RandomMergeNoNormWEIGHTED-7B-MODELSTOCK

RandomMergeNoNormWEIGHTED-7B-MODELSTOCK is a merge of the following models using LazyMergekit:

• FelixChao/WestSeverus-7B-DPO-v2
• CultriX/Wernicke-7B-v9
• mlabonne/NeuralBeagle14-7B

🧩 Configuration

models:
  - model: FelixChao/WestSeverus-7B-DPO-v2
    # No parameters necessary for base model
  - model: FelixChao/WestSeverus-7B-DPO-v2
    parameters:
      density: [1, 0.7, 0.1]
      weight: [0, 0.3, 0.7, 1]
  - model: CultriX/Wernicke-7B-v9
    parameters:
      density: [1, 0.7, 0.3]
      weight: [0, 0.25, 0.5, 1]
  - model: mlabonne/NeuralBeagle14-7B
    parameters:
      density: 0.25
      weight:
        - filter: mlp
          value: 0.5
        - value: 0
merge_method: model_stock
base_model: FelixChao/WestSeverus-7B-DPO-v2
parameters:
  int8_mask: true
  normalize: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jsfs11/RandomMergeNoNormWEIGHTED-7B-MODELSTOCK"
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"])