICommand):
                     self.args.files[0],
                     cache_dir=self.args.cache_dir,
                     local_dir=self.args.local_dir,
-                    revision=self.args.revision)
+                    revision=self.args.revision,
+                    cookies=cookies)
             elif len(
                     self.args.files) > 1:  # download specified multiple files.
                 snapshot_download(
@@ -135,7 +146,7 @@ class DownloadCMD(CLICommand):
                     local_dir=self.args.local_dir,
                     allow_file_pattern=self.args.files,
                     max_workers=self.args.max_workers,
-                )
+                    cookies=cookies)
             else:  # download repo
                 snapshot_download(
                     self.args.model,
@@ -145,7 +156,7 @@ class DownloadCMD(CLICommand):
                     allow_file_pattern=convert_patterns(self.args.include),
                     ignore_file_pattern=convert_patterns(self.args.exclude),
                     max_workers=self.args.max_workers,
-                )
+                    cookies=cookies)
         elif self.args.dataset:
             dataset_revision: str = self.args.revision if self.args.revision else DEFAULT_DATASET_REVISION
             if len(self.args.files) == 1:  # download single file
@@ -154,7 +165,8 @@ class DownloadCMD(CLICommand):
                     self.args.files[0],
                     cache_dir=self.args.cache_dir,
                     local_dir=self.args.local_dir,
-                    revision=dataset_revision)
+                    revision=dataset_revision,
+                    cookies=cookies)
             elif len(
                     self.args.files) > 1:  # download specified multiple files.
                 dataset_snapshot_download(
@@ -164,7 +176,7 @@ class DownloadCMD(CLICommand):
                     local_dir=self.args.local_dir,
                     allow_file_pattern=self.args.files,
                     max_workers=self.args.max_workers,
-                )
+                    cookies=cookies)
             else:  # download repo
                 dataset_snapshot_download(
                     self.args.dataset,
@@ -174,6 +186,6 @@ class DownloadCMD(CLICommand):
                     allow_file_pattern=convert_patterns(self.args.include),
                     ignore_file_pattern=convert_patterns(self.args.exclude),
                     max_workers=self.args.max_workers,
-                )
+                    cookies=cookies)
         else:
             pass  # noop

modelscope/cli/upload.py

@@ -91,7 +91,7 @@ class UploadCMD(CLICommand):
             '--endpoint',
             type=str,
             default=get_endpoint(),
-            help='Endpoint for Modelscope service.')
+            help='Endpoint for ModelScope service.')
 
         parser.set_defaults(func=subparser_func)
 
@@ -137,14 +137,15 @@ class UploadCMD(CLICommand):
 
         # Check token and login
         # The cookies will be reused if the user has logged in before.
+        cookies = None
         api = HubApi(endpoint=self.args.endpoint)
-
         if self.args.token:
-            api.login(access_token=self.args.token)
+            cookies = api.get_cookies(access_token=self.args.token)
-        cookies = ModelScopeConfig.get_cookies()
+        else:
+            cookies = ModelScopeConfig.get_cookies()
         if cookies is None:
             raise ValueError(
-                'The `token` is not provided! '
+                'No credential found for entity upload. '
                 'You can pass the `--token` argument, '
                 'or use api.login(access_token=`your_sdk_token`). '
                 'Your token is available at https://modelscope.cn/my/myaccesstoken'
@@ -158,6 +159,7 @@ class UploadCMD(CLICommand):
                 repo_type=self.args.repo_type,
                 commit_message=self.args.commit_message,
                 commit_description=self.args.commit_description,
+                token=self.args.token,
             )
         elif os.path.isdir(self.local_path):
             api.upload_folder(
@@ -170,6 +172,7 @@ class UploadCMD(CLICommand):
                 allow_patterns=convert_patterns(self.args.include),
                 ignore_patterns=convert_patterns(self.args.exclude),
                 max_workers=self.args.max_workers,
+                token=self.args.token,
             )
         else:
             raise ValueError(f'{self.local_path} is not a valid local path')

modelscope/hub/api.py

@@ -34,6 +34,7 @@ from modelscope.hub.constants import (API_HTTP_CLIENT_MAX_RETRIES,
                                       API_RESPONSE_FIELD_USERNAME,
                                       DEFAULT_CREDENTIALS_PATH,
                                       DEFAULT_MAX_WORKERS,
+                                      DEFAULT_MODELSCOPE_DOMAIN,
                                       MODELSCOPE_CLOUD_ENVIRONMENT,
                                       MODELSCOPE_CLOUD_USERNAME,
                                       MODELSCOPE_REQUEST_ID, ONE_YEAR_SECONDS,
@@ -112,9 +113,19 @@ class HubApi:
 
         self.upload_checker = UploadingCheck()
 
