Neural-4-Wino-7b

Neural-4-Wino-7b

Neural-4-Wino-7b is a merge of the following models using LazyMergekit:

• Kukedlc/NeuralFusion-7b-Dare-Ties
• paulml/OmniBeagleSquaredMBX-v3-7B-v2
• macadeliccc/MBX-7B-v3-DPO
• Kukedlc/Fasciculus-Arcuatus-7B-slerp
• liminerity/Neurotic-Jomainotrik-7b-slerp

🧩 Configuration

models:
  - model: liminerity/Neurotic-Jomainotrik-7b-slerp
    # No parameters necessary for base model
  - model: Kukedlc/NeuralFusion-7b-Dare-Ties
    parameters:
      density: 0.66
      weight: 0.2
  - model: paulml/OmniBeagleSquaredMBX-v3-7B-v2
    parameters:
      density: 0.55
      weight: 0.2
  - model: macadeliccc/MBX-7B-v3-DPO
    parameters:
      density: 0.55
      weight: 0.2
  - model: Kukedlc/Fasciculus-Arcuatus-7B-slerp
    parameters:
      density: 0.44
      weight: 0.2
  - model: liminerity/Neurotic-Jomainotrik-7b-slerp
    parameters:
      density: 0.66
      weight: 0.2
merge_method: dare_ties
base_model: liminerity/Neurotic-Jomainotrik-7b-slerp
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/Neural-4-Wino-7b"
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"])