---
tags:
- merge
- mergekit
- lazymergekit
- paulml/OmniBeagleSquaredMBX-v3-7B
- nlpguy/AlloyIngotNeoX
- Gille/StrangeMerges_21-7B-slerp
- Kukedlc/Jupiter-k-7B-slerp
- Kukedlc/NeuralSirKrishna-7b
base_model:
- paulml/OmniBeagleSquaredMBX-v3-7B
- nlpguy/AlloyIngotNeoX
- Gille/StrangeMerges_21-7B-slerp
- Kukedlc/Jupiter-k-7B-slerp
- Kukedlc/NeuralSirKrishna-7b
license: apache-2.0
---

# Neural-4-ARC-7b

Neural-4-ARC-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [paulml/OmniBeagleSquaredMBX-v3-7B](https://huggingface.co./paulml/OmniBeagleSquaredMBX-v3-7B)
* [nlpguy/AlloyIngotNeoX](https://huggingface.co./nlpguy/AlloyIngotNeoX)
* [Gille/StrangeMerges_21-7B-slerp](https://huggingface.co./Gille/StrangeMerges_21-7B-slerp)
* [Kukedlc/Jupiter-k-7B-slerp](https://huggingface.co./Kukedlc/Jupiter-k-7B-slerp)
* [Kukedlc/NeuralSirKrishna-7b](https://huggingface.co./Kukedlc/NeuralSirKrishna-7b)

## 🧩 Configuration

```yaml
models:
  - model: Kukedlc/NeuralSirKrishna-7b
    # No parameters necessary for base model
  - model: paulml/OmniBeagleSquaredMBX-v3-7B
    parameters:
      density: 0.66
      weight: 0.2
  - model: nlpguy/AlloyIngotNeoX
    parameters:
      density: 0.55
      weight: 0.2
  - model: Gille/StrangeMerges_21-7B-slerp
    parameters:
      density: 0.55
      weight: 0.2
  - model: Kukedlc/Jupiter-k-7B-slerp
    parameters:
      density: 0.44
      weight: 0.2
  - model: Kukedlc/NeuralSirKrishna-7b
    parameters:
      density: 0.66
      weight: 0.2
merge_method: dare_ties
base_model: Kukedlc/NeuralSirKrishna-7b
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-ARC-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"])
```