metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - yam-peleg/Experiment21-7B
  - CultriX/NeuralTrix-bf16
  - louisgrc/Montebello_7B_SLERP
  - CorticalStack/pastiche-crown-clown-7b-dare-dpo
  - chihoonlee10/T3Q-Mistral-Orca-Math-DPO
base_model:
  - yam-peleg/Experiment21-7B
  - CultriX/NeuralTrix-bf16
  - louisgrc/Montebello_7B_SLERP
  - CorticalStack/pastiche-crown-clown-7b-dare-dpo
  - chihoonlee10/T3Q-Mistral-Orca-Math-DPO
license: apache-2.0

Neural-4-QA-7b

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

🧩 Configuration

models:
  - model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
    # No parameters necessary for base model
  - model: yam-peleg/Experiment21-7B
    parameters:
      density: 0.66
      weight: 0.2
  - model: CultriX/NeuralTrix-bf16
    parameters:
      density: 0.55
      weight: 0.2
  - model: louisgrc/Montebello_7B_SLERP
    parameters:
      density: 0.55
      weight: 0.2
  - model: CorticalStack/pastiche-crown-clown-7b-dare-dpo
    parameters:
      density: 0.44
      weight: 0.2
  - model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
    parameters:
      density: 0.66
      weight: 0.2
merge_method: dare_ties
base_model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/Neural-4-QA-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"])