README.md

---
language:
- en
- hi
- de
- fr
- ar
- ja
license: apache-2.0
tags:
- moe
model-index:
- name: MetaModel_moe_multilingualv1
  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: 67.58
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/MetaModel_moe_multilingualv1
      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: 84.72
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/MetaModel_moe_multilingualv1
      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: 63.77
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/MetaModel_moe_multilingualv1
      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: 61.21
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/MetaModel_moe_multilingualv1
      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: 77.35
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/MetaModel_moe_multilingualv1
      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: 61.33
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/MetaModel_moe_multilingualv1
      name: Open LLM Leaderboard
---

# MetaModel_moe_multilingualv1

This model is a Mixure of Experts (MoE) made with [mergekit](https://github.com/cg123/mergekit) (mixtral branch). It uses the following base models:
* [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210)
* [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B)
* [maywell/PiVoT-0.1-Starling-LM-RP](https://huggingface.co/maywell/PiVoT-0.1-Starling-LM-RP)
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
* [davidkim205/komt-mistral-7b-v1](https://huggingface.co/davidkim205/komt-mistral-7b-v1)
* [OpenBuddy/openbuddy-zephyr-7b-v14.1](https://huggingface.co/OpenBuddy/openbuddy-zephyr-7b-v14.1)
* [manishiitg/open-aditi-hi-v1](https://huggingface.co/manishiitg/open-aditi-hi-v1)
* [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)

## 🧩 Configuration

```yaml
base_model: mlabonne/Marcoro14-7B-slerp
dtype: bfloat16
experts:
- positive_prompts:
  - chat
  - assistant
  - tell me
  - explain
  source_model: openchat/openchat-3.5-1210
- positive_prompts:
  - code
  - python
  - javascript
  - programming
  - algorithm
  source_model: beowolx/CodeNinja-1.0-OpenChat-7B
- positive_prompts:
  - storywriting
  - write
  - scene
  - story
  - character
  source_model: maywell/PiVoT-0.1-Starling-LM-RP
- positive_prompts:
  - reason
  - math
  - mathematics
  - solve
  - count
  source_model: WizardLM/WizardMath-7B-V1.1
- positive_prompts:
  - korean
  - answer in korean
  - korea
  source_model: davidkim205/komt-mistral-7b-v1
- positive_prompts:
  - chinese
  - china
  - answer in chinese
  source_model: OpenBuddy/openbuddy-zephyr-7b-v14.1
- positive_prompts:
  - hindi
  - india
  - hindu
  - answer in hindi
  source_model: manishiitg/open-aditi-hi-v1
- positive_prompts:
  - german
  - germany
  - answer in german
  - deutsch
  source_model: VAGOsolutions/SauerkrautLM-7b-v1-mistral
gate_mode: hidden
```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "gagan3012/MetaModel_moe_multilingualv1"

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_multilingualv1)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |69.33|
|AI2 Reasoning Challenge (25-Shot)|67.58|
|HellaSwag (10-Shot)              |84.72|
|MMLU (5-Shot)                    |63.77|
|TruthfulQA (0-shot)              |61.21|
|Winogrande (5-shot)              |77.35|
|GSM8k (5-shot)                   |61.33|