+    def get_cookies(self, access_token):
+        from requests.cookies import RequestsCookieJar
+        jar = RequestsCookieJar()
+        jar.set('m_session_id',
+                access_token,
+                domain=os.getenv('MODELSCOPE_DOMAIN',
+                                 DEFAULT_MODELSCOPE_DOMAIN),
+                path='/')
+        return jar
+
     def login(
             self,
-            access_token: Optional[str] = None,
+            access_token: Optional[str] = None
     ):
         """Login with your SDK access token, which can be obtained from
            https://www.modelscope.cn user center.

modelscope/hub/check_model.py

@@ -71,6 +71,10 @@ def check_local_model_is_latest(
             headers=snapshot_header,
             use_cookies=cookies,
         )
+        model_cache = None
+        # download via non-git method
+        if not os.path.exists(os.path.join(model_root_path, '.git')):
+            model_cache = ModelFileSystemCache(model_root_path)
         for model_file in model_files:
             if model_file['Type'] == 'tree':
                 continue

modelscope/hub/git.py

@@ -46,6 +46,7 @@ class GitCommandWrapper(metaclass=Singleton):
         git_env = os.environ.copy()
         git_env['GIT_TERMINAL_PROMPT'] = '0'
         command = [self.git_path, *args]
+        command = [item for item in command if item]
         response = subprocess.run(
             command,
             stdout=subprocess.PIPE,

modelscope/pipelines/builder.py

@@ -10,6 +10,8 @@ from modelscope.utils.config import ConfigDict, check_config
 from modelscope.utils.constant import (DEFAULT_MODEL_REVISION, Invoke, Tasks,
                                        ThirdParty)
 from modelscope.utils.hub import read_config
+from modelscope.utils.import_utils import is_transformers_available
+from modelscope.utils.logger import get_logger
 from modelscope.utils.plugins import (register_modelhub_repo,
                                       register_plugins_repo)
 from modelscope.utils.registry import Registry, build_from_cfg
@@ -17,6 +19,7 @@ from .base import Pipeline
 from .util import is_official_hub_path
 
 PIPELINES = Registry('pipelines')
+logger = get_logger()
 
 
 def normalize_model_input(model,
@@ -72,7 +75,7 @@ def pipeline(task: str = None,
              config_file: str = None,
              pipeline_name: str = None,
              framework: str = None,
-             device: str = 'gpu',
+             device: str = None,
              model_revision: Optional[str] = DEFAULT_MODEL_REVISION,
              ignore_file_pattern: List[str] = None,
              **kwargs) -> Pipeline:
@@ -109,6 +112,7 @@ def pipeline(task: str = None,
     if task is None and pipeline_name is None:
         raise ValueError('task or pipeline_name is required')
 
+    pipeline_props = None
     if pipeline_name is None:
         # get default pipeline for this task
         if isinstance(model, str) \
@@ -157,8 +161,11 @@ def pipeline(task: str = None,
                 if pipeline_name:
                     pipeline_props = {'type': pipeline_name}
                 else:
-                    check_config(cfg)
+                    try:
-                    pipeline_props = cfg.pipeline
+                        check_config(cfg)
+                        pipeline_props = cfg.pipeline
+                    except AssertionError as e:
+                        logger.info(str(e))
 
         elif model is not None:
             # get pipeline info from Model object
@@ -166,9 +173,13 @@ def pipeline(task: str = None,
             if not hasattr(first_model, 'pipeline'):
                 # model is instantiated by user, we should parse config again
                 cfg = read_config(first_model.model_dir)
-                check_config(cfg)
+                try:
-                first_model.pipeline = cfg.pipeline
+                    check_config(cfg)
-            pipeline_props = first_model.pipeline
+                    first_model.pipeline = cfg.pipeline
+                except AssertionError as e:
+                    logger.info(str(e))
+            if first_model.__dict__.get('pipeline'):
+                pipeline_props = first_model.pipeline
         else:
             pipeline_name, default_model_repo = get_default_pipeline_info(task)
             model = normalize_model_input(default_model_repo, model_revision)
@@ -176,6 +187,23 @@ def pipeline(task: str = None,
     else:
         pipeline_props = {'type': pipeline_name}
 
