README.md

---
license: apache-2.0
tags:
- moe
- merge
- deepseek-ai/deepseek-coder-6.7b-instruct
- ise-uiuc/Magicoder-S-CL-7B
- WizardLM/WizardMath-7B-V1.0
- WizardLM/WizardCoder-Python-7B-V1.0
model-index:
- name: Magician-MoE-4x7B
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 28.24
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FelixChao/Magician-MoE-4x7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 30.06
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FelixChao/Magician-MoE-4x7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 24.67
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FelixChao/Magician-MoE-4x7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 0.0
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FelixChao/Magician-MoE-4x7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 49.88
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FelixChao/Magician-MoE-4x7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 0.0
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FelixChao/Magician-MoE-4x7B
      name: Open LLM Leaderboard
---

# Magician-MoE-4x7B

Magician-MoE-4x7B is a Mixure of Experts (MoE) made with the following models:
* [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct)
* [ise-uiuc/Magicoder-S-CL-7B](https://huggingface.co/ise-uiuc/Magicoder-S-CL-7B)
* [WizardLM/WizardMath-7B-V1.0](https://huggingface.co/WizardLM/WizardMath-7B-V1.0)
* [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: deepseek-ai/deepseek-coder-6.7b-instruct
    positive_prompts: ["You are an AI coder","coding","Java expert"]
  - source_model: ise-uiuc/Magicoder-S-CL-7B
    positive_prompts: ["You are an AI programmer","programming","C++ expert"]
  - source_model: WizardLM/WizardMath-7B-V1.0
    positive_prompts: ["Math problem solving","Think step by step","Math 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-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"])
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__Magician-MoE-4x7B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |22.14|
|AI2 Reasoning Challenge (25-Shot)|28.24|
|HellaSwag (10-Shot)              |30.06|
|MMLU (5-Shot)                    |24.67|
|TruthfulQA (0-shot)              | 0.00|
|Winogrande (5-shot)              |49.88|
|GSM8k (5-shot)                   | 0.00|