---
license: apache-2.0
tags:
- moe
- merge
- mergekit
- HuggingFaceH4/zephyr-7b-beta
- mistralai/Mistral-7B-Instruct-v0.2
- teknium/OpenHermes-2.5-Mistral-7B
- meta-math/MetaMath-Mistral-7B
- Mistral
base_model:
- HuggingFaceH4/zephyr-7b-beta
- mistralai/Mistral-7B-Instruct-v0.2
- teknium/OpenHermes-2.5-Mistral-7B
- meta-math/MetaMath-Mistral-7B
---

# Boundary-mistral-4x7b-MoE

Boundary-mistral-4x7b-MoE is a Mixture of Experts (MoE) made with the following models:
* [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
* [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B)

## 🧩 Configuration

```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.2
dtype: float16
gate_mode: cheap_embed
experts:
  - source_model: HuggingFaceH4/zephyr-7b-beta
    positive_prompts: ["You are an helpful general-pupose assistant."]
  - source_model: mistralai/Mistral-7B-Instruct-v0.2
    positive_prompts: ["You are helpful assistant."]
  - source_model: teknium/OpenHermes-2.5-Mistral-7B
    positive_prompts: ["You are helpful a coding assistant."]
  - source_model: meta-math/MetaMath-Mistral-7B
    positive_prompts: ["You are an assistant good at math."]
```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "NotAiLOL/Boundary-mistral-4x7b-MoE"

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"])
```