kangaroo_7B_test01

kangaroo_7B_test01 is a merge of the following models using LazyMergekit:

• Weyaxi/MetaMath-OpenHermes-2.5-neural-chat-7b-v3-1-7B-Linear
• icefog72/IceMoonshineRP-7b
• Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp
• VAGOsolutions/SauerkrautLM-7b-HerO
• mrfakename/NeuralOrca-7B-v1

🧩 Configuration

models:
  - model: BioMistral/BioMistral-7B-DARE
    # No parameters necessary for base model
  - model: Weyaxi/MetaMath-OpenHermes-2.5-neural-chat-7b-v3-1-7B-Linear
    parameters:
      density: 0.5
      weight: 0.2


  - model: icefog72/IceMoonshineRP-7b
    parameters:
      density: 0.5
      weight: 0.2


  - model: Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp
    parameters:
      density: 0.5
      weight: 0.2


  - model: VAGOsolutions/SauerkrautLM-7b-HerO
    parameters:
      density: 0.5
      weight: 0.2


  - model: mrfakename/NeuralOrca-7B-v1
    parameters:
      density: 0.5
      weight: 0.2


merge_method: dare_ties
base_model: BioMistral/BioMistral-7B-DARE
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "kainatq/kangaroo_7B_test01"
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"])