--- 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 --- # MoNeuTrix-MoE-4x7B MoNeuTrix-MoE-4x7B is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [CultriX/MonaTrix-v4](https://huggingface.co/CultriX/MonaTrix-v4) * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0) * [CultriX/MoNeuTrix-7B-v1](https://huggingface.co/CultriX/MoNeuTrix-7B-v1) * [paulml/OmniBeagleSquaredMBX-v3-7B](https://huggingface.co/paulml/OmniBeagleSquaredMBX-v3-7B) ## 🧩 Configuration ```yaml base_model: "CultriX/MonaTrix-v4" dtype: bfloat16 gate: type: "learned" temperature: 0.1 scaling_factor: 10 experts: - source_model: "CultriX/MonaTrix-v4" # Historical Analysis, Geopolitics, and Economic Evaluation positive_prompts: - "Historical Analysis" - "Geopolitical Evaluation" - "Economic Insights" - "Policy Analysis" - "Socio-Economic Impacts" - "Geopolitical Analysis" - "Cultural Commentary" - "Analyze geopolitical" - "Analyze historic" - "Analyze historical" - "Assess the political dynamics of the Cold War and its global impact." - "Evaluate the historical significance of the Silk Road in ancient trade." negative_prompts: - "Technical Writing" - "Mathematical Problem Solving" - "Software Development" - "Artistic Creation" - "Machine Learning Development" - "Storywriting" - "Character Development" - "Roleplaying" - "Narrative Creation" - source_model: "mlabonne/OmniTruthyBeagle-7B-v0" # Multilingual Communication and Cultural Insights positive_prompts: - "Multilingual Communication" - "Cultural Insights" - "Translation and Interpretation" - "Cultural Norms Exploration" - "Intercultural Communication Practices" - "Describe cultural significance" - "Narrate cultural" - "Discuss cultural impact" negative_prompts: - "Scientific Analysis" - "Creative Writing" - "Technical Documentation" - "Economic Modeling" - "Historical Documentation" - "Programming" - "Algorithm Development" - source_model: "CultriX/MoNeuTrix-7B-v1" # Creative Problem Solving and Innovation positive_prompts: - "Innovation and Design" - "Problem Solving" - "Creative Thinking" - "Strategic Planning" - "Conceptual Design" - "Innovation and Design" - "Problem Solving" - "Compose narrative content or poetry." - "Create complex puzzles and games." - "Devise strategy" negative_prompts: - "Historical Analysis" - "Linguistic Translation" - "Economic Forecasting" - "Geopolitical Analysis" - "Cultural Commentary" - "Historical Documentation" - "Scientific Explanation" - "Data Analysis Techniques" - source_model: "paulml/OmniBeagleSquaredMBX-v3-7B" # Scientific and Technical Expertise positive_prompts: - "Scientific Explanation" - "Technical Analysis" - "Experimental Design" - "Data Analysis Techniques" - "Scientific Innovation" - "Mathematical Problem Solving" - "Algorithm Development" - "Programming" - "Analyze data" - "Analyze statistical data on climate change trends." - "Conduct basic data analysis or statistical evaluations." negative_prompts: - "Cultural Analysis" - "Creative Arts" - "Linguistic Challenges" - "Political Debating" - "Marketing Strategies" - "Storywriting" - "Character Development" - "Roleplaying" - "Narrative Creation" ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "CultriX/MoNeuTrix-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"]) ```