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README.md

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+---
+license: apache-2.0
+tags:
+- merge
+- mergekit
+- BioMistral/BioMistral-7B-DARE
+- NousResearch/Nous-Hermes-2-Mistral-7B-DPO
+---
+
+# BioMistral-Hermes-Slerp
+
+BioMistral-Hermes-Slerp is a merge of the following models:
+* [BioMistral/BioMistral-7B-DARE](https://huggingface.co/BioMistral/BioMistral-7B-DARE)
+* [NousResearch/Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO)
+
+## Evaluations
+
+| Benchmark | BioMistral-Hermes-Slerp | Orca-2-7b | llama-2-7b | meditron-7b | meditron-70b |
+| --- | --- | --- | --- | --- | --- |
+| MedMCQA |  |  |  |  |  |
+| ClosedPubMedQA |  |  |  |  |  |
+| PubMedQA |  |  |  |  |  |
+| MedQA |  |  |  |  |  |
+| MedQA4 |  |  |  |  |  |
+| MedicationQA |  |  |  |  |  |
+| MMLU Medical |  |  |  |  |  |
+| MMLU |  |  |  |  |  |
+| TruthfulQA |  |  |  |  |  |
+| GSM8K |  |  |  |  |  |
+| ARC |  |  |  |  |  |
+| HellaSwag |  |  |  |  |  |
+| Winogrande |  |  |  |  |  |
+
+More details on the Open LLM Leaderboard evaluation results can be found here.
+
+## 🧩 Configuration
+
+```yaml
+slices:
+  - sources:
+      - model: BioMistral/BioMistral-7B-DARE
+        layer_range: [0, 32]
+      - model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
+        layer_range: [0, 32]
+merge_method: slerp
+base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
+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 # fallback for rest of tensors
+dtype: float16
+```
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "Technoculture/BioMistral-Hermes-Slerp"
+messages = [{"role": "user", "content": "I am feeling sleepy these days"}]
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+```

added_tokens.json

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+{
+  "<|im_end|>": 32000,
+  "<|im_start|>": 32001
+}

config.json

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+{
+  "_name_or_path": "NousResearch/Nous-Hermes-2-Mistral-7B-DPO",
+  "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": 32768,
+  "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": "float16",
+  "transformers_version": "4.37.2",
+  "use_cache": false,
+  "vocab_size": 32002
+}

mergekit_config.yml

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+
+slices:
+  - sources:
+      - model: BioMistral/BioMistral-7B-DARE
+        layer_range: [0, 32]
+      - model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
+        layer_range: [0, 32]
+merge_method: slerp
+base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
+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 # fallback for rest of tensors
+dtype: float16

model-00001-of-00002.safetensors

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