Instructions to use sraj/Merge_Linear_NormBalanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sraj/Merge_Linear_NormBalanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sraj/Merge_Linear_NormBalanced")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sraj/Merge_Linear_NormBalanced") model = AutoModelForMaskedLM.from_pretrained("sraj/Merge_Linear_NormBalanced") - Notebooks
- Google Colab
- Kaggle
metadata
base_model:
- sraj/CMB_MARK_CX_LRD
- sraj/CMB_FWEdu_V2_FastTxt_CX_LRD
- sraj/CMB_WX_SYN_CX_LRD
library_name: transformers
tags:
- mergekit
- merge
merge_linear_normbalanced
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:
# Norm-balanced: weights set inversely proportional to avg task vector L2 norm
# A ≈ 16, F ≈ 43, S ≈ 16 → weights ≈ 1/16, 1/43, 1/16 → normalized ≈ 2.7, 1.0, 2.7
models:
- model: sraj/CMB_MARK_CX_LRD
parameters:
weight: 2.7
- model: sraj/CMB_FWEdu_V2_FastTxt_CX_LRD
parameters:
weight: 1.0
- model: sraj/CMB_WX_SYN_CX_LRD
parameters:
weight: 2.7
merge_method: linear
parameters:
normalize: true
dtype: bfloat16