README.md

---
license: apache-2.0
tags:
- mergekit
- merge
base_model: []
model-index:
- name: supermario_v4
  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: 73.46
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vanillaOVO/supermario_v4
      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: 88.77
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vanillaOVO/supermario_v4
      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: 65.41
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vanillaOVO/supermario_v4
      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: 72.07
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vanillaOVO/supermario_v4
      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: 85.24
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vanillaOVO/supermario_v4
      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: 70.13
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vanillaOVO/supermario_v4
      name: Open LLM Leaderboard
---
This is a merge of pre-trained language models created based on DARE using [mergekit](https://github.com/cg123/mergekit).

More descriptions of the model will be added soon.

### **Loading the Model**

Use the following Python code to load the model:

```python
import torch
from transformers import MistralForCausalLM, AutoTokenizer

model = MistralForCausalLM.from_pretrained("vanillaOVO/supermario_v4", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("vanillaOVO/supermario_v4")
```

### **Generating Text**

To generate text, use the following Python code:

```python
text = "Large language models are "
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

# [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_vanillaOVO__supermario_v4)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |75.85|
|AI2 Reasoning Challenge (25-Shot)|73.46|
|HellaSwag (10-Shot)              |88.77|
|MMLU (5-Shot)                    |65.41|
|TruthfulQA (0-shot)              |72.07|
|Winogrande (5-shot)              |85.24|
|GSM8k (5-shot)                   |70.13|