README.md

---
language:
- en
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- maywell/PiVoT-0.1-Evil-a
- mlabonne/NeuralOmniBeagle-7B-v2
- roleplay
- rp
- not-for-all-audiences
base_model:
- maywell/PiVoT-0.1-Evil-a
- mlabonne/NeuralOmniBeagle-7B-v2
model-index:
- name: Konstanta-7B
  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: 70.05
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/Konstanta-7B
      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: 87.5
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/Konstanta-7B
      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: 65.06
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/Konstanta-7B
      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: 65.43
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/Konstanta-7B
      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: 82.16
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/Konstanta-7B
      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: 71.04
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Inv/Konstanta-7B
      name: Open LLM Leaderboard
---

# Konstanta-7B

Konstanta-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [maywell/PiVoT-0.1-Evil-a](https://huggingface.co/maywell/PiVoT-0.1-Evil-a)
* [mlabonne/NeuralOmniBeagle-7B-v2](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B-v2)

This is a test merge that is supposed to improve Kunoichi by merging it with new Beagle model and PiVoT Evil, which both show good performance. Even though the model's name is in Russian, it is not really capable of properly using it, as it was not the main goal of the model.

## 🧩 Configuration

```yaml
merge_method: dare_ties
dtype: bfloat16
parameters:
  int8_mask: true
base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
models:
  - model: SanjiWatsuki/Kunoichi-DPO-v2-7B
  - model: maywell/PiVoT-0.1-Evil-a
    parameters:
      density: 0.65
      weight: 0.15
  - model: mlabonne/NeuralOmniBeagle-7B-v2
    parameters:
      density: 0.85
      weight: 0.45
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Inv/Konstanta-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

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_Inv__Konstanta-7B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |73.54|
|AI2 Reasoning Challenge (25-Shot)|70.05|
|HellaSwag (10-Shot)              |87.50|
|MMLU (5-Shot)                    |65.06|
|TruthfulQA (0-shot)              |65.43|
|Winogrande (5-shot)              |82.16|
|GSM8k (5-shot)                   |71.04|