---
tags:
- merge
- mergekit
- lazymergekit
- SciPhi/SciPhi-Mistral-7B-32k
base_model:
- SciPhi/SciPhi-Mistral-7B-32k
---

# SciPhi-Mistral-7B-32k-sliced

SciPhi-Mistral-7B-32k-sliced is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [SciPhi/SciPhi-Mistral-7B-32k](https://huggingface.co/SciPhi/SciPhi-Mistral-7B-32k)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: SciPhi/SciPhi-Mistral-7B-32k
        layer_range: [0, 0]
  - sources:
      - model: SciPhi/SciPhi-Mistral-7B-32k
        layer_range: [1, 1]
  - sources:
      - model: SciPhi/SciPhi-Mistral-7B-32k
        layer_range: [3, 3]
  - sources:
      - model: SciPhi/SciPhi-Mistral-7B-32k
        layer_range: [5, 5]
  - sources:
      - model: SciPhi/SciPhi-Mistral-7B-32k
        layer_range: [6, 6]
  - sources:
      - model: SciPhi/SciPhi-Mistral-7B-32k
        layer_range: [10, 10]
  - sources:
      - model: SciPhi/SciPhi-Mistral-7B-32k
        layer_range: [17, 17]
  - sources:
      - model: SciPhi/SciPhi-Mistral-7B-32k
        layer_range: [18, 18]
  - sources:
      - model: SciPhi/SciPhi-Mistral-7B-32k
        layer_range: [19, 19]
  - sources:
      - model: SciPhi/SciPhi-Mistral-7B-32k
        layer_range: [20, 20]
  - sources:
      - model: SciPhi/SciPhi-Mistral-7B-32k
        layer_range: [23, 23]
  - sources:
      - model: SciPhi/SciPhi-Mistral-7B-32k
        layer_range: [32, 32]
merge_method: slerp
base_model: Locutusque/TinyMistral-248M-v2.5-Instruct
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: 0.3
dtype: float16
tokenizer_source: base

```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jtatman/SciPhi-Mistral-7B-32k-sliced"
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"])
```