---
tags:
- merge
- mergekit
license: cc-by-nc-4.0
language:
- id
- en
---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/642b04e4ecec03b44649e318/Z4a8op_F7GOyd4b7RkRn6.png)

# MiaLatte-Indo-Mistral-7b
MiaLatte is a derivative model of [OpenMia](https://huggingface.co/indischepartij/OpenMia-Indo-Mistral-7b-v2), which is able to answer everyday questions specifically in Bahasa Indonesia (Indonesia Language).

some of GGUF: https://huggingface.co/indischepartij/MiaLatte-Indo-Mistral-7b-GGUF

# Examples


![image/png](https://cdn-uploads.huggingface.co/production/uploads/642b04e4ecec03b44649e318/L-ZbAtMnhMHC6P1r5JMTI.png)
![image/png](https://cdn-uploads.huggingface.co/production/uploads/642b04e4ecec03b44649e318/YF3ueo-OFis7PcGwkKwb1.png)
![image/png](https://cdn-uploads.huggingface.co/production/uploads/642b04e4ecec03b44649e318/G26MN5W0TcKkLOj9tbSqW.png)



MiaLatte-Indo-Mistral-7b is a merge of the following models using MergeKit:
* [indischepartij/OpenMia-Indo-Mistral-7b-v2](https://huggingface.co/indischepartij/OpenMia-Indo-Mistral-7b-v2)
* [Obrolin/Kesehatan-7B-v0.1](https://huggingface.co/Obrolin/Kesehatan-7B-v0.1)
* [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2)

## 🪄 Open LLM Benchmark

![image/png](https://cdn-uploads.huggingface.co/production/uploads/642b04e4ecec03b44649e318/Akt0cmoFQtz-H8NX6j96D.png)

## 🧩 Configuration

```yaml
slices:
models:
  - model: indischepartij/OpenMia-Indo-Mistral-7b-v2
    parameters:
      density: 0.50
      weight: 0.35
  - model: Obrolin/Kesehatan-7B-v0.1
    parameters:
      density: 0.50
      weight: 0.35
  - model: FelixChao/WestSeverus-7B-DPO-v2
    parameters:
      density: 0.50
      weight: 0.30
merge_method: dare_ties
base_model: indischepartij/OpenMia-Indo-Mistral-7b-v2
parameters:
  int8_mask: true
dtype: float16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "indischepartij/MiaLatte-Indo-Mistral-7b"
messages = [{"role": "user", "content": "Apa jenis skincare yang cocok untuk kulit berjerawat??"}]

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"])
```