Instructions to use sraj/Merge_Drop_MARK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sraj/Merge_Drop_MARK with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sraj/Merge_Drop_MARK")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sraj/Merge_Drop_MARK") model = AutoModelForMaskedLM.from_pretrained("sraj/Merge_Drop_MARK", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from sraj/Merge_Drop_MARK: direct link, hf CLI and curl.
- Browser
- Download file 909 Bytes
-
https://huggingface.co/sraj/Merge_Drop_MARK/resolve/main/README.md
- Command line
-
hf download hf://sraj/Merge_Drop_MARK/README.md
-
curl -L -o README.md https://huggingface.co/sraj/Merge_Drop_MARK/resolve/main/README.md
909 Bytes
metadata
base_model:
- sraj/CMB_FWEdu_V2_CX_LRD
- sraj/CMB_WX_SYN_CX_LRD
library_name: transformers
tags:
- mergekit
- merge
merge_drop_MARK
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the Linear merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: sraj/CMB_FWEdu_V2_CX_LRD
parameters:
weight: 1.0
- model: sraj/CMB_WX_SYN_CX_LRD
parameters:
weight: 1.0
merge_method: linear
parameters:
normalize: true
dtype: bfloat16