README.md

---
tags:
- merge
- mergekit
- lazymergekit
- NousResearch/Meta-Llama-3-8B-Instruct
- elinas/Llama-3-8B-Ultra-Instruct
- mlabonne/ChimeraLlama-3-8B-v3
- nvidia/Llama3-ChatQA-1.5-8B
- Kukedlc/SmartLlama-3-8B-MS-v0.1
base_model:
- NousResearch/Meta-Llama-3-8B-Instruct
- elinas/Llama-3-8B-Ultra-Instruct
- mlabonne/ChimeraLlama-3-8B-v3
- nvidia/Llama3-ChatQA-1.5-8B
- Kukedlc/SmartLlama-3-8B-MS-v0.1
license: other
---

# NeuralMiLLaMa-8B-slerp

NeuralMiLLaMa-8B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)
* [elinas/Llama-3-8B-Ultra-Instruct](https://huggingface.co/elinas/Llama-3-8B-Ultra-Instruct)
* [mlabonne/ChimeraLlama-3-8B-v3](https://huggingface.co/mlabonne/ChimeraLlama-3-8B-v3)
* [nvidia/Llama3-ChatQA-1.5-8B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B)
* [Kukedlc/SmartLlama-3-8B-MS-v0.1](https://huggingface.co/Kukedlc/SmartLlama-3-8B-MS-v0.1)

## 🧩 Configuration

```yaml
models:
  - model: NousResearch/Meta-Llama-3-8B
    # No parameters necessary for base model
  - model: NousResearch/Meta-Llama-3-8B-Instruct
    parameters:
      density: 0.6
      weight: 0.4
  - model: elinas/Llama-3-8B-Ultra-Instruct
    parameters:
      density: 0.55
      weight: 0.1
  - model: mlabonne/ChimeraLlama-3-8B-v3
    parameters:
      density: 0.55
      weight: 0.2
  - model: nvidia/Llama3-ChatQA-1.5-8B
    parameters:
      density: 0.55
      weight: 0.2
  - model: Kukedlc/SmartLlama-3-8B-MS-v0.1
    parameters:
      density: 0.55
      weight: 0.1
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
  int8_mask: true
dtype: float16

```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/NeuralMiLLaMa-8B-slerp"
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"])
```