Text Generation
Transformers
Safetensors
mixtral
Mixture of Experts
frankenmoe
Merge
mergekit
lazymergekit
mlabonne/AlphaMonarch-7B
FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B
SanjiWatsuki/Kunoichi-DPO-v2-7B
OmnicromsBrain/NeuralStar-7b-Lazy
conversational
Eval Results
text-generation-inference
Inference Endpoints
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
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 |
- Downloads last month
- 44
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for OmnicromsBrain/NeuralStar_AlphaWriter_4x7b
Merge model
this model
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard70.220
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.310
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.600
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard71.700
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard82.000
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard63.000