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---
base_model:
- LeroyDyer/Mixtral_AI_CyberBrain_2.0
- ezelikman/quietstar-8-ahead
library_name: transformers
tags:
- mergekit
- merge
license: mit
language:
- en
---
hopefully this merge took correctly ! ....
Enabling for thights to be displayed ;
obviously untrained and will still need fine tuning !
as well as it has not been correctly coded for true management via transformers pretrained args.
i will try to add the other arch: leaving it available to perhaps load with different remote auto mapping! ,
I will leve both automapping here and test both models to see which configuration loads correctly for training ! then wich loads correctly for usage ; as this also has been a minor issue ;
the internall heads have default settings ; with remote code installed then its should be configuarble.
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [LeroyDyer/Mixtral_AI_CyberBrain_2.0](https://huggingface.co/LeroyDyer/Mixtral_AI_CyberBrain_2.0)
* [ezelikman/quietstar-8-ahead](https://huggingface.co/ezelikman/quietstar-8-ahead)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: LeroyDyer/Mixtral_AI_CyberBrain_2.0
layer_range: [0, 32]
- model: ezelikman/quietstar-8-ahead
layer_range: [0, 32]
# or, the equivalent models: syntax:
# models:
# - model: mistralai/Mistral-7B-Instruct-v0.2
# LaRGER MODEL MUST BE BASE or
# BASE MODEL MUST BE THE TOKENIZER YOU WISH TO ADOPT
# so for models with customized processes they must be the base model
# If the base model has remote code then this must be collected and added
# to the repo after and the config file adusted to allow for automapping to your new repo
# - model: yanismiraoui/Yarn-Mistral-7b-128k-sharded
merge_method: slerp
base_model: ezelikman/quietstar-8-ahead
parameters:
t:
- filter: self_attn
value: [0.3, 0.6, 0.3786, 0.6, 0.6]
- filter: mlp
value: [0.7, 0.4, 0.6, 0.4, 0.7]
- value: 0.5 # fallback for rest of tensors
dtype: float16
``` |