Pearl-34B-ties, an xtraordinary 34B 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-34B-ties is a merge of the following models:

• jondurbin/bagel-dpo-34b-v0.2
• abacusai/MetaMath-Bagel-DPO-34B

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

models:
  - model: abacusai/Smaug-34B-v0.1
  - model: jondurbin/bagel-dpo-34b-v0.2
    parameters:
      density: 0.45
      weight: 0.5
  - model: abacusai/MetaMath-Bagel-DPO-34B
    parameters:
      density: 0.48
      weight: 0.5
merge_method: ties
base_model: abacusai/Smaug-34B-v0.1
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16

Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "louisbrulenaudet/Pearl-34B-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.

@misc{louisbrulenaudet2023,
  author =       {Louis Brulé Naudet},
  title =        {Pearl-34B-ties, an xtraordinary 34B model},
  year =         {2023}
  howpublished = {\url{https://huggingface.co/louisbrulenaudet/Pearl-34B-ties}},
}

Feedback

If you have any feedback, please reach out at [email protected].