---
license: apache-2.0
tags:
- moe
- mixtral
---

# MetaModel_moe

This model is a Mixure of Experts (MoE) made with [mergekit](https://github.com/cg123/mergekit) (mixtral branch). It uses the following base models:
* [gagan3012/MetaModel](https://huggingface.co/gagan3012/MetaModel)
* [jeonsworld/CarbonVillain-en-10.7B-v2](https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v2)
* [jeonsworld/CarbonVillain-en-10.7B-v4](https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v4)
* [TomGrc/FusionNet_linear](https://huggingface.co/TomGrc/FusionNet_linear)

## 🧩 Configuration

```yaml
base_model: gagan3012/MetaModel
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: gagan3012/MetaModel
- source_model: jeonsworld/CarbonVillain-en-10.7B-v2
- source_model: jeonsworld/CarbonVillain-en-10.7B-v4
- source_model: TomGrc/FusionNet_linear
```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "gagan3012/MetaModel_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"])
```
# [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_gagan3012__MetaModel_moe)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 74.42   |
| ARC (25-shot)         | 71.25          |
| HellaSwag (10-shot)   | 88.4    |
| MMLU (5-shot)         | 66.26         |
| TruthfulQA (0-shot)   | 71.86   |
| Winogrande (5-shot)   | 83.35   |
| GSM8K (5-shot)        | 65.43        |