README.md

---
license: cc-by-nc-4.0
tags:
- merge
- mergekit
base_model:
- seyf1elislam/WestKunai-Hermes-7b
- seyf1elislam/WestKunai-Hermes-7b
- seyf1elislam/WestKunai-Hermes-7b
- seyf1elislam/WestKunai-Hermes-7b
- seyf1elislam/WestKunai-Hermes-7b
---

# WestKunai-Hermes-10.7b-test

Replicate the configuration utilized in the [froggeric/WestLake-10.7B-v2](https://huggingface.co/froggeric/WestLake-10.7B-v2/) model to extend the [WestKunai-Hermes-7b](https://huggingface.co/seyf1elislam/WestKunai-Hermes-7b) model to 10.7b.

## Merge Details


### Models Merged
The following models were included in the merge:
* [seyf1elislam/WestKunai-Hermes-7b](https://huggingface.co/seyf1elislam/WestKunai-Hermes-7b)
* [seyf1elislam/WestKunai-Hermes-7b](https://huggingface.co/seyf1elislam/WestKunai-Hermes-7b)
* [seyf1elislam/WestKunai-Hermes-7b](https://huggingface.co/seyf1elislam/WestKunai-Hermes-7b)
* [seyf1elislam/WestKunai-Hermes-7b](https://huggingface.co/seyf1elislam/WestKunai-Hermes-7b)
* [seyf1elislam/WestKunai-Hermes-7b](https://huggingface.co/seyf1elislam/WestKunai-Hermes-7b)

##  Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
  - sources:
    - model: seyf1elislam/WestKunai-Hermes-7b
      layer_range: [0,9]
  - sources:
    - model: seyf1elislam/WestKunai-Hermes-7b
      layer_range: [5,14]
  - sources:
    - model: seyf1elislam/WestKunai-Hermes-7b
      layer_range: [10,19]
  - sources:
    - model: seyf1elislam/WestKunai-Hermes-7b
      layer_range: [15,24]
  - sources:
    - model: seyf1elislam/WestKunai-Hermes-7b
      layer_range: [20,32]
merge_method: passthrough
dtype: bfloat16
```
## Usage Example

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "seyf1elislam/WestKunai-Hermes-10.7b-test"
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"])
```