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---
tags:
- merge
- mergekit
- lazymergekit
- WizardLM/WizardMath-7B-V1.1
- AurelPx/Percival_01-7b-slerp
- Weyaxi/Einstein-v4-7B
- Kukedlc/NeuralMaths-Experiment-7b
- Gille/StrangeMerges_35-7B-slerp
base_model:
- WizardLM/WizardMath-7B-V1.1
- AurelPx/Percival_01-7b-slerp
- Weyaxi/Einstein-v4-7B
- Kukedlc/NeuralMaths-Experiment-7b
- Gille/StrangeMerges_35-7B-slerp
---
# StrangeMerges_52-7B-dare_ties
StrangeMerges_52-7B-dare_ties is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
* [AurelPx/Percival_01-7b-slerp](https://huggingface.co/AurelPx/Percival_01-7b-slerp)
* [Weyaxi/Einstein-v4-7B](https://huggingface.co/Weyaxi/Einstein-v4-7B)
* [Kukedlc/NeuralMaths-Experiment-7b](https://huggingface.co/Kukedlc/NeuralMaths-Experiment-7b)
* [Gille/StrangeMerges_35-7B-slerp](https://huggingface.co/Gille/StrangeMerges_35-7B-slerp)
## 🧩 Configuration
```yaml
models:
- model: Gille/StrangeMerges_51-7B-dare_ties
# No parameters necessary for base model
- model: WizardLM/WizardMath-7B-V1.1
parameters:
density: 0.66
weight: 0.2
- model: AurelPx/Percival_01-7b-slerp
parameters:
density: 0.55
weight: 0.2
- model: Weyaxi/Einstein-v4-7B
parameters:
density: 0.55
weight: 0.2
- model: Kukedlc/NeuralMaths-Experiment-7b
parameters:
density: 0.44
weight: 0.2
- model: Gille/StrangeMerges_35-7B-slerp
parameters:
density: 0.66
weight: 0.2
merge_method: dare_ties
base_model: Gille/StrangeMerges_51-7B-dare_ties
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Gille/StrangeMerges_52-7B-dare_ties"
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"])
``` |