RichardErkhov/Gille_-_StrangeMerges_52-7B-dare_ties-gguf

Quantization made by Richard Erkhov.

Github

Discord

Request more models

StrangeMerges52-7B-dareties - GGUF

• —Model creator: https://huggingface.co/Gille/
• —Original model: https://huggingface.co/Gille/StrangeMerges52-7B-dareties/

Original model description: --- license: apache-2.0 tags:

• —merge
• —mergekit
• —lazymergekit
• —WizardLM/WizardMath-7B-V1.1
• —AurelPx/Percival_01-7b-slerp
• —Weyaxi/Einstein-v4-7B
• —Kukedlc/NeuralMaths-Experiment-7b
• —Gille/StrangeMerges35-7B-slerp basemodel:
• —WizardLM/WizardMath-7B-V1.1
• —AurelPx/Percival_01-7b-slerp
• —Weyaxi/Einstein-v4-7B
• —Kukedlc/NeuralMaths-Experiment-7b
• —Gille/StrangeMerges_35-7B-slerp model-index:
• —name: StrangeMerges52-7B-dareties results:
• —task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2arc config: ARC-Challenge split: test args: numfew_shot: 25 metrics:
• —type: accnorm value: 69.03 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=Gille/StrangeMerges52-7B-dare_ties name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: numfewshot: 10 metrics:
• —type: accnorm value: 87.15 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=Gille/StrangeMerges52-7B-dare_ties name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: numfewshot: 5 metrics:
• —type: acc value: 64.94 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=Gille/StrangeMerges52-7B-dareties name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthfulqa config: multiplechoice split: validation args: numfewshot: 0 metrics:
• —type: mc2 value: 65.76 source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=Gille/StrangeMerges52-7B-dareties name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winograndexl split: validation args: numfew_shot: 5 metrics:
• —type: acc value: 81.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=Gille/StrangeMerges52-7B-dareties name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: numfewshot: 5 metrics:
• —type: acc value: 72.25 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=Gille/StrangeMerges52-7B-dareties name: Open LLM Leaderboard ---

StrangeMerges52-7B-dareties

StrangeMerges52-7B-dareties is a merge of the following models using LazyMergekit:

• —WizardLM/WizardMath-7B-V1.1
• —AurelPx/Percival_01-7b-slerp
• —Weyaxi/Einstein-v4-7B
• —Kukedlc/NeuralMaths-Experiment-7b
• —Gille/StrangeMerges_35-7B-slerp

🧩 Configuration

models:
  - model: Gille/StrangeMerges_51-7B-dare_ties
    # No parameters necessary for base model
  - model: WizardLM/WizardMath-7B-V1.1
    parameters:
      density: 0.66
      weight: 0.2
  - model: AurelPx/Percival_01-7b-slerp
    parameters:
      density: 0.55
      weight: 0.2
  - model: Weyaxi/Einstein-v4-7B
    parameters:
      density: 0.55
      weight: 0.2
  - model: Kukedlc/NeuralMaths-Experiment-7b
    parameters:
      density: 0.44
      weight: 0.2
  - model: Gille/StrangeMerges_35-7B-slerp
    parameters:
      density: 0.66
      weight: 0.2
merge_method: dare_ties
base_model: Gille/StrangeMerges_51-7B-dare_ties
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Gille/StrangeMerges_52-7B-dare_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"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here