---
license: apache-2.0
tags:
- moe
- mergekit
---

# Mixolar-4x7b

This model is a Mixure of Experts (MoE) made with [mergekit](https://github.com/cg123/mergekit) (mixtral branch). It uses the following base models:
 * [kyujinpy/Sakura-SOLAR-Instruct](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct)
 * [jeonsworld/CarbonVillain-en-10.7B-v1](https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v1)
 * [rishiraj/meow](https://huggingface.co/rishiraj/meow)
 * [kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2](https://huggingface.co/kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2)

## 🧩 Configuration

```yaml
base_model: kyujinpy/Sakura-SOLAR-Instruct
gate_mode: hidden
experts:
  - source_model: kyujinpy/Sakura-SOLAR-Instruct
    positive_prompts:
    - "chat"
    - "assistant"
    - "tell me"
    - "explain"
    negative_prompts:
    - "mathematics"
    - "reasoning"
  - source_model: jeonsworld/CarbonVillain-en-10.7B-v1
    positive_prompts:
    - "write"
    - "AI"
    - "text"
    - "paragraph"
    negative_prompts:
    - "mathematics"
    - "reasoning"
  - source_model: rishiraj/meow
    positive_prompts:
    - "chat"
    - "say"
    - "what"
    negative_prompts:
    - "mathematics"
    - "reasoning"
  - source_model: kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2
    positive_prompts:
    - "reason"
    - "math"
    - "mathematics"
    - "solve"
    - "count"
    negative_prompts:
    - "chat"
    - "assistant"
    - "storywriting"
```

## 💻 Usage

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Mixolar-4x7b"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])
```