
# MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.2-slerp

MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [Weyaxi/MetaMath-Chupacabra-7B-v2.01-Slerp](https://huggingface.co/Weyaxi/MetaMath-Chupacabra-7B-v2.01-Slerp)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: mistralai/Mistral-7B-Instruct-v0.2
        layer_range: [0, 32]
      - model: Weyaxi/MetaMath-Chupacabra-7B-v2.01-Slerp
        layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16
```


## 💻 Usage


```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.2-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"])
```
{"license": "apache-2.0", "tags": ["merge", "mergekit", "mistral", "7b", "lazymergekit", "mistralai/Mistral-7B-Instruct-v0.2", "Weyaxi/MetaMath-Chupacabra-7B-v2.01-Slerp"]}
transformers safetensors mistral text-generation merge mergekit 7b lazymergekit mistralai/Mistral-7B-Instruct-v0.2 Weyaxi/MetaMath-Chupacabra-7B-v2.01-Slerp conversational license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us