---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- gordicaleksa/YugoGPT
- mlabonne/AlphaMonarch-7B
model-index:
- name: Tito-7B-slerp
  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: 68.09
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Stopwolf/Tito-7B-slerp
      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: 86.38
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Stopwolf/Tito-7B-slerp
      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: 64.01
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Stopwolf/Tito-7B-slerp
      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: 57.01
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Stopwolf/Tito-7B-slerp
      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: 81.69
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Stopwolf/Tito-7B-slerp
      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: 63.61
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Stopwolf/Tito-7B-slerp
      name: Open LLM Leaderboard
---

# Tito-7B-slerp

Tito-7B-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [gordicaleksa/YugoGPT](https://huggingface.co/gordicaleksa/YugoGPT)
* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: gordicaleksa/YugoGPT
        layer_range: [0, 32]
      - model: mlabonne/AlphaMonarch-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/AlphaMonarch-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.6
dtype: bfloat16
```

## Results

Evaluations on Serbian LLM eval suite (or rather, performance and knowledge of Serbian):
|           | ARC-E | ARC-C | Hellaswag | BoolQ | Winogrande | OpenbookQA | PiQA  | NQ Open | TriviaQA | Avg.  |
|-----------|-------|-------|-----------|-------|------------|------------|-------|---------|----------|-------|
| [Zamfir-7B](https://huggingface.co/Stopwolf/Zamfir-7B-slerp) | 51.85 | 32.25 | 46.03     | 75.59 | 62.59      | 26.00      | 66.81 | 16.09   | 36.11    | 45.92 |
| [Mustra-7B](https://huggingface.co/Stopwolf/Mustra-7B-Instruct-v0.1) | 52.95 | 33.70 | 45.89     | **77.55** | 64.17      | **30.60**      | 67.25 | 15.40   | 34.84    | 46.93 |
| [Tito-7B](https://huggingface.co/Stopwolf/Tito-7B-slerp)   | 55.43 | **34.73** | 48.19     | 77.37 | **65.27**      | 30.00      | 67.30 | **16.7**    | 35.38    | **47.82** |
| [YugoGPT](https://huggingface.co/gordicaleksa/YugoGPT)   | **57.79** | **34.73** | **49.89**     | 69.45 | 64.56      | 28.20      | **72.03** | 15.82   | **36.14**    | 47.62 |

Here, all benchmarks were done 0-shot, on the exception of NQ Open and TriviaQA which were done in 5-shot manner, in order to be comparable to Mistral paper.

If we try to replicate OpenLLM Leaderboard results on available Serbian datasets (running an appropriate amount of shots instead of 0), we get:
|         | ARC   | Hellaswag | Winogrande | TruthfulQA | Avg.  |
|---------|-------|-----------|------------|------------|-------|
| Tito-7B | 47.27 |     -     |   69.93    |  **57.48** | 58.23 |
| [Perucac-7B](https://huggingface.co/Stopwolf/Perucac-7B-slerp)  | **49.74** |   -   | **71.98** | 56.03 | **59.25** |
| YugoGPT | 44.03 |     -     |   70.64    |    48.06   | 54.24 |
| Llama3-8B | 42.24 |    -    |   61.25    |    51.08   | 51.52 |
| SambaLingo | 37.88 |   -    |   61.48    |    47.23   | 48.86 |

Note that YugoGPT, Llama3 and SambaLingo are all base models, unlike Tito and Perucac.

# [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_Stopwolf__Tito-7B-slerp)

|             Metric              |Tito | YugoGPT |
|---------------------------------|----:|--------:|
|Avg.                             |70.13|  57.34  |
|AI2 Reasoning Challenge (25-Shot)|68.09|  58.10  |
|HellaSwag (10-Shot)              |86.38|  81.44  |
|MMLU (5-Shot)                    |64.01|  60.68  |
|TruthfulQA (0-shot)              |57.01|  36.60  |
|Winogrande (5-shot)              |81.69|  76.56  |
|GSM8k (5-shot)                   |63.61|  30.70  |