mirror of
https://github.com/modelscope/modelscope.git
synced 2025-12-16 16:27:45 +01:00
Merge remote-tracking branch 'origin' into fallback
This commit is contained in:
8
.github/workflows/docker-image.yml
vendored
8
.github/workflows/docker-image.yml
vendored
@@ -26,8 +26,12 @@ on:
|
||||
other_params:
|
||||
description: 'Other params in --xxx xxx'
|
||||
required: false
|
||||
python_version:
|
||||
description: 'Python version to use, default is 3.10.14'
|
||||
required: false
|
||||
default: '3.10.14'
|
||||
|
||||
run-name: Docker-${{ inputs.modelscope_branch }}-${{ inputs.image_type }}-${{ inputs.workflow_name }}-by-@${{ github.actor }}
|
||||
run-name: Docker-${{ inputs.modelscope_branch }}-${{ inputs.image_type }}-${{ inputs.workflow_name }}-${{ inputs.python_version }}-by-@${{ github.actor }}
|
||||
|
||||
jobs:
|
||||
build:
|
||||
@@ -51,4 +55,4 @@ jobs:
|
||||
run: |
|
||||
set -e
|
||||
source ~/.bashrc
|
||||
python docker/build_image.py --image_type ${{ github.event.inputs.image_type }} --modelscope_branch ${{ github.event.inputs.modelscope_branch }} --modelscope_version ${{ github.event.inputs.modelscope_version }} --swift_branch ${{ github.event.inputs.swift_branch }} --ci_image ${{ github.event.inputs.ci_image }} ${{ github.event.inputs.other_params }}
|
||||
python docker/build_image.py --image_type ${{ github.event.inputs.image_type }} --python_version ${{ github.event.inputs.python_version }} --modelscope_branch ${{ github.event.inputs.modelscope_branch }} --modelscope_version ${{ github.event.inputs.modelscope_version }} --swift_branch ${{ github.event.inputs.swift_branch }} --ci_image ${{ github.event.inputs.ci_image }} ${{ github.event.inputs.other_params }}
|
||||
|
||||
@@ -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'
|
||||
self.face_detection = pipeline(
|
||||
Tasks.face_detection, model=det_model_id)
|
||||
if use_det:
|
||||
det_model_id = 'damo/cv_ddsar_face-detection_iclr23-damofd'
|
||||
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())
|
||||
rtn = self._choose_face(det_result, img_shape=img.shape)
|
||||
if rtn is not None:
|
||||
scores, bboxes, face_lmks = rtn
|
||||
face_lmks = face_lmks.reshape(5, 2)
|
||||
align_img, _ = align_face(img, (112, 112), face_lmks)
|
||||
if self.use_det:
|
||||
det_result = self.face_detection(img.copy())
|
||||
rtn = self._choose_face(det_result, img_shape=img.shape)
|
||||
if rtn is not None:
|
||||
scores, bboxes, face_lmks = rtn
|
||||
face_lmks = face_lmks.reshape(5, 2)
|
||||
align_img, _ = align_face(img, (112, 112), face_lmks)
|
||||
|
||||
result = {}
|
||||
result['img'] = np.ascontiguousarray(align_img)
|
||||
result['scores'] = [scores]
|
||||
result['bbox'] = bboxes
|
||||
result['lmks'] = face_lmks
|
||||
return result
|
||||
result = {}
|
||||
result['img'] = np.ascontiguousarray(align_img)
|
||||
result['scores'] = [scores]
|
||||
result['bbox'] = bboxes
|
||||
result['lmks'] = face_lmks
|
||||
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))
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -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")}.*',
|
||||
@@ -666,6 +679,14 @@ template_info = [
|
||||
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'),
|
||||
]
|
||||
|
||||
|
||||
@@ -1014,33 +1047,50 @@ 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)
|
||||
for name in names:
|
||||
names['model_id'] = model_id
|
||||
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(
|
||||
_info.modelfile_prefix + '.template')
|
||||
