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README.md

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+---
+base_model:
+- mlabonne/Hermes-3-Llama-3.1-8B-lorablated
+- Solshine/reflection-llama-3.1-8B
+- Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder
+- Solshine/reflection-llama-3.1-8B
+- mlabonne/Hermes-3-Llama-3.1-8B-lorablated
+tags:
+- merge
+- mergekit
+- lazymergekit
+- mlabonne/Hermes-3-Llama-3.1-8B-lorablated
+- Solshine/reflection-llama-3.1-8B
+- Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder
+---
+
+# Llama-3-1-8B-big-thoughtful-passthrough-merge
+
+Llama-3-1-8B-big-thoughtful-passthrough-merge is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+* [mlabonne/Hermes-3-Llama-3.1-8B-lorablated](https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated)
+* [Solshine/reflection-llama-3.1-8B](https://huggingface.co/Solshine/reflection-llama-3.1-8B)
+* [Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder](https://huggingface.co/Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder)
+* [Solshine/reflection-llama-3.1-8B](https://huggingface.co/Solshine/reflection-llama-3.1-8B)
+* [mlabonne/Hermes-3-Llama-3.1-8B-lorablated](https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated)
+
+## 🧩 Configuration
+
+```yaml
+slices:
+- sources:
+  - layer_range: [0, 16]
+    model: mlabonne/Hermes-3-Llama-3.1-8B-lorablated
+- sources:
+  - layer_range: [4, 20]
+    model: Solshine/reflection-llama-3.1-8B
+- sources:
+  - layer_range: [8, 24]
+    model: Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder
+- sources:
+  - layer_range: [12, 28]
+    model: Solshine/reflection-llama-3.1-8B
+- sources:
+  - layer_range: [16, 32]
+    model: mlabonne/Hermes-3-Llama-3.1-8B-lorablated
+merge_method: passthrough
+dtype: float16
+```
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "Solshine/Llama-3-1-8B-big-thoughtful-passthrough-merge"
+messages = [{"role": "user", "content": "What is a large language model?"}]
+
+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"])
+```