metadata
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:
- 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"])