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	---
	tags:
	- merge
	- mergekit
	- louisbrulenaudet/Pearl-7B-slerp
	- WizardLM/WizardMath-7B-V1.1
	- cognitivecomputations/WestLake-7B-v2-laser
	- CultriX/NeuralTrix-7B-dpo
	- chemistry
	- biology
	- math
	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-0210-ties
	results:
	- task:
	type: text-generation
	metrics:
	- name: Average
	type: Average
	value: 74.66
	- name: ARC
	type: ARC
	value: 71.08
	- name: GSM8K
	type: GSM8K
	value: 69.98
	- name: Winogrande
	type: Winogrande
	value: 83.98
	- name: TruthfulQA
	type: TruthfulQA
	value: 70.47
	- name: HellaSwag
	type: HellaSwag
	value: 88.63
	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-0210-ties, an xtraordinary 7B model

	03-22-2024 - To date, louisbrulenaudet/Pearl-34B-ties is the "Best 🤝 base merges and moerges model of around 30B" on the Open LLM Leaderboard.

	Pearl-7B-0210-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: 0.5
	weight: 0.4
	- model: WizardLM/WizardMath-7B-V1.1
	parameters:
	density: 0.5
	weight: 0.2
	- model: cognitivecomputations/WestLake-7B-v2-laser
	parameters:
	density: 0.5
	weight: 0.2
	- model: CultriX/NeuralTrix-7B-dpo
	parameters:
	density: 0.5
	weight: 0.2
	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-0210-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-0210-ties, an xtraordinary 7B model},
	year = {2023}
	howpublished = {\url{https://huggingface.co/louisbrulenaudet/Pearl-7B-0210-ties}},
	}
	```

	## Feedback

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