transformers safetensors mixtral text-generation moe frankenmoe merge mergekit lazymergekit mistralai/Mistral-7B-v0.1 mlabonne/drmistral-7b base_model:mistralai/Mistral-7B-v0.1 base_model:mlabonne/drmistral-7b license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- mistralai/Mistral-7B-v0.1
- mlabonne/drmistral-7b
base_model:
- mistralai/Mistral-7B-v0.1
- mlabonne/drmistral-7b
---

# 2xMistral

2xMistral is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
* [mlabonne/drmistral-7b](https://huggingface.co/mlabonne/drmistral-7b)

## 🧩 Configuration

```yaml
base_model: mistralai/Mistral-7B-v0.1
gate_mode: cheap_embed
experts:
  - source_model: mistralai/Mistral-7B-v0.1
    positive_prompts: ["general"]
  - source_model: mlabonne/drmistral-7b
    positive_prompts: ["medical"]
```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "AbstractPerspective/2xMistral"

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"])
```