---
tags:
- merge
- mergekit
- lazymergekit
- automerger/YamShadow-7B
- mlabonne/AlphaMonarch-7B
- automerger/OgnoExperiment27-7B
- Kukedlc/Jupiter-k-7B-slerp
base_model:
- automerger/YamShadow-7B
- mlabonne/AlphaMonarch-7B
- automerger/OgnoExperiment27-7B
- Kukedlc/Jupiter-k-7B-slerp
license: apache-2.0
---

# NeuralShiva-7B-DT


![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d71ab4089bc502ceb44d29/fk_S2Xf9oeVGdTPTJxt3Q.png)

NeuralShiva-7B-DT is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [automerger/YamShadow-7B](https://huggingface.co/automerger/YamShadow-7B)
* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
* [automerger/OgnoExperiment27-7B](https://huggingface.co/automerger/OgnoExperiment27-7B)
* [Kukedlc/Jupiter-k-7B-slerp](https://huggingface.co/Kukedlc/Jupiter-k-7B-slerp)

## 🧬 Model Family

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d71ab4089bc502ceb44d29/8ToDn8zpU9I-OGFB354pU.png)

## 🧩 Configuration

```yaml
models:
  - model: liminerity/M7-7b
    # no parameters necessary for base model
  - model: automerger/YamShadow-7B 
    parameters:
      weight: 0.3
      density: 0.5
  - model: mlabonne/AlphaMonarch-7B 
    parameters:
      weight: 0.2
      density: 0.5
  - model: automerger/OgnoExperiment27-7B 
    parameters:
      weight: 0.2
      density: 0.5
  - model: Kukedlc/Jupiter-k-7B-slerp
    parameters:
      weight: 0.3
      density: 0.5
merge_method: dare_ties
base_model: liminerity/M7-7b

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


## 💻 Usage - Stream

```python
# Requirements
!pip install -qU transformers accelerate bitsandbytes

# Imports & settings
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import warnings
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
warnings.filterwarnings('ignore')

# Model & Tokenizer
MODEL_NAME = "Kukedlc/NeuralShiva-7B-DT"
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map='cuda:1', load_in_4bit=True)
tok = AutoTokenizer.from_pretrained(MODEL_NAME)

# Inference
prompt = "I want you to generate a theory that unites quantum mechanics with the theory of relativity and cosmic consciousness"
inputs = tok([prompt], return_tensors="pt").to('cuda')
streamer = TextStreamer(tok)

# Despite returning the usual output, the streamer will also print the generated text to stdout.
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=512, do_sample=True, num_beams=1, top_p=0.9, temperature=0.7)

```
## 💻 Usage - Clasic

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/NeuralShiva-7B-DT"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])
```