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RichardErkhov/
louisbrulenaudet_-_Pearl-34B-ties-gguf

Name: louisbrulenaudet_-_Pearl-34B-ties-gguf
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Pearl-34B-ties - GGUF

Model creator: https://huggingface.co/louisbrulenaudet/
Original model: https://huggingface.co/louisbrulenaudet/Pearl-34B-ties/

Name	Quant method	Size
Pearl-34B-ties.Q2_K.gguf	Q2_K	11.94GB
Pearl-34B-ties.IQ3_XS.gguf	IQ3_XS	13.26GB
Pearl-34B-ties.IQ3_S.gguf	IQ3_S	13.99GB
Pearl-34B-ties.Q3_K_S.gguf	Q3_K_S	13.93GB
Pearl-34B-ties.IQ3_M.gguf	IQ3_M	14.5GB
Pearl-34B-ties.Q3_K.gguf	Q3_K	15.51GB
Pearl-34B-ties.Q3_K_M.gguf	Q3_K_M	15.51GB
Pearl-34B-ties.Q3_K_L.gguf	Q3_K_L	16.89GB
Pearl-34B-ties.IQ4_XS.gguf	IQ4_XS	17.36GB
Pearl-34B-ties.Q4_0.gguf	Q4_0	18.13GB
Pearl-34B-ties.IQ4_NL.gguf	IQ4_NL	18.3GB
Pearl-34B-ties.Q4_K_S.gguf	Q4_K_S	18.25GB
Pearl-34B-ties.Q4_K.gguf	Q4_K	19.24GB
Pearl-34B-ties.Q4_K_M.gguf	Q4_K_M	19.24GB
Pearl-34B-ties.Q4_1.gguf	Q4_1	20.1GB
Pearl-34B-ties.Q5_0.gguf	Q5_0	22.08GB
Pearl-34B-ties.Q5_K_S.gguf	Q5_K_S	22.08GB
Pearl-34B-ties.Q5_K.gguf	Q5_K	22.65GB
Pearl-34B-ties.Q5_K_M.gguf	Q5_K_M	22.65GB
Pearl-34B-ties.Q5_1.gguf	Q5_1	24.05GB
Pearl-34B-ties.Q6_K.gguf	Q6_K	26.28GB
Pearl-34B-ties.Q8_0.gguf	Q8_0	34.03GB

Original model description:

tags:

merge
mergekit
jondurbin/bagel-dpo-34b-v0.2
abacusai/MetaMath-Bagel-DPO-34B base_model:
jondurbin/bagel-dpo-34b-v0.2
abacusai/MetaMath-Bagel-DPO-34B 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: 75.48
  - name: ARC type: ARC value: 70.99
  - name: GSM8K type: GSM8K value: 67.48
  - name: Winogrande type: Winogrande value: 82.64
  - name: TruthfulQA type: TruthfulQA value: 70.32
  - name: HellaSwag type: HellaSwag value: 84.83
  - name: MMLU type: MMLU value: 76.63 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard

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:

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: 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

python

!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.

BibTeX

@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 louisbrulenaudet@icloud.com.

RichardErkhov/louisbrulenaudet_-_Pearl-34B-ties-gguf