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---
license: apache-2.0
tags:
- moe
- mixtral
model-index:
- name: MetaModel_moe
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: 71.25
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/MetaModel_moe
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: 88.4
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/MetaModel_moe
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: 66.26
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/MetaModel_moe
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: 71.86
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/MetaModel_moe
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: 83.35
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/MetaModel_moe
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: 65.43
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/MetaModel_moe
name: Open LLM Leaderboard
---
# 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 |
# [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|
|AI2 Reasoning Challenge (25-Shot)|71.25|
|HellaSwag (10-Shot) |88.40|
|MMLU (5-Shot) |66.26|
|TruthfulQA (0-shot) |71.86|
|Winogrande (5-shot) |83.35|
|GSM8k (5-shot) |65.43|
|