
# merge-arxiv-50_github-50_avg

This model is a merge between the following models:

- https://huggingface.co/Multi-Domain-Expert-Layers/expert-arxiv
- https://huggingface.co/Multi-Domain-Expert-Layers/expert-github

Using a naive weight averaging strategy at a 50/50 ratio per model.

It has yet to be evaluated.


## Model description

More information needed

## Intended uses & limitations

More information needed

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["merge"], "model-index": [{"name": "expert-uspto", "results": []}]}
transformers pytorch gpt_neox text-generation merge license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt_neox #text-generation #merge #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# merge-arxiv-50_github-50_avg

This model is a merge between the following models:

- URL
- URL

Using a naive weight averaging strategy at a 50/50 ratio per model.

It has yet to be evaluated.


## Model description

More information needed

## Intended uses & limitations

More information needed

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3

# merge-arxiv-50_github-50_avg

This model is a merge between the following models:

- URL
- URL

Using a naive weight averaging strategy at a 50/50 ratio per model.

It has yet to be evaluated. ## Model description

More information needed ## Intended uses & limitations

More information needed ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #gpt_neox #text-generation #merge #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # merge-arxiv-50_github-50_avg

This model is a merge between the following models:

- URL
- URL

Using a naive weight averaging strategy at a 50/50 ratio per model.

It has yet to be evaluated. ## Model description

More information needed ## Intended uses & limitations

More information needed ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3