StrangeMerges_38-7B-dare_ties/README.md

license, tags, base_model

license	tags	base_model
apache-2.0	merge mergekit lazymergekit automerger/NeuralsirkrishnaExperiment26-7B Gille/StrangeMerges_21-7B-slerp Gille/StrangeMerges_34-7B-slerp	automerger/NeuralsirkrishnaExperiment26-7B Gille/StrangeMerges_21-7B-slerp Gille/StrangeMerges_34-7B-slerp

StrangeMerges_38-7B-dare_ties

StrangeMerges_38-7B-dare_ties is a merge of the following models using LazyMergekit:

• automerger/NeuralsirkrishnaExperiment26-7B
• Gille/StrangeMerges_21-7B-slerp
• Gille/StrangeMerges_34-7B-slerp

🧩 Configuration

models:
  - model: Gille/StrangeMerges_37-7B-dare_ties
    # No parameters necessary for base model
  - model: automerger/NeuralsirkrishnaExperiment26-7B
    parameters:
      density: 0.53
      weight: 0.4
  - model: Gille/StrangeMerges_21-7B-slerp
    parameters:
      density: 0.53
      weight: 0.3
  - model: Gille/StrangeMerges_34-7B-slerp
    parameters:
      density: 0.53
      weight: 0.3
merge_method: dare_ties
base_model: Gille/StrangeMerges_37-7B-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_38-7B-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"])