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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,66 @@
+---
+base_model:
+- bamec66557/MNRP_0.5
+- bamec66557/MISCHIEVOUS-12B
+library_name: transformers
+tags:
+- mergekit
+- merge
+
+---
+# 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:
+* [bamec66557/MNRP_0.5](https://huggingface.co/bamec66557/MNRP_0.5)
+* [bamec66557/MISCHIEVOUS-12B](https://huggingface.co/bamec66557/MISCHIEVOUS-12B)
+
+### Configuration
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+slices:
+  - sources:
+      - model: bamec66557/MNRP_0.5
+        layer_range: [0, 40]  # MNRP_0.5 모델의 병합 레이어 범위
+      - model: bamec66557/MISCHIEVOUS-12B
+        layer_range: [0, 40]  # MISCHIEVOUS-12B 모델의 병합 레이어 범위
+    
+    # Layer별 병합 비율을 조정하여 더 부드러운 통합을 유도
+    # 각 필터는 모델 내 특정 메커니즘에 영향을 미침
+    parameters:
+      t:
+        - filter: self_attn
+          value: [0.2, 0.4, 0.6, 0.8, 1.0]  # Self-attention 레이어의 점진적 병합
+        - filter: mlp
+          value: [0.8, 0.6, 0.4, 0.2, 0.0]  # MLP 레이어는 반대 비율로 병합
+        - filter: layer_norm
+          value: [0.5, 0.5, 0.5, 0.5, 0.5]  # Layer Normalization은 균일 병합
+        - value: 0.7  # 기본값
+
+merge_method: slerp  # 병합 방식을 slerp로 변경
+
+base_model: bamec66557/MISCHIEVOUS-12B  # 병합의 기본 모델
+
+dtype: bfloat16  # 병합 시 효율적이고 빠른 연산을 위한 데이터 타입
+
+# 추가적으로 사용할 수 있는 옵션
+regularization:
+  - method: l2_norm  # L2 정규화를 통해 병합된 모델 가중치 안정화
+    scale: 0.01
+
+postprocessing:
+  - operation: smoothing  # 병합 후 가중치를 부드럽게 조정
+    kernel_size: 3
+  - operation: normalize  # 전체 가중치를 정규화
+
+```

config.json

@@ -0,0 +1,27 @@
+{
+  "_name_or_path": "bamec66557/MISCHIEVOUS-12B",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 1024000,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.2",
+  "use_cache": true,
+  "vocab_size": 131074
+}

mergekit_config.yml

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+slices:
+  - sources:
+      - model: bamec66557/MNRP_0.5
+        layer_range: [0, 40]  # MNRP_0.5 모델의 병합 레이어 범위
+      - model: bamec66557/MISCHIEVOUS-12B
+        layer_range: [0, 40]  # MISCHIEVOUS-12B 모델의 병합 레이어 범위
+    
+    # Layer별 병합 비율을 조정하여 더 부드러운 통합을 유도
+    # 각 필터는 모델 내 특정 메커니즘에 영향을 미침
+    parameters:
+      t:
+        - filter: self_attn
+          value: [0.2, 0.4, 0.6, 0.8, 1.0]  # Self-attention 레이어의 점진적 병합
+        - filter: mlp
+          value: [0.8, 0.6, 0.4, 0.2, 0.0]  # MLP 레이어는 반대 비율로 병합
+        - filter: layer_norm
+          value: [0.5, 0.5, 0.5, 0.5, 0.5]  # Layer Normalization은 균일 병합
+        - value: 0.7  # 기본값
+
+merge_method: slerp  # 병합 방식을 slerp로 변경
+
+base_model: bamec66557/MISCHIEVOUS-12B  # 병합의 기본 모델
+
+dtype: bfloat16  # 병합 시 효율적이고 빠른 연산을 위한 데이터 타입
+
+# 추가적으로 사용할 수 있는 옵션
+regularization:
+  - method: l2_norm  # L2 정규화를 통해 병합된 모델 가중치 안정화
+    scale: 0.01
+
+postprocessing:
+  - operation: smoothing  # 병합 후 가중치를 부드럽게 조정
+    kernel_size: 3
+  - operation: normalize  # 전체 가중치를 정규화

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special_tokens_map.json

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tokenizer.json

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