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---
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"])
``` |