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RichardErkhov/
OmnicromsBrain_-_NeuralStar_AlphaWriter_4x7b-gguf

Name: OmnicromsBrain_-_NeuralStar_AlphaWriter_4x7b-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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NeuralStar_AlphaWriter_4x7b - GGUF

Model creator: https://huggingface.co/OmnicromsBrain/
Original model: https://huggingface.co/OmnicromsBrain/NeuralStar_AlphaWriter_4x7b/

Name	Quant method	Size
NeuralStar_AlphaWriter_4x7b.Q2_K.gguf	Q2_K	8.24GB
NeuralStar_AlphaWriter_4x7b.IQ3_XS.gguf	IQ3_XS	9.21GB
NeuralStar_AlphaWriter_4x7b.IQ3_S.gguf	IQ3_S	9.73GB
NeuralStar_AlphaWriter_4x7b.Q3_K_S.gguf	Q3_K_S	9.72GB
NeuralStar_AlphaWriter_4x7b.IQ3_M.gguf	IQ3_M	9.92GB
NeuralStar_AlphaWriter_4x7b.Q3_K.gguf	Q3_K	10.79GB
NeuralStar_AlphaWriter_4x7b.Q3_K_M.gguf	Q3_K_M	10.79GB
NeuralStar_AlphaWriter_4x7b.Q3_K_L.gguf	Q3_K_L	11.68GB
NeuralStar_AlphaWriter_4x7b.IQ4_XS.gguf	IQ4_XS	12.15GB
NeuralStar_AlphaWriter_4x7b.Q4_0.gguf	Q4_0	12.69GB
NeuralStar_AlphaWriter_4x7b.IQ4_NL.gguf	IQ4_NL	12.81GB
NeuralStar_AlphaWriter_4x7b.Q4_K_S.gguf	Q4_K_S	12.8GB
NeuralStar_AlphaWriter_4x7b.Q4_K.gguf	Q4_K	13.61GB
NeuralStar_AlphaWriter_4x7b.Q4_K_M.gguf	Q4_K_M	13.61GB
NeuralStar_AlphaWriter_4x7b.Q4_1.gguf	Q4_1	14.09GB
NeuralStar_AlphaWriter_4x7b.Q5_0.gguf	Q5_0	15.48GB
NeuralStar_AlphaWriter_4x7b.Q5_K_S.gguf	Q5_K_S	15.48GB
NeuralStar_AlphaWriter_4x7b.Q5_K.gguf	Q5_K	15.96GB
NeuralStar_AlphaWriter_4x7b.Q5_K_M.gguf	Q5_K_M	15.96GB
NeuralStar_AlphaWriter_4x7b.Q5_1.gguf	Q5_1	16.88GB
NeuralStar_AlphaWriter_4x7b.Q6_K.gguf	Q6_K	18.46GB
NeuralStar_AlphaWriter_4x7b.Q8_0.gguf	Q8_0	23.9GB

Original model description:

license: apache-2.0 tags:

moe
frankenmoe
merge
mergekit
lazymergekit
mlabonne/AlphaMonarch-7B
FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B
SanjiWatsuki/Kunoichi-DPO-v2-7B
OmnicromsBrain/NeuralStar-7b-Lazy base_model:
mlabonne/AlphaMonarch-7B
FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B
SanjiWatsuki/Kunoichi-DPO-v2-7B
OmnicromsBrain/NeuralStar-7b-Lazy model-index:
name: NeuralStar_AlphaWriter_4x7b results:
- task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
  - type: acc_norm value: 70.22 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
  - type: acc_norm value: 88.31 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
  - type: acc value: 64.6 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: mc2 value: 71.7 source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
  - type: acc value: 82.0 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 63.0 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b name: Open LLM Leaderboard

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:

⚡ 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

yaml

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

python

!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

Metric	Value
Avg.	73.31
AI2 Reasoning Challenge (25-Shot)	70.22
HellaSwag (10-Shot)	88.31
MMLU (5-Shot)	64.60
TruthfulQA (0-shot)	71.70
Winogrande (5-shot)	82.00
GSM8k (5-shot)	63.00

RichardErkhov/OmnicromsBrain_-_NeuralStar_AlphaWriter_4x7b-gguf