+    if not pipeline_props and is_transformers_available():
+        try:
+            from modelscope.utils.hf_util import hf_pipeline
+            return hf_pipeline(
+                task=task,
+                model=model,
+                framework=framework,
+                device=device,
+                **kwargs)
+        except Exception as e:
+            logger.error(
+                'We couldn\'t find a suitable pipeline from ms, so we tried to load it using the transformers pipeline,'
+                ' but that also failed.')
+            raise e
+
+    if not device:
+        device = 'gpu'
     pipeline_props['model'] = model
     pipeline_props['device'] = device
     cfg = ConfigDict(pipeline_props)

modelscope/pipelines/cv/face_processing_base_pipeline.py

@@ -22,7 +22,7 @@ logger = get_logger()
 
 class FaceProcessingBasePipeline(Pipeline):
 
-    def __init__(self, model: str, **kwargs):
+    def __init__(self, model: str, use_det=True, **kwargs):
         """
         use `model` to create a face processing pipeline and output cropped img, scores, bbox and lmks.
 
@@ -30,11 +30,13 @@ class FaceProcessingBasePipeline(Pipeline):
             model: model id on modelscope hub.
 
         """
+        self.use_det = use_det
         super().__init__(model=model, **kwargs)
         # face detect pipeline
-        det_model_id = 'damo/cv_ddsar_face-detection_iclr23-damofd'
+        if use_det:
-        self.face_detection = pipeline(
+            det_model_id = 'damo/cv_ddsar_face-detection_iclr23-damofd'
-            Tasks.face_detection, model=det_model_id)
+            self.face_detection = pipeline(
+                Tasks.face_detection, model=det_model_id)
 
     def _choose_face(self,
                      det_result,
@@ -94,21 +96,27 @@ class FaceProcessingBasePipeline(Pipeline):
     def preprocess(self, input: Input) -> Dict[str, Any]:
         img = LoadImage.convert_to_ndarray(input)
         img = img[:, :, ::-1]
-        det_result = self.face_detection(img.copy())
+        if self.use_det:
-        rtn = self._choose_face(det_result, img_shape=img.shape)
+            det_result = self.face_detection(img.copy())
-        if rtn is not None:
+            rtn = self._choose_face(det_result, img_shape=img.shape)
-            scores, bboxes, face_lmks = rtn
+            if rtn is not None:
-            face_lmks = face_lmks.reshape(5, 2)
+                scores, bboxes, face_lmks = rtn
-            align_img, _ = align_face(img, (112, 112), face_lmks)
+                face_lmks = face_lmks.reshape(5, 2)
+                align_img, _ = align_face(img, (112, 112), face_lmks)
 
-            result = {}
+                result = {}
-            result['img'] = np.ascontiguousarray(align_img)
+                result['img'] = np.ascontiguousarray(align_img)
-            result['scores'] = [scores]
+                result['scores'] = [scores]
-            result['bbox'] = bboxes
+                result['bbox'] = bboxes
-            result['lmks'] = face_lmks
+                result['lmks'] = face_lmks
-            return result
+                return result
+            else:
+                return None
         else:
-            return None
+            result = {}
+            resized_img = cv2.resize(img, (112, 112))
+            result['img'] = np.ascontiguousarray(resized_img)
+            return result
 
     def align_face_padding(self, img, rect, padding_size=16, pad_pixel=127):
         rect = np.reshape(rect, (-1, 4))

modelscope/pipelines/cv/face_recognition_pipeline.py

@@ -14,10 +14,11 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines import pipeline
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES
+from modelscope.pipelines.cv.face_processing_base_pipeline import \
+    FaceProcessingBasePipeline
 from modelscope.preprocessors import LoadImage
 from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.logger import get_logger
-from . import FaceProcessingBasePipeline
 
 logger = get_logger()
 
