README.md

---
license: apache-2.0
tags:
- merge
- mergekit
- '#dpo'
- MaximeLabonne
- '#mergeofmerge'
base_model:
- CultriX/NeuralTrix-7B-dpo
- paulml/OmniBeagleSquaredMBX-v3-7B-v2
model-index:
- name: NeuTrixOmniBe-DPO
  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=Kukedlc/NeuTrixOmniBe-DPO
      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: 89.03
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuTrixOmniBe-DPO
      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: 64.28
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuTrixOmniBe-DPO
      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: 77.21
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuTrixOmniBe-DPO
      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: 85.16
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuTrixOmniBe-DPO
      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: 68.54
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuTrixOmniBe-DPO
      name: Open LLM Leaderboard
---
# This model was merged, trained, and so on, thanks to the knowledge I gained from reading Maxime Labonne's course. Special thanks to him! 
[Labonne LLM Course](https://github.com/mlabonne/llm-course)

![NeuTrixOmniBe](https://raw.githubusercontent.com/kukedlc87/imagenes/main/DALL%C2%B7E%202023-12-29%2002.13.09%20-%20A%20robot%20with%20a%20unique%20design%20where%20its%20face%20is%20a%20screen%20displaying%20binary%20code.%20The%20robot's%20body%20is%20sleek%20and%20modern%2C%20with%20a%20metallic%20finish%20that%20refl.png)

# NeuTrixOmniBe-DPO

NeuTrixOmniBe-DPO is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):

## 🧩 Configuration

```yaml
MODEL_NAME = "NeuTrixOmniBe-DPO"
yaml_config = """
slices:
  - sources:
      - model: CultriX/NeuralTrix-7B-dpo
        layer_range: [0, 32]
      - model: paulml/OmniBeagleSquaredMBX-v3-7B-v2
        layer_range: [0, 32]
merge_method: slerp
base_model: CultriX/NeuralTrix-7B-dpo
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: 0.5
dtype: bfloat16
"""
```

It was then trained with DPO using: 
* Intel/orca_dpo_pairs





## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/NeuTrixOmniBe-DPO"
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=128, do_sample=True, temperature=0.5, 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_Kukedlc__NeuTrixOmniBe-DPO)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |76.17|
|AI2 Reasoning Challenge (25-Shot)|72.78|
|HellaSwag (10-Shot)              |89.03|
|MMLU (5-Shot)                    |64.28|
|TruthfulQA (0-shot)              |77.21|
|Winogrande (5-shot)              |85.16|
|GSM8k (5-shot)                   |68.54|