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README.md

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+---
+license: apache-2.0
+tags:
+- merge
+- mergekit
+- lazymergekit
+- berkeley-nest/Starling-LM-7B-alpha
+- shahzebnaveed/NeuralHermes-2.5-Mistral-7B
+---
+
+# StarlingHermes-2.5-Mistral-7B-slerp
+
+StarlingHermes-2.5-Mistral-7B-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
+* [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)
+* [shahzebnaveed/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/shahzebnaveed/NeuralHermes-2.5-Mistral-7B)
+
+## 🧩 Configuration
+
+```yaml
+slices:
+  - sources:
+      - model: berkeley-nest/Starling-LM-7B-alpha
+        layer_range: [0, 32]
+      - model: shahzebnaveed/NeuralHermes-2.5-Mistral-7B
+        layer_range: [0, 32]
+merge_method: slerp
+base_model: berkeley-nest/Starling-LM-7B-alpha
+parameters:
+  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
+dtype: bfloat16
+
+```

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config.json

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+{
+  "_name_or_path": "berkeley-nest/Starling-LM-7B-alpha",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 32000,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 8192,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.37.0",
+  "use_cache": true,
+  "vocab_size": 32002
+}

mergekit_config.yml

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+
+slices:
+  - sources:
+      - model: berkeley-nest/Starling-LM-7B-alpha
+        layer_range: [0, 32]
+      - model: shahzebnaveed/NeuralHermes-2.5-Mistral-7B
+        layer_range: [0, 32]
+merge_method: slerp
+base_model: berkeley-nest/Starling-LM-7B-alpha
+parameters:
+  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
+dtype: bfloat16
+

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