@@ -26,15 +27,14 @@ logger = get_logger()
     Tasks.face_recognition, module_name=Pipelines.face_recognition)
 class FaceRecognitionPipeline(FaceProcessingBasePipeline):
 
-    def __init__(self, model: str, **kwargs):
+    def __init__(self, model: str, use_det=True, **kwargs):
         """
         use `model` to create a face recognition pipeline for prediction
         Args:
             model: model id on modelscope hub.
         """
-
         # face recong model
-        super().__init__(model=model, **kwargs)
+        super().__init__(model=model, use_det=use_det, **kwargs)
         device = torch.device(
             f'cuda:{0}' if torch.cuda.is_available() else 'cpu')
         self.device = device

modelscope/preprocessors/templates/loader.py

@@ -18,6 +18,7 @@ class TemplateInfo:
     template: str = None
     template_regex: str = None
     modelfile_prefix: str = None
+    allow_general_name: bool = True
 
 
 def cases(*names):
@@ -255,6 +256,12 @@ template_info = [
         modelfile_prefix=
         'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/phi3',
     ),
+    TemplateInfo(
+        template_regex=
+        f'.*{cases("phi4-mini", "phi-4-mini")}.*',
+        modelfile_prefix=
+        'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/phi4-mini',
+    ),
     TemplateInfo(
         template_regex=
         f'.*{cases("phi4", "phi-4")}{no_multi_modal()}.*',
@@ -470,6 +477,12 @@ template_info = [
         modelfile_prefix=
         'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/command-r-plus',
     ),
+    TemplateInfo(
+        template_regex=
+        f'.*{cases("command-r7b-arabic")}.*',
+        modelfile_prefix=
+        'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/command-r7b-arabic',
+    ),
     TemplateInfo(
         template_regex=
         f'.*{cases("command-r7b")}.*',
@@ -663,9 +676,17 @@ template_info = [
         modelfile_prefix=
         'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/granite3-guardian'),
     TemplateInfo(
-        template_regex=f'.*{cases("granite")}.*{cases("code")}.*', 
+        template_regex=f'.*{cases("granite")}.*{cases("code")}.*',
         modelfile_prefix=
         'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/granite-code'),
+    TemplateInfo(
+        template_regex=f'.*{cases("granite")}.*{cases("vision")}.*{cases("3.2")}.*',
+        modelfile_prefix=
+        'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/granite3.2-vision'),
+    TemplateInfo(
+        template_regex=f'.*{cases("granite")}.*{cases("3.2")}.*',
+        modelfile_prefix=
+        'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/granite3.2'),
     TemplateInfo(
         template_regex=f'.*{cases("granite-3.1")}.*{cases("2b", "8b")}.*', 
         modelfile_prefix=
@@ -733,6 +754,12 @@ template_info = [
         modelfile_prefix=
         'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/smallthinker'),
 
+    TemplateInfo(
+        template_regex=f'.*{cases("openthinker")}.*',
+        modelfile_prefix=
+        'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/openthinker',
+        allow_general_name=False),
+
     TemplateInfo(
         template_regex=
         f'.*{cases("olmo2", "olmo-2")}.*',
@@ -888,8 +915,14 @@ template_info = [
         template_regex=f'.*{cases("exaone")}.*',
         modelfile_prefix=
         'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/exaone3.5'),
-
+    TemplateInfo(
-
+        template_regex=f'.*{cases("r1-1776")}.*',
+        modelfile_prefix=
+        'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/r1-1776'),
+    TemplateInfo(
+        template_regex=f'.*{cases("deepscaler")}.*',
+        modelfile_prefix=
+        'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/deepscaler'),
 ]
 
