👁 image/png

NeuralStar_AlphaWriter_4x7b

I was blown away by the writing results I was getting from mlabonne/Beyonder-4x7B-v3 while writing in NovelCrafter.

Inspired by his LLM Course and fueled by his LazyMergeKit. I couldnt help but wonder what a writing model would be like if all 4 “experts” excelled in creative writing.

I present NeuralStar-AlphaWriter-4x7b:

NeuralStar_AlphaWriter_4x7b is a Mixture of Experts (MoE) made with the following models using LazyMergekit:

• mlabonne/AlphaMonarch-7B
• FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B
• SanjiWatsuki/Kunoichi-DPO-v2-7B
• OmnicromsBrain/NeuralStar-7b-Lazy

⚡ Quantized Models

Special thanks to MRadermacher for the Static and iMatrx quantized models

.GGUF https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF

iMatrix https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-i1-GGUF

Q4_K_M and Q5_K_M .gguf Here created with mlabonne/Autogguf

🧩 Configuration

base_model: mlabonne/AlphaMonarch-7B
experts: 
 - source_model: mlabonne/AlphaMonarch-7B
 positive_prompts: 
 - "chat"
 - "assistant"
 - "tell me"
 - "explain"
 - "I want"
 - source_model: FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B
 positive_prompts:
 - "edit"
 - "rewrite"
 - "evaluate"
 - "spelling"
 - "grammer"
 - source_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
 positive_prompts:
 - "storywriting"
 - "write"
 - "scene"
 - "prose"
 - "character"
 - source_model: OmnicromsBrain/NeuralStar-7b-Lazy
 positive_prompts:
 - "codex"
 - "plot"
 - "outline"
 - "scenebeat"
 - "count"

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "OmnicromsBrain/NeuralStar_AlphaWriter_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"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here