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---
tags:
- merge
- mergekit
- lazymergekit
- HuggingFaceH4/mistral-7b-grok
- senseable/WestLake-7B-v2
base_model:
- HuggingFaceH4/mistral-7b-grok
- senseable/WestLake-7B-v2
---
# westral
westral is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [HuggingFaceH4/mistral-7b-grok](https://huggingface.co/HuggingFaceH4/mistral-7b-grok)
* [senseable/WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: HuggingFaceH4/mistral-7b-grok
layer_range: [0, 32]
- model: senseable/WestLake-7B-v2
layer_range: [0, 32]
merge_method: slerp
base_model: HuggingFaceH4/mistral-7b-grok
parameters:
t:
- filter: lm_head
value: [0.75]
- filter: embed_tokens
value: [0.75]
- filter: self_attn
value: [0.75,0.25]
- filter: mlp
value: [0.25,0.75]
- filter: layernorm
value: [0.5,0.5]
- filter: modelnorm
value: [0.75]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kiruthikarthi/westral"
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"])
``` |