 
@@ -1015,33 +1048,50 @@ class TemplateLoader:
                 f'Please make sure you model_id: {model_id} '
                 f'and template_name: {template_name} is supported.')
         logger.info('Exporting to ollama:')
-        names = []
+        names = {}
+        match_infos = {}
         if gguf_meta:
             gguf_header_name = gguf_meta.get("general.name", None)
-            names.append(gguf_header_name)
+            if gguf_header_name:
+                names['gguf_header_name'] = gguf_header_name
         if model_id:
-            names.append(model_id)
+            names['model_id'] = model_id
-        for name in names:
+        for name_type, name in names.items():
             for _info in template_info:
                 if re.fullmatch(_info.template_regex, name):
                     if _info.modelfile_prefix and not kwargs.get('ignore_oss_model_file', False):
-                        template_str = TemplateLoader._read_content_from_url(
+                        match_infos[name_type] = name, _info
-                            _info.modelfile_prefix + '.template')
+                        break
-                        if not template_str:
+
-                            logger.info(f'{name} has no template file.')
+        _name = None
-                        params = TemplateLoader._read_content_from_url(_info.modelfile_prefix + '.params')
+        _info = None
-                        if params:
+        if len(match_infos) == 1:
-                            params = json.loads(params)
+            _, (_name, _info) = match_infos.popitem()
-                        else:
+        elif len(match_infos) > 1:
-                            logger.info(f'{name} has no params file.')
+            if not match_infos['model_id'][1].allow_general_name:
-                        license = TemplateLoader._read_content_from_url(
+                _name, _info = match_infos['model_id']
-                            _info.modelfile_prefix + '.license')
+            else:
-                        if not template_str:
+                _name, _info = match_infos['gguf_header_name']
-                            logger.info(f'{name} has no license file.')
+
-                        format_out = TemplateLoader._format_return(template_str, params, split, license)
+        if _info:
-                        if debug:
+            template_str = TemplateLoader._read_content_from_url(
-                            return format_out, _info
+                _info.modelfile_prefix + '.template')
-                        return format_out
+            if not template_str:
+                logger.info(f'{_name} has no template file.')
+            params = TemplateLoader._read_content_from_url(_info.modelfile_prefix + '.params')
+            if params:
+                params = json.loads(params)
+            else:
+                logger.info(f'{_name} has no params file.')
+            license = TemplateLoader._read_content_from_url(
+                _info.modelfile_prefix + '.license')
+            if not template_str:
+                logger.info(f'{_name} has no license file.')
+            format_out = TemplateLoader._format_return(template_str, params, split, license)
+            if debug:
+                return format_out, _info
+            return format_out
+
         if template_name:
             template = TemplateLoader.load_by_template_name(
                 template_name, **kwargs)

modelscope/utils/hf_util/__init__.py

@@ -1,2 +1,3 @@
 from .auto_class import *
 from .patcher import patch_context, patch_hub, unpatch_hub
+from .pipeline_builder import hf_pipeline

modelscope/utils/hf_util/patcher.py

@@ -27,7 +27,8 @@ def get_all_imported_modules():
     transformers_include_names = [
         'Auto.*', 'T5.*', 'BitsAndBytesConfig', 'GenerationConfig', 'Awq.*',
         'GPTQ.*', 'BatchFeature', 'Qwen.*', 'Llama.*', 'PretrainedConfig',
-        'PreTrainedTokenizer', 'PreTrainedModel', 'PreTrainedTokenizerFast'
+        'PreTrainedTokenizer', 'PreTrainedModel', 'PreTrainedTokenizerFast',
+        'Pipeline'
     ]
     peft_include_names = ['.*PeftModel.*', '.*Config']
     diffusers_include_names = ['^(?!TF|Flax).*Pipeline$']
@@ -252,6 +253,44 @@ def _patch_pretrained_class(all_imported_modules, wrap=False):
                     model_dir, *model_args, **kwargs)
                 return module_obj
 
+            def save_pretrained(
+                self,
+                save_directory: Union[str, os.PathLike],
+                safe_serialization: bool = True,
+                **kwargs,
+            ):
+                push_to_hub = kwargs.pop('push_to_hub', False)
+                if push_to_hub:
+                    from modelscope.hub.push_to_hub import push_to_hub
+                    from modelscope.hub.api import HubApi
+                    from modelscope.hub.repository import Repository
+
+                    token = kwargs.get('token')
+                    commit_message = kwargs.pop('commit_message', None)
+                    repo_name = kwargs.pop(
+                        'repo_id',
+                        save_directory.split(os.path.sep)[-1])
+
+                    api = HubApi()
+                    api.login(token)
+                    api.create_repo(repo_name)
+                    # clone the repo
+                    Repository(save_directory, repo_name)
+
+                super().save_pretrained(
+                    save_directory=save_directory,
+                    safe_serialization=safe_serialization,
+                    push_to_hub=False,
+                    **kwargs)
+
+                # Class members may be unpatched, so push_to_hub is done separately here
+                if push_to_hub:
+                    push_to_hub(
+                        repo_name=repo_name,
+                        output_dir=save_directory,
+                        commit_message=commit_message,
+                        token=token)
+
         if not hasattr(module_class, 'from_pretrained'):
             del ClassWrapper.from_pretrained
         else:
@@ -266,6 +305,9 @@ def _patch_pretrained_class(all_imported_modules, wrap=False):
         if not hasattr(module_class, 'get_config_dict'):
             del ClassWrapper.get_config_dict
 
