README.md

---
tags:
- merge
- mergekit
- louisbrulenaudet/Pearl-7B-slerp
- WizardLM/WizardMath-7B-V1.1
- cognitivecomputations/WestLake-7B-v2-laser
- CultriX/NeuralTrix-7B-dpo
- chemistry
- biology
- math
- 4-bits
base_model:
- louisbrulenaudet/Pearl-7B-slerp
- WizardLM/WizardMath-7B-V1.1
- cognitivecomputations/WestLake-7B-v2-laser
- CultriX/NeuralTrix-7B-dpo
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: Pearl-7B-0211-ties
  results:
  - task:
      type: text-generation
    metrics:
    - name: Average
      type: Average
      value: 75.11
    - name: ARC
      type: ARC
      value: 71.42
    - name: GSM8K
      type: GSM8K
      value: 70.66
    - name: Winogrande
      type: Winogrande
      value: 84.37
    - name: TruthfulQA
      type: TruthfulQA
      value: 71.46
    - name: HellaSwag
      type: HellaSwag
      value: 88.86
    source:
      name: Open LLM Leaderboard
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
---
<center><img src='https://i.imgur.com/0xFTuAX.png' width='450px'></center>

# Pearl-7B-0211-ties, an xtraordinary 7B model

Pearl-7B-0211-ties is a merge of the following models:
* [louisbrulenaudet/Pearl-7B-slerp](https://huggingface.co/louisbrulenaudet/Pearl-7B-slerp)
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
* [cognitivecomputations/WestLake-7B-v2-laser](https://huggingface.co/cognitivecomputations/WestLake-7B-v2-laser)
* [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo)

## Evaluation

The evaluation was performed using the HuggingFace Open LLM Leaderboard.

| Model                                            | Average | ARC   | HellaSwag | MMLU  | TruthfulQA | Winogrande | GSM8K | #Params (B) |
|--------------------------------------------------|---------|-------|-----------|-------|------------|------------|-------|--------------|
| **louisbrulenaudet/Pearl-34B-ties**             | **75.48** | 70.99 | 84.83 | **76.63** | 70.32 | 82.64 | 67.48 | 34.39 |
| **louisbrulenaudet/Pearl-7B-0211-ties**         | **75.11** | **71.42** | **88.86** | 63.91 | **71.46** | **84.37** | 70.66 | 7.24 |
| NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO     | 73.35   | 71.08 | 87.29 | 72.17 | 54.83 | 83.11 | 71.65 | 46.7 |
| argilla/notus-8x7b-experiment                   | 73.18   | 70.99 | 87.73 | 71.33 | 65.79 | 81.61 | 61.64 | 46.7 |
| **louisbrulenaudet/Pearl-7B-slerp**             | 72.75   | 68.00 | 87.16 | 64.04 | 62.35 | 81.29 | **73.62** | 7.24 |
| mistralai/Mixtral-8x7B-Instruct-v0.1            | 72.7    | 70.14 | 87.55 | 71.4  | 64.98 | 81.06 | 61.11 | 46.7 |
| microsoft/Orca-2-13b                            | 61.98   | 60.92 | 79.85 | 60.3  | 56.42 | 76.56 | 37.83 | 13 |
| microsoft/phi-2                                 | 61.33   | 61.09 | 75.11 | 58.11 | 44.47 | 74.35 | 54.81 | 2.78 |

### Ties merging

TIES-Merging is a method designed to facilitate the efficient merging of multiple task-specific models into a consolidated multitask model. It addresses two primary challenges encountered in the process of model merging with a focus on maintaining objectivity.

One key challenge tackled by TIES-Merging involves addressing redundancy in model parameters. This is achieved by identifying and eliminating redundant parameters within task-specific models, emphasizing the changes made during fine-tuning and selectively retaining the top-k% most significant changes while discarding the rest.

Another challenge pertains to conflicts arising from disagreements between parameter signs across different models. TIES-Merging resolves these conflicts by creating a unified sign vector representing the most dominant direction of change across all models.

The TIES-Merging process consists of three steps:

- Trim: Reduces redundancy in task-specific models by retaining a fraction of the most significant parameters (density parameter) and resetting the remaining parameters to zero.
- Elect Sign: Resolves sign conflicts across different models by creating a unified sign vector based on the most dominant direction (positive or negative) in terms of cumulative magnitude.
- Disjoint Merge: Averages parameter values aligned with the unified sign vector, excluding zero values.

## Configuration

```yaml
models:
  - model: OpenPipe/mistral-ft-optimized-1227
  - model: louisbrulenaudet/Pearl-7B-slerp
    parameters:
      density: 0.6
      weight: 0.3
  - model: WizardLM/WizardMath-7B-V1.1
    parameters:
      density: 0.55
      weight: 0.2
  - model: cognitivecomputations/WestLake-7B-v2-laser
    parameters:
      density: 0.55
      weight: 0.25
  - model: CultriX/NeuralTrix-7B-dpo
    parameters:
     density: 0.6
     weight: 0.25
merge_method: ties
base_model: OpenPipe/mistral-ft-optimized-1227
parameters:
  normalize: true
  int8_mask: true
dtype: float16
```

## Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "louisbrulenaudet/Pearl-7B-0211-ties"
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"])
```

## Citing & Authors

If you use this code in your research, please use the following BibTeX entry.

```BibTeX
@misc{louisbrulenaudet2023,
  author =       {Louis Brulé Naudet},
  title =        {Pearl-7B-0211-ties, an xtraordinary 7B model},
  year =         {2023}
  howpublished = {\url{https://huggingface.co/louisbrulenaudet/Pearl-7B-0211-ties}},
}
```

## Feedback

If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).