Instructions to use aehrc/cxrmate-rrg24 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aehrc/cxrmate-rrg24 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="aehrc/cxrmate-rrg24", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("aehrc/cxrmate-rrg24", trust_remote_code=True) model = AutoModel.from_pretrained("aehrc/cxrmate-rrg24", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- modelling_cxrrg.py +2 -1
modelling_cxrrg.py
CHANGED
|
@@ -4,7 +4,6 @@ from typing import Optional, Tuple, Union
|
|
| 4 |
|
| 5 |
import torch
|
| 6 |
import transformers
|
| 7 |
-
from modelling_uniformer import MultiUniFormerWithProjectionHead
|
| 8 |
from torch.nn import CrossEntropyLoss, Linear
|
| 9 |
from transformers import PreTrainedTokenizerFast, VisionEncoderDecoderModel
|
| 10 |
from transformers.configuration_utils import PretrainedConfig
|
|
@@ -15,6 +14,8 @@ from transformers.models.vision_encoder_decoder.configuration_vision_encoder_dec
|
|
| 15 |
)
|
| 16 |
from transformers.utils import logging
|
| 17 |
|
|
|
|
|
|
|
| 18 |
logger = logging.get_logger(__name__)
|
| 19 |
|
| 20 |
|
|
|
|
| 4 |
|
| 5 |
import torch
|
| 6 |
import transformers
|
|
|
|
| 7 |
from torch.nn import CrossEntropyLoss, Linear
|
| 8 |
from transformers import PreTrainedTokenizerFast, VisionEncoderDecoderModel
|
| 9 |
from transformers.configuration_utils import PretrainedConfig
|
|
|
|
| 14 |
)
|
| 15 |
from transformers.utils import logging
|
| 16 |
|
| 17 |
+
from .modelling_uniformer import MultiUniFormerWithProjectionHead
|
| 18 |
+
|
| 19 |
logger = logging.get_logger(__name__)
|
| 20 |
|
| 21 |
|