+        if not hasattr(module_class, 'save_pretrained'):
+            del ClassWrapper.save_pretrained
+
         ClassWrapper.__name__ = module_class.__name__
         ClassWrapper.__qualname__ = module_class.__qualname__
         return ClassWrapper
@@ -289,12 +331,16 @@ def _patch_pretrained_class(all_imported_modules, wrap=False):
             has_from_pretrained = hasattr(var, 'from_pretrained')
             has_get_peft_type = hasattr(var, '_get_peft_type')
             has_get_config_dict = hasattr(var, 'get_config_dict')
+            has_save_pretrained = hasattr(var, 'save_pretrained')
         except:  # noqa
             continue
 
-        if wrap:
+        # save_pretrained is not a classmethod and cannot be overridden by replacing
+        # the class method. It requires replacing the class object method.
+        if wrap or ('pipeline' in name.lower() and has_save_pretrained):
             try:
-                if not has_from_pretrained and not has_get_config_dict and not has_get_peft_type:
+                if (not has_from_pretrained and not has_get_config_dict
+                        and not has_get_peft_type and not has_save_pretrained):
                     all_available_modules.append(var)
                 else:
                     all_available_modules.append(

modelscope/utils/hf_util/pipeline_builder.py

@@ -0,0 +1,54 @@
+import os
+from typing import Optional, Union
+
+import torch
+from transformers import Pipeline as PipelineHF
+from transformers import PreTrainedModel, TFPreTrainedModel, pipeline
+from transformers.pipelines import check_task, get_task
+
+from modelscope.hub import snapshot_download
+from modelscope.utils.hf_util.patcher import _patch_pretrained_class, patch_hub
+
+
+def _get_hf_device(device):
+    if isinstance(device, str):
+        device_name = device.lower()
+        eles = device_name.split(':')
+        if eles[0] == 'gpu':
+            eles = ['cuda'] + eles[1:]
+            device = ''.join(eles)
+    return device
+
+
+def _get_hf_pipeline_class(task, model):
+    if not task:
+        task = get_task(model)
+    normalized_task, targeted_task, task_options = check_task(task)
+    pipeline_class = targeted_task['impl']
+    pipeline_class = _patch_pretrained_class([pipeline_class])[0]
+    return pipeline_class
+
+
+def hf_pipeline(
+    task: str = None,
+    model: Optional[Union[str, 'PreTrainedModel', 'TFPreTrainedModel']] = None,
+    framework: Optional[str] = None,
+    device: Optional[Union[int, str, 'torch.device']] = None,
+    **kwargs,
+) -> PipelineHF:
+    if isinstance(model, str):
+        if not os.path.exists(model):
+            model = snapshot_download(model)
+
+    framework = 'pt' if framework == 'pytorch' else framework
+
+    device = _get_hf_device(device)
+    pipeline_class = _get_hf_pipeline_class(task, model)
+
+    return pipeline(
+        task=task,
+        model=model,
+        framework=framework,
+        device=device,
+        pipeline_class=pipeline_class,
+        **kwargs)

modelscope/utils/repo_utils.py

@@ -220,7 +220,7 @@ class RepoUtils:
 
 
 @dataclass
-class CommitInfo(str):
+class CommitInfo:
     """Data structure containing information about a newly created commit.
 
     Returned by any method that creates a commit on the Hub: [`create_commit`], [`upload_file`], [`upload_folder`],
@@ -240,46 +240,12 @@ class CommitInfo(str):
         oid (`str`):
             Commit hash id. Example: `"91c54ad1727ee830252e457677f467be0bfd8a57"`.
 
