NeuralStar_FusionWriter_4x7b

NeuralStar_FusionWriter_4x7b is a Mixture of Experts (MoE) made with the following models using LazyMergekit:

• mlabonne/AlphaMonarch-7B
• OmnicromsBrain/Eros_Scribe-7b
• SanjiWatsuki/Kunoichi-DPO-v2-7B
• 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

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

!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"])