base_model:CultriX/MonaTrix-v4dtype:bfloat16experts:-source_model:"CultriX/MonaTrix-v4"# Historical Analysis, Geopolitics, and Economic Evaluationpositive_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 Insightspositive_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 Thinkingpositive_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 Principlespositive_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"])
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