Quantization made by Richard Erkhov.
NeuralMona_MoE-4x7B - GGUF
- Model creator: https://huggingface.co./CultriX/
- Original model: https://huggingface.co./CultriX/NeuralMona_MoE-4x7B/
Original model description:
license: apache-2.0 tags: - moe - frankenmoe - merge - mergekit - lazymergekit - CultriX/MonaTrix-v4 - mlabonne/OmniTruthyBeagle-7B-v0 - CultriX/MoNeuTrix-7B-v1 - paulml/OmniBeagleSquaredMBX-v3-7B base_model: - CultriX/MonaTrix-v4 - mlabonne/OmniTruthyBeagle-7B-v0 - CultriX/MoNeuTrix-7B-v1 - paulml/OmniBeagleSquaredMBX-v3-7B
NeuralMona_MoE-4x7B
NeuralMona_MoE-4x7B is a Mixture of Experts (MoE) made with the following models using LazyMergekit:
- CultriX/MonaTrix-v4
- mlabonne/OmniTruthyBeagle-7B-v0
- CultriX/MoNeuTrix-7B-v1
- paulml/OmniBeagleSquaredMBX-v3-7B
🧩 Configuration
base_model: CultriX/MonaTrix-v4
dtype: bfloat16
experts:
- source_model: "CultriX/MonaTrix-v4" # Historical Analysis, Geopolitics, and Economic Evaluation
positive_prompts:
- "Historic analysis"
- "Geopolitical impacts"
- "Evaluate significance"
- "Predict impact"
- "Assess consequences"
- "Discuss implications"
- "Explain geopolitical"
- "Analyze historical"
- "Examine economic"
- "Evaluate role"
- "Analyze importance"
- "Discuss cultural impact"
- "Discuss historical"
negative_prompts:
- "Compose"
- "Translate"
- "Debate"
- "Solve math"
- "Analyze data"
- "Forecast"
- "Predict"
- "Process"
- "Coding"
- "Programming"
- "Code"
- "Datascience"
- "Cryptography"
- source_model: "mlabonne/OmniTruthyBeagle-7B-v0" # Multilingual Communication and Cultural Insights
positive_prompts:
- "Describe cultural"
- "Explain in language"
- "Translate"
- "Compare cultural differences"
- "Discuss cultural impact"
- "Narrate in language"
- "Explain impact on culture"
- "Discuss national identity"
- "Describe cultural significance"
- "Narrate cultural"
- "Discuss folklore"
negative_prompts:
- "Compose"
- "Debate"
- "Solve math"
- "Analyze data"
- "Forecast"
- "Predict"
- "Coding"
- "Programming"
- "Code"
- "Datascience"
- "Cryptography"
- source_model: "CultriX/MoNeuTrix-7B-v1" # Problem Solving, Innovation, and Creative Thinking
positive_prompts:
- "Devise strategy"
- "Imagine society"
- "Invent device"
- "Design concept"
- "Propose theory"
- "Reason math"
- "Develop strategy"
- "Invent"
negative_prompts:
- "Translate"
- "Discuss"
- "Debate"
- "Summarize"
- "Explain"
- "Detail"
- "Compose"
- source_model: "paulml/OmniBeagleSquaredMBX-v3-7B" # Explaining Scientific Phenomena and Principles
positive_prompts:
- "Explain scientific"
- "Discuss impact"
- "Analyze potential"
- "Elucidate significance"
- "Summarize findings"
- "Detail explanation"
negative_prompts:
- "Cultural significance"
- "Engage in creative writing"
- "Perform subjective judgment tasks"
- "Discuss cultural traditions"
- "Write review"
- "Design"
- "Create"
- "Narrate"
- "Discuss"
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "CultriX/NeuralMona_MoE-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"])