Update README.md

README.md

@@ -10,4 +10,29 @@ DARE TIE mergers are [very strong at transferring strengths](https://medium.com/
 
 For 7B models, we can't drop as many of the parameters and retain the model's strengths. In the original paper, the WizardMath model showed transferrable skills when 90% of the parameters were dropped but showed more strength when 70% were dropped. Experimentally, it appears that [even lower drop rates like 40%](https://github.com/cg123/mergekit/issues/26) have performed the best even for larger 34B models. In some instances, [even densities as high as 80% create an unstable merger](https://huggingface.co/jan-hq/supermario-v1), making DARE TIES unsuitable for merging models.
 
-This is an experiment utilizing two merger techniques together to try and transfer skills between finetuned models. If we were to DARE TIE a low density merger onto the base Mistral model and then task arithmetic merge those low density delta weights onto a finetune, could we still achieve skill transfer?
+This is an experiment utilizing two merger techniques together to try and transfer skills between finetuned models. If we were to DARE TIE a low density merger onto the base Mistral model and then task arithmetic merge those low density delta weights onto a finetune, could we still achieve skill transfer?
+
+```
+models:
+  - model: mistralai/Mistral-7B-v0.1
+    # no parameters necessary for base model
+  - model: WizardLM/WizardMath-7B-V1.1
+    parameters:
+      weight: 1
+      density: 0.3
+merge_method: dare_ties
+base_model: mistralai/Mistral-7B-v0.1
+parameters:
+  normalize: true
+  int8_mask: true
+dtype: bfloat16
+
+merge_method: task_arithmetic
+base_model: mistralai/Mistral-7B-v0.1
+models:
+  - model: C:\Users\sanji\Documents\Apps\text-generation-webui-main\models\mistral-wizardmath-dare-0.7
+  - model: Intel/neural-chat-7b-v3-3
+parameters:
+  weight: 1.0
+dtype: bfloat16
+```