---
tags:
- merge
- mergekit
- lazymergekit
- yam-peleg/Experiment26-7B
- Gille/StrangeMerges_32-7B-slerp
- MSL7/INEX12-7b
- automerger/YamShadow-7B
- Kukedlc/NeuralSirKrishna-7b
base_model:
- yam-peleg/Experiment26-7B
- Gille/StrangeMerges_32-7B-slerp
- MSL7/INEX12-7b
- automerger/YamShadow-7B
- Kukedlc/NeuralSirKrishna-7b
---

# NeuralArjuna-7B-DT


![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d71ab4089bc502ceb44d29/zFLiis1pQWnriLQb2ZGGn.png)

NeuralArjuna-7B-DT is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B)
* [Gille/StrangeMerges_32-7B-slerp](https://huggingface.co/Gille/StrangeMerges_32-7B-slerp)
* [MSL7/INEX12-7b](https://huggingface.co/MSL7/INEX12-7b)
* [automerger/YamShadow-7B](https://huggingface.co/automerger/YamShadow-7B)
* [Kukedlc/NeuralSirKrishna-7b](https://huggingface.co/Kukedlc/NeuralSirKrishna-7b)

## 🧩 Configuration

```yaml
models:
  - model: liminerity/M7-7b
    # no parameters necessary for base model
  - model: yam-peleg/Experiment26-7B 
    parameters:
      weight: 0.2
      density: 0.66
  - model: Gille/StrangeMerges_32-7B-slerp
    parameters:
      weight: 0.2
      density: 0.55
  - model: MSL7/INEX12-7b 
    parameters:
      weight: 0.2
      density: 0.33
  - model: automerger/YamShadow-7B
    parameters:
      weight: 0.2
      density: 0.66
  - model: Kukedlc/NeuralSirKrishna-7b
    parameters:
      weight: 0.2
      density: 0.66
merge_method: dare_ties
base_model: liminerity/M7-7b

parameters:
  int8_mask: true
  normalize: true
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/NeuralArjuna-7B-DT"
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"])
```