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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+NeuralMona_MoE-4x7B - GGUF
+- Model creator: https://huggingface.co/CultriX/
+- Original model: https://huggingface.co/CultriX/NeuralMona_MoE-4x7B/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [NeuralMona_MoE-4x7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q2_K.gguf) | Q2_K | 8.24GB |
+| [NeuralMona_MoE-4x7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.IQ3_XS.gguf) | IQ3_XS | 9.21GB |
+| [NeuralMona_MoE-4x7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.IQ3_S.gguf) | IQ3_S | 9.73GB |
+| [NeuralMona_MoE-4x7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q3_K_S.gguf) | Q3_K_S | 9.72GB |
+| [NeuralMona_MoE-4x7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.IQ3_M.gguf) | IQ3_M | 9.92GB |
+| [NeuralMona_MoE-4x7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q3_K.gguf) | Q3_K | 10.79GB |
+| [NeuralMona_MoE-4x7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q3_K_M.gguf) | Q3_K_M | 10.79GB |
+| [NeuralMona_MoE-4x7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q3_K_L.gguf) | Q3_K_L | 11.68GB |
+| [NeuralMona_MoE-4x7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.IQ4_XS.gguf) | IQ4_XS | 12.15GB |
+| [NeuralMona_MoE-4x7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q4_0.gguf) | Q4_0 | 12.69GB |
+| [NeuralMona_MoE-4x7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.IQ4_NL.gguf) | IQ4_NL | 12.81GB |
+| [NeuralMona_MoE-4x7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q4_K_S.gguf) | Q4_K_S | 12.8GB |
+| [NeuralMona_MoE-4x7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q4_K.gguf) | Q4_K | 13.61GB |
+| [NeuralMona_MoE-4x7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q4_K_M.gguf) | Q4_K_M | 13.61GB |
+| [NeuralMona_MoE-4x7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q4_1.gguf) | Q4_1 | 14.09GB |
+| [NeuralMona_MoE-4x7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q5_0.gguf) | Q5_0 | 15.48GB |
+| [NeuralMona_MoE-4x7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q5_K_S.gguf) | Q5_K_S | 15.48GB |
+| [NeuralMona_MoE-4x7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q5_K.gguf) | Q5_K | 15.96GB |
+| [NeuralMona_MoE-4x7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q5_K_M.gguf) | Q5_K_M | 15.96GB |
+| [NeuralMona_MoE-4x7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q5_1.gguf) | Q5_1 | 16.88GB |
+| [NeuralMona_MoE-4x7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q6_K.gguf) | Q6_K | 18.46GB |
+| [NeuralMona_MoE-4x