README.md

---
tags:
- merge
- mergekit
- lazymergekit
- Kukedlc/Neural-Cosmic-7B-slerp
- Kukedlc/NeuralLogic-7B-V
- Kukedlc/SuperCombo
base_model:
- Kukedlc/Neural-Cosmic-7B-slerp
- Kukedlc/NeuralLogic-7B-V
- Kukedlc/SuperCombo
license: apache-2.0
---

## Note: The merge method is ties, not slerp.

# Neural-Cosmic-Boy-7B-slerp

![Neural Cosmic Boy - 7 billons params](https://raw.githubusercontent.com/kukedlc87/imagenes/main/DALL%C2%B7E%202024-02-17%2020.28.38%20-%20Visualize%20a%20human%20face%20composed%20entirely%20of%20topographic%20lines%2C%20similar%20to%20those%20found%20on%20a%20mountain%20map.%20This%20artistic%20representation%20uses%20only%20lines%20.webp)
Neural-Cosmic-Boy-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Kukedlc/Neural-Cosmic-7B-slerp](https://huggingface.co./Kukedlc/Neural-Cosmic-7B-slerp)
* [Kukedlc/NeuralLogic-7B-V](https://huggingface.co./Kukedlc/NeuralLogic-7B-V)
* [Kukedlc/SuperCombo](https://huggingface.co./Kukedlc/SuperCombo)

## 🧩 Configuration

```yaml
models:
  - model: Kukedlc/Neural-Cosmic-7B-slerp
    parameters:
      density: [1, 0.7, 0.1] # density gradient
      weight: 1.0
  - model: Kukedlc/NeuralLogic-7B-V
    parameters:
      density: 0.5
      weight: [0, 0.3, 0.7, 1] # weight gradient
  - model: Kukedlc/SuperCombo
    parameters:
      density: 0.33
      weight:
        - filter: mlp
          value: 0.5
        - value: 0
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  normalize: true
  int8_mask: true
dtype: float16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/Neural-Cosmic-Boy-7B-slerp"
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"])
```