---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- mlabonne/AlphaMonarch-7B
- Kukedlc/NeuralMaxime-7B-slerp
- bardsai/jaskier-7b-dpo-v5.6
base_model:
- mlabonne/AlphaMonarch-7B
- Kukedlc/NeuralMaxime-7B-slerp
- bardsai/jaskier-7b-dpo-v5.6
model-index:
- name: MonaTrix-v6
  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: 72.78
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CultriX/MonaTrix-v6
      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: 88.9
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CultriX/MonaTrix-v6
      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: 64.45
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CultriX/MonaTrix-v6
      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: 77.45
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CultriX/MonaTrix-v6
      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: 84.61
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CultriX/MonaTrix-v6
      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: 67.85
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CultriX/MonaTrix-v6
      name: Open LLM Leaderboard
---

# MonaTrix-v6

MonaTrix-v6 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
* [Kukedlc/NeuralMaxime-7B-slerp](https://huggingface.co/Kukedlc/NeuralMaxime-7B-slerp)
* [bardsai/jaskier-7b-dpo-v5.6](https://huggingface.co/bardsai/jaskier-7b-dpo-v5.6)

## 🧩 Configuration

```yaml
models:
  - model: eren23/dpo-binarized-NeutrixOmnibe-7B
    # No parameters necessary for base model
  - model: mlabonne/AlphaMonarch-7B
    #Emphasize the beginning of Vicuna format models
    parameters:
      weight: 0.6
      density: 0.59
  - model: Kukedlc/NeuralMaxime-7B-slerp
    parameters:
      weight: 0.1
      density: 0.55
  # Vicuna format
  - model: bardsai/jaskier-7b-dpo-v5.6
    parameters:
      weight: 0.3
      density: 0.55

merge_method: dare_ties
base_model: eren23/dpo-binarized-NeutrixOmnibe-7B
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "CultriX/MonaTrix-v6"
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_CultriX__MonaTrix-v6)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |76.01|
|AI2 Reasoning Challenge (25-Shot)|72.78|
|HellaSwag (10-Shot)              |88.90|
|MMLU (5-Shot)                    |64.45|
|TruthfulQA (0-shot)              |77.45|
|Winogrande (5-shot)              |84.61|
|GSM8k (5-shot)                   |67.85|