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base_model:
- Weyaxi/MetaMath-OpenHermes-2.5-neural-chat-7b-v3-1-7B-Linear
- icefog72/IceMoonshineRP-7b
- Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp
- VAGOsolutions/SauerkrautLM-7b-HerO
- mrfakename/NeuralOrca-7B-v1
tags:
- merge
- mergekit
- lazymergekit
- Weyaxi/MetaMath-OpenHermes-2.5-neural-chat-7b-v3-1-7B-Linear
- icefog72/IceMoonshineRP-7b
- Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp
- VAGOsolutions/SauerkrautLM-7b-HerO
- mrfakename/NeuralOrca-7B-v1
---
# kangaroo_7B_test01
kangaroo_7B_test01 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Weyaxi/MetaMath-OpenHermes-2.5-neural-chat-7b-v3-1-7B-Linear](https://huggingface.co/Weyaxi/MetaMath-OpenHermes-2.5-neural-chat-7b-v3-1-7B-Linear)
* [icefog72/IceMoonshineRP-7b](https://huggingface.co/icefog72/IceMoonshineRP-7b)
* [Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp](https://huggingface.co/Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp)
* [VAGOsolutions/SauerkrautLM-7b-HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO)
* [mrfakename/NeuralOrca-7B-v1](https://huggingface.co/mrfakename/NeuralOrca-7B-v1)
## 🧩 Configuration
```yaml
models:
- model: BioMistral/BioMistral-7B-DARE
# No parameters necessary for base model
- model: Weyaxi/MetaMath-OpenHermes-2.5-neural-chat-7b-v3-1-7B-Linear
parameters:
density: 0.5
weight: 0.2
- model: icefog72/IceMoonshineRP-7b
parameters:
density: 0.5
weight: 0.2
- model: Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp
parameters:
density: 0.5
weight: 0.2
- model: VAGOsolutions/SauerkrautLM-7b-HerO
parameters:
density: 0.5
weight: 0.2
- model: mrfakename/NeuralOrca-7B-v1
parameters:
density: 0.5
weight: 0.2
merge_method: dare_ties
base_model: BioMistral/BioMistral-7B-DARE
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "kainatq/kangaroo_7B_test01"
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"])
``` |