-        pr_url (`str`, *optional*):
-            Url to the PR that has been created, if any. Populated when `create_pr=True`
-            is passed.
-
-        pr_revision (`str`, *optional*):
-            Revision of the PR that has been created, if any. Populated when
-            `create_pr=True` is passed. Example: `"refs/pr/1"`.
-
-        pr_num (`int`, *optional*):
-            Number of the PR discussion that has been created, if any. Populated when
-            `create_pr=True` is passed. Can be passed as `discussion_num` in
-            [`get_discussion_details`]. Example: `1`.
-
-        _url (`str`, *optional*):
-            Legacy url for `str` compatibility. Can be the url to the uploaded file on the Hub (if returned by
-            [`upload_file`]), to the uploaded folder on the Hub (if returned by [`upload_folder`]) or to the commit on
-            the Hub (if returned by [`create_commit`]). Defaults to `commit_url`. It is deprecated to use this
-            attribute. Please use `commit_url` instead.
     """
 
     commit_url: str
     commit_message: str
     commit_description: str
     oid: str
-    pr_url: Optional[str] = None
-
-    # Computed from `pr_url` in `__post_init__`
-    pr_revision: Optional[str] = field(init=False)
-    pr_num: Optional[str] = field(init=False)
-
-    # legacy url for `str` compatibility (ex: url to uploaded file, url to uploaded folder, url to PR, etc.)
-    _url: str = field(
-        repr=False, default=None)  # type: ignore  # defaults to `commit_url`
-
-    def __new__(cls,
-                *args,
-                commit_url: str,
-                _url: Optional[str] = None,
-                **kwargs):
-        return str.__new__(cls, _url or commit_url)
 
     def to_dict(cls):
         return {
@@ -287,7 +253,6 @@ class CommitInfo(str):
             'commit_message': cls.commit_message,
             'commit_description': cls.commit_description,
             'oid': cls.oid,
-            'pr_url': cls.pr_url,
         }
 
 

tests/pipelines/test_face_recognition.py

@@ -17,8 +17,8 @@ class FaceRecognitionTest(unittest.TestCase):
 
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_face_compare(self):
-        img1 = 'data/test/images/face_recognition_1.png'
+        img1 = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/face_recognition_1.png'
-        img2 = 'data/test/images/face_recognition_2.png'
+        img2 = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/face_recognition_2.png'
 
         face_recognition = pipeline(
             Tasks.face_recognition, model=self.model_id)
@@ -27,6 +27,30 @@ class FaceRecognitionTest(unittest.TestCase):
         sim = np.dot(emb1[0], emb2[0])
         print(f'Cos similarity={sim:.3f}, img1:{img1}  img2:{img2}')
 
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_face_compare_use_det(self):
+        img1 = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/face_recognition_1.png'
+        img2 = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/face_recognition_2.png'
+
+        face_recognition = pipeline(
+            Tasks.face_recognition, model=self.model_id, use_det=True)
+        emb1 = face_recognition(img1)[OutputKeys.IMG_EMBEDDING]
+        emb2 = face_recognition(img2)[OutputKeys.IMG_EMBEDDING]
+        sim = np.dot(emb1[0], emb2[0])
+        print(f'Cos similarity={sim:.3f}, img1:{img1}  img2:{img2}')
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_face_compare_not_use_det(self):
+        img1 = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/face_recognition_1.png'
+        img2 = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/face_recognition_2.png'
+
+        face_recognition = pipeline(
+            Tasks.face_recognition, model=self.model_id, use_det=False)
+        emb1 = face_recognition(img1)[OutputKeys.IMG_EMBEDDING]
+        emb2 = face_recognition(img2)[OutputKeys.IMG_EMBEDDING]
+        sim = np.dot(emb1[0], emb2[0])
+        print(f'Cos similarity={sim:.3f}, img1:{img1}  img2:{img2}')
+
 
 if __name__ == '__main__':
     unittest.main()

tests/tools/test_to_ollama.py

@@ -122,6 +122,36 @@ class TestToOllama(unittest.TestCase):
 
