---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- unsloth/mistral-7b-v0.2
- mistralai/Mistral-7B-Instruct-v0.2
base_model:
- unsloth/mistral-7b-v0.2
- mistralai/Mistral-7B-Instruct-v0.2
---


![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/645cfe4603fc86c46b3e46d1/DOoAs2yzNOUC465BSM9-s.jpeg)

# Mini-Mixtral-v0.2

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

## 🧩 Configuration

```yaml

base_model: unsloth/mistral-7b-v0.2
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: unsloth/mistral-7b-v0.2
    positive_prompts:
      - "Answer this question from the ARC (Argument Reasoning Comprehension)."
      - "Use common sense and logical reasoning skills."
    negative_prompts:
      - "nonsense"
      - "irrational"
      - "math"
      - "code"
  - source_model: mistralai/Mistral-7B-Instruct-v0.2
    positive_prompts:
      - "Calculate the answer to this math problem"
      - "My mathematical capabilities are strong, allowing me to handle complex mathematical queries"
      - "solve for"
    negative_prompts:
      - "incorrect"
      - "inaccurate"
      - "creativity"
```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "NeuralNovel/Mini-Mixtral-v0.2"

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"])
```