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---
license: apache-2.0
tags:
- moe
- merge
- mergekit
- lazymergekit
- mlabonne/AlphaMonarch-7B
- OmnicromsBrain/Eros_Scribe-7b
- SanjiWatsuki/Kunoichi-DPO-v2-7B
- OmnicromsBrain/NeuralStar_Fusion-7B
base_model:
- mlabonne/AlphaMonarch-7B
- OmnicromsBrain/Eros_Scribe-7b
- SanjiWatsuki/Kunoichi-DPO-v2-7B
- OmnicromsBrain/NeuralStar_Fusion-7B
---
# NeuralStar_FusionWriter_4x7b
NeuralStar_FusionWriter_4x7b is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
* [OmnicromsBrain/Eros_Scribe-7b](https://huggingface.co/OmnicromsBrain/Eros_Scribe-7b)
* [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B)
* [OmnicromsBrain/NeuralStar_Fusion-7B](https://huggingface.co/OmnicromsBrain/NeuralStar_Fusion-7B)
## ⚡ Quantized Models
Special thanks to **MRadermacher** for the **static** and **imatrix** quantized models
**.GGUF** https://huggingface.co/mradermacher/NeuralStar_FusionWriter_4x7b-GGUF
**IMatrix** https://huggingface.co/mradermacher/NeuralStar_FusionWriter_4x7b-i1-GGUF
## 🧩 Configuration
```yaml
base_model: mlabonne/AlphaMonarch-7B
experts:
- source_model: mlabonne/AlphaMonarch-7B
positive_prompts:
- "chat"
- "assistant"
- "tell me"
- "explain"
- "ideas"
- source_model: OmnicromsBrain/Eros_Scribe-7b
positive_prompts:
- "adult"
- "sex"
- "explicit"
- "nsfw"
- "gory"
- source_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
positive_prompts:
- "story"
- "character"
- "scene"
- "plot"
- "editor"
- source_model: OmnicromsBrain/NeuralStar_Fusion-7B
positive_prompts:
- "codex"
- "write"
- "outline"
- "scenebeat"
- "prose"
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "OmnicromsBrain/NeuralStar_FusionWriter_4x7b"
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"])
``` |