---
license: apache-2.0
tags:
- moe
- mergekit
- merge
- chinese
- arabic
- english
- multilingual
- german
- french
- gagan3012/MetaModel
- jeonsworld/CarbonVillain-en-10.7B-v2
- jeonsworld/CarbonVillain-en-10.7B-v4
- TomGrc/FusionNet_linear
- DopeorNope/SOLARC-M-10.7B
- VAGOsolutions/SauerkrautLM-SOLAR-Instruct
- upstage/SOLAR-10.7B-Instruct-v1.0
- fblgit/UNA-SOLAR-10.7B-Instruct-v1.0
model-index:
- name: MetaModel_moex8
  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: 71.16
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Xenon1/MetaModel_moex8
      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: 88.38
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Xenon1/MetaModel_moex8
      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: 66.29
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Xenon1/MetaModel_moex8
      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: 71.91
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Xenon1/MetaModel_moex8
      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: 83.27
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Xenon1/MetaModel_moex8
      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: 65.35
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Xenon1/MetaModel_moex8
      name: Open LLM Leaderboard
---

# MetaModel_moex8

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)
* [DopeorNope/SOLARC-M-10.7B](https://huggingface.co/DopeorNope/SOLARC-M-10.7B)
* [VAGOsolutions/SauerkrautLM-SOLAR-Instruct](https://huggingface.co/VAGOsolutions/SauerkrautLM-SOLAR-Instruct)
* [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
* [fblgit/UNA-SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/fblgit/UNA-SOLAR-10.7B-Instruct-v1.0)

## 🧩 Configuration

```yamlbase_model: jeonsworld/CarbonVillain-en-10.7B-v4
dtype: bfloat16
experts:
- positive_prompts:
  - ''
  source_model: gagan3012/MetaModel
- positive_prompts:
  - ''
  source_model: jeonsworld/CarbonVillain-en-10.7B-v2
- positive_prompts:
  - ''
  source_model: jeonsworld/CarbonVillain-en-10.7B-v4
- positive_prompts:
  - ''
  source_model: TomGrc/FusionNet_linear
- positive_prompts:
  - ''
  source_model: DopeorNope/SOLARC-M-10.7B
- positive_prompts:
  - ''
  source_model: VAGOsolutions/SauerkrautLM-SOLAR-Instruct
- positive_prompts:
  - ''
  source_model: upstage/SOLAR-10.7B-Instruct-v1.0
- positive_prompts:
  - ''
  source_model: fblgit/UNA-SOLAR-10.7B-Instruct-v1.0
gate_mode: hidden
```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "gagan3012/MetaModel_moex8"

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_Xenon1__MetaModel_moex8)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |74.39|
|AI2 Reasoning Challenge (25-Shot)|71.16|
|HellaSwag (10-Shot)              |88.38|
|MMLU (5-Shot)                    |66.29|
|TruthfulQA (0-shot)              |71.91|
|Winogrande (5-shot)              |83.27|
|GSM8k (5-shot)                   |65.35|