Update README.md

README.md

@@ -20,6 +20,36 @@ based mistral, instruction tuned and dpo.
 
 merge mncai/mistral-7b-dpo-v6, rwitz2/go-bruins-v2.1.1, ignos/LeoScorpius-GreenNode-Alpaca-7B-v1.
 
+### Details
+
+ties
+
+```
+models:
+  - model: rwitz2/go-bruins-v2.1.1
+    # no parameters necessary for base model
+  - model: janai-hq/trinity-v1 # psmathur/orca_mini_v3_13b
+    parameters:
+      density: [1, 0.7, 0.1] # density gradient
+      weight: 1.0
+  - model: ignos/LeoScorpius-GreenNode-Alpaca-7B-v1
+    parameters:
+      density: 0.5
+      weight: [0, 0.3, 0.7, 1] # weight gradient
+  - model: mncai/mistral-7b-dpo-v6
+    parameters:
+      density: 0.33
+      weight:
+        - filter: mlp
+          value: 0.5
+        - value: 0
+merge_method: ties
+base_model: rwitz2/go-bruins-v2.1.1
+parameters:
+  normalize: true
+  int8_mask: true
+dtype: float16
+```
 
 ### How to Use
 Here give some examples of how to use our model.
@@ -43,6 +73,10 @@ for seq in sequences:
     print(f"Result: {seq['generated_text']}")
 ```
 
+### Warnings
+Currently, the leaderboard is overfitted. It is inevitable because, unlike Kaggle, where there's private scoring followed by the end of the competition, here the scores are continuously open.
+Even among my models, some received lower scores in internal data evaluations. mncai/agiin-13.6B-v0.1 > mncai/agiin-11.1B-v0.1 > mncai/mistral-7b-dpo-v6. However, on the leaderboard, mncai/mistral-7b-dpo-v6 has the highest score.
+When choosing a model to use on the open LLM leaderboard, it would be best to evaluate with your own private dataset that is not publicly available.
 
 ### Contact
 If you have any questions, please raise an issue or contact us at [email protected]