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---
tags:
- merge
- mergekit
- lazymergekit
- SciPhi/SciPhi-Mistral-7B-32k
- SciPhi/SciPhi-Mistral-7B-32k
base_model:
- SciPhi/SciPhi-Mistral-7B-32k
- SciPhi/SciPhi-Mistral-7B-32k
---
# SciPhi-Mistral-7B-32k-sliced
This is purely an experiment in sliced layer extraction to find active layers.
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)
* [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, 6]
- model: SciPhi/SciPhi-Mistral-7B-32k
layer_range: [26, 32]
merge_method: slerp
base_model: SciPhi/SciPhi-Mistral-7B-32k
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 # fallback for rest of tensors
tokenizer_source: union
dtype: float16
```
## 💻 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"])
``` |