transformers safetensors mixtral text-generation moe merge mergekit lazymergekit ise-uiuc/Magicoder-S-CL-7B WizardLM/WizardCoder-Python-7B-V1.0 license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
tags:
- moe
- merge
- mergekit
- lazymergekit
- ise-uiuc/Magicoder-S-CL-7B
- WizardLM/WizardCoder-Python-7B-V1.0
---

# Magician-MoE-2x7B

Magician-MoE-2x7B is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [ise-uiuc/Magicoder-S-CL-7B](https://huggingface.co/ise-uiuc/Magicoder-S-CL-7B)
* [WizardLM/WizardCoder-Python-7B-V1.0](https://huggingface.co/WizardLM/WizardCoder-Python-7B-V1.0)

## 🧩 Configuration

```yaml
base_model: ise-uiuc/Magicoder-S-CL-7B
gate_mode: cheap_embed
experts:
  - source_model: ise-uiuc/Magicoder-S-CL-7B
    positive_prompts: ["You are an AI programmer","programming","C++ expert"]
  - source_model: WizardLM/WizardCoder-Python-7B-V1.0
    positive_prompts: ["Great at Deep learning","Algorithm and Data Structure","Python expert"]
```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "FelixChao/Magician-MoE-2x7B"

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"])
```