README.md

---
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"])
```