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---
license: apache-2.0
---
My first successful Dare-Ties merge. Because of the tokenizer difference of the model types (also bf16 vs f16), Had to use Slerp as well.
Seems to perform well! Did a local lm-eval and HellaSWAG gives me around 84.5, which seems decent. will be submitting this for eval on the openLLM leaderboard as well.
Preset for this should be ChatML, but standard default presets should work ok too.
---
base_model:
- senseable/WestLake-7B-v2
- cognitivecomputations/dolphin-2.8-mistral-7b-v02
library_name: transformers
tags:
- mergekit
- merge
---
# Noodlz_DolphinLake-DARE_TIE_SLERP-tokenwest
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 [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [cognitivecomputations/dolphin-2.8-mistral-7b-v02](https://huggingface.co/cognitivecomputations/dolphin-2.8-mistral-7b-v02) as a base.
### Models Merged
The following models were included in the merge:
* [senseable/WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: dare_ties
parameters:
int8_mask: true
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # fallback for rest of tensors
embed_slerp: true
models:
- model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
# No parameters necessary for base model
- model: senseable/WestLake-7B-v2
parameters:
density: 0.58
weight: 0.8
base_model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
tokenizer_source: model:senseable/WestLake-7B-v2
dtype: bfloat16
``` |