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
|
||||
match_infos[name_type] = name, _info
|
||||
break
|
||||
|
||||
_name = None
|
||||
_info = None
|
||||
if len(match_infos) == 1:
|
||||
_, (_name, _info) = match_infos.popitem()
|
||||
elif len(match_infos) > 1:
|
||||
if not match_infos['model_id'][1].allow_general_name:
|
||||
_name, _info = match_infos['model_id']
|
||||
else:
|
||||
_name, _info = match_infos['gguf_header_name']
|
||||
|
||||
if _info:
|
||||
template_str = TemplateLoader._read_content_from_url(
|
||||
_info.modelfile_prefix + '.template')
|
||||
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)
|
||||
|
||||
@@ -345,7 +345,7 @@ def _patch_pretrained_class(all_imported_modules, wrap=False):
|
||||
else:
|
||||
all_available_modules.append(
|
||||
get_wrapped_class(var, **ignore_file_pattern_kwargs))
|
||||
except: # noqa
|
||||
except Exception:
|
||||
all_available_modules.append(var)
|
||||
else:
|
||||
if has_from_pretrained and not hasattr(var,
|
||||
@@ -370,9 +370,10 @@ def _patch_pretrained_class(all_imported_modules, wrap=False):
|
||||
if has_get_config_dict and not hasattr(var,
|
||||
'_get_config_dict_origin'):
|
||||
var._get_config_dict_origin = var.get_config_dict
|
||||
var.get_config_dict = classmethod(
|
||||
partial(patch_get_config_dict,
|
||||
**ignore_file_pattern_kwargs))
|
||||
var.get_config_dict = partial(
|
||||
patch_pretrained_model_name_or_path,
|
||||
ori_func=var._get_config_dict_origin,
|
||||
**ignore_file_pattern_kwargs)
|
||||
|
||||
all_available_modules.append(var)
|
||||
return all_available_modules
|
||||
@@ -618,6 +619,11 @@ def _patch_hub():
|
||||
# Patch repocard.validate
|
||||
from huggingface_hub import repocard
|
||||
if not hasattr(repocard.RepoCard, '_validate_origin'):
|
||||
|
||||
def load(*args, **kwargs): # noqa
|
||||
from huggingface_hub.errors import EntryNotFoundError
|
||||
raise EntryNotFoundError(message='API not supported.')
|
||||
|
||||
repocard.RepoCard._validate_origin = repocard.RepoCard.validate
|
||||
repocard.RepoCard.validate = lambda *args, **kwargs: None
|
||||
repocard.RepoCard._load_origin = repocard.RepoCard.load
|
||||
|
||||
@@ -1,13 +1,8 @@
|
||||
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
|
||||
from modelscope.utils.hf_util.patcher import _patch_pretrained_class
|
||||
|
||||
|
||||
def _get_hf_device(device):
|
||||
@@ -21,6 +16,7 @@ def _get_hf_device(device):
|
||||
|
||||
|
||||
def _get_hf_pipeline_class(task, model):
|
||||
from transformers.pipelines import check_task, get_task
|
||||
if not task:
|
||||
task = get_task(model)
|
||||
normalized_task, targeted_task, task_options = check_task(task)
|
||||
@@ -35,7 +31,9 @@ def hf_pipeline(
|
||||
framework: Optional[str] = None,
|
||||
device: Optional[Union[int, str, 'torch.device']] = None,
|
||||
**kwargs,
|
||||
) -> PipelineHF:
|
||||
) -> 'transformers.Pipeline':
|
||||
from transformers import pipeline
|
||||
|
||||
if isinstance(model, str):
|
||||
if not os.path.exists(model):
|
||||
model = snapshot_download(model)
|
||||
|
||||
@@ -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,
|
||||
}
|
||||
|
||||
|
||||
|
||||
@@ -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'
|
||||
img2 = 'data/test/images/face_recognition_2.png'
|
||||
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)
|
||||
@@ -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()
|
||||
|
||||
@@ -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',
|
||||
|
||||
Reference in New Issue
Block a user