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---
base_model:
- migtissera/Tess-3-Llama-3.1-70B
- HODACHI/Llama-3.1-70B-EZO-1.1-it
- shenzhi-wang/Llama3.1-70B-Chinese-Chat
- Saxo/Linkbricks-Horizon-AI-Korean-llama3.1-sft-dpo-70B
tags:
- merge
- mergekit
- lazymergekit
- migtissera/Tess-3-Llama-3.1-70B
- HODACHI/Llama-3.1-70B-EZO-1.1-it
- shenzhi-wang/Llama3.1-70B-Chinese-Chat
- Saxo/Linkbricks-Horizon-AI-Korean-llama3.1-sft-dpo-70B
---
# L3.1-70b-MeowMixV2
Meow.
L3.1-70b-MeowMixV2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing) running on Runpod:
* [migtissera/Tess-3-Llama-3.1-70B](https://huggingface.co/migtissera/Tess-3-Llama-3.1-70B)
* [HODACHI/Llama-3.1-70B-EZO-1.1-it](https://huggingface.co/HODACHI/Llama-3.1-70B-EZO-1.1-it)
* [shenzhi-wang/Llama3.1-70B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3.1-70B-Chinese-Chat)
* [Saxo/Linkbricks-Horizon-AI-Korean-llama3.1-sft-dpo-70B](https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-llama3.1-sft-dpo-70B)
## Yap / Chat Format
Llama 3 Instruct.
## 🧩 Configuration
```yaml
models:
- model: migtissera/Tess-3-Llama-3.1-70B
parameters:
density: 0.7
weight:
- value: 0.75
- model: HODACHI/Llama-3.1-70B-EZO-1.1-it
parameters:
density: 0.2
weight:
- value: [1, 0.75, 0.5, 0.25, 0, 0, 0, 0, 0.0, 0.5, 1]
- model: shenzhi-wang/Llama3.1-70B-Chinese-Chat
parameters:
density: 0.2
weight:
- value: [1, 0.75, 0.5, 0.25, 0, 0, 0, 0, 0.0, 0.5, 1]
- model: Saxo/Linkbricks-Horizon-AI-Korean-llama3.1-sft-dpo-70B
parameters:
density: 0.2
weight:
- value: [1, 0.75, 0.5, 0.25, 0, 0, 0, 0, 0.0, 0.5, 1]
merge_method: della_linear
base_model: migtissera/Tess-3-Llama-3.1-70B
parameters:
normalize: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "KaraKaraWitch/L3.1-70b-MeowMixV2"
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"])
``` |