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_check_template_type(self):
+        _test_check_tmpl_type(
+            'DevQuasar/CohereForAI.c4ai-command-r7b-arabic-02-2025-GGUF',
+            'command-r7b-arabic',
+            gguf_meta={
+                'general.name': 'CohereForAI.c4ai Command R7B Arabic 02 2025'
+            })
+        _test_check_tmpl_type(
+            'lmstudio-community/granite-vision-3.2-2b-GGUF',
+            'granite3.2-vision',
+            gguf_meta={'general.name': 'Granite Vision 3.2 2b'})
+        _test_check_tmpl_type(
+            'unsloth/Phi-4-mini-instruct-GGUF',
+            'phi4-mini',
+            gguf_meta={'general.name': 'Phi 4 Mini Instruct'})
+        _test_check_tmpl_type(
+            'lmstudio-community/granite-3.2-2b-instruct-GGUF',
+            'granite3.2',
+            gguf_meta={'general.name': 'Granite 3.2 2b Instruct'})
+        _test_check_tmpl_type(
+            'unsloth/r1-1776-GGUF',
+            'r1-1776',
+            gguf_meta={'general.name': 'R1 1776'})
+        _test_check_tmpl_type(
+            'QuantFactory/DeepScaleR-1.5B-Preview-GGUF',
+            'deepscaler',
+            gguf_meta={'general.name': 'DeepScaleR 1.5B Preview'})
+        _test_check_tmpl_type(
+            'lmstudio-community/OpenThinker-32B-GGUF',
+            'openthinker',
+            gguf_meta={'general.name': 'Qwen2.5 7B Instruct'})
         _test_check_tmpl_type(
             'LLM-Research/Llama-3.3-70B-Instruct',
             'llama3.3',

tests/utils/test_hf_util.py

@@ -40,6 +40,14 @@ class HFUtilTest(unittest.TestCase):
         with open(self.test_file2, 'w') as f:
             f.write('{}')
 
+        self.pipeline_qa_context = r"""
+            Extractive Question Answering is the task of extracting an answer from a text given a question. An example
+            of a question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would
+            like to fine-tune a model on a SQuAD task, you may leverage the
+            examples/pytorch/question-answering/run_squad.py script.
+            """
+        self.pipeline_qa_question = 'What is a good example of a question answering dataset?'
+
     def tearDown(self):
         logger.info('TearDown')
         shutil.rmtree(self.model_dir, ignore_errors=True)
@@ -235,6 +243,59 @@ class HFUtilTest(unittest.TestCase):
                 'Qwen/Qwen1.5-0.5B-Chat', trust_remote_code=True)
             model.push_to_hub(self.create_model_name)
 
+    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
+    def test_pipeline_model_id(self):
+        from modelscope import pipeline
+        model_id = 'damotestx/distilbert-base-cased-distilled-squad'
+        qa = pipeline('question-answering', model=model_id)
+        assert qa(
+            question=self.pipeline_qa_question,
+            context=self.pipeline_qa_context)
+
+    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
+    def test_pipeline_auto_model(self):
+        from modelscope import pipeline, AutoModelForQuestionAnswering, AutoTokenizer
+        model_id = 'damotestx/distilbert-base-cased-distilled-squad'
+        model = AutoModelForQuestionAnswering.from_pretrained(model_id)
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        qa = pipeline('question-answering', model=model, tokenizer=tokenizer)
+        assert qa(
+            question=self.pipeline_qa_question,
+            context=self.pipeline_qa_context)
+
+    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
+    def test_pipeline_save_pretrained(self):
+        from modelscope import pipeline
+        model_id = 'damotestx/distilbert-base-cased-distilled-squad'
+
+        pipe_ori = pipeline('question-answering', model=model_id)
+
+        result_ori = pipe_ori(
+            question=self.pipeline_qa_question,
+            context=self.pipeline_qa_context)
+
+        # save_pretrained
+        repo_id = self.create_model_name
+        save_dir = './tmp_test_hf_pipeline'
+        try:
+            os.system(f'rm -rf {save_dir}')
+            self.api.delete_model(repo_id)
+            # wait for delete repo
+            import time
+            time.sleep(5)
+        except Exception:
+            # if repo not exists
+            pass
+        pipe_ori.save_pretrained(save_dir, push_to_hub=True, repo_id=repo_id)
+
+        # load from saved
+        pipe_new = pipeline('question-answering', model=repo_id)
+        result_new = pipe_new(
+            question=self.pipeline_qa_question,
+            context=self.pipeline_qa_context)
+
+        assert result_new == result_ori
+
 
 if __name__ == '__main__':
     unittest.main()