---
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](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [yam-peleg/Experiment21-7B](https://huggingface.co/yam-peleg/Experiment21-7B)
* [CultriX/NeuralTrix-bf16](https://huggingface.co/CultriX/NeuralTrix-bf16)
* [louisgrc/Montebello_7B_SLERP](https://huggingface.co/louisgrc/Montebello_7B_SLERP)
* [CorticalStack/pastiche-crown-clown-7b-dare-dpo](https://huggingface.co/CorticalStack/pastiche-crown-clown-7b-dare-dpo)
* [chihoonlee10/T3Q-Mistral-Orca-Math-DPO](https://huggingface.co/chihoonlee10/T3Q-Mistral-Orca-Math-DPO)

## 🧩 Configuration

```yaml
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

```python
!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"])
```