Gille / StrangeMerges_52-7B-dare_ties

StrangeMerges_52-7B-dare_ties

StrangeMerges_52-7B-dare_ties 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