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.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using cognitivecomputations/dolphin-2.8-mistral-7b-v02 as a base.

Models Merged

The following models were included in the merge:

senseable/WestLake-7B-v2

Configuration

The following YAML configuration was used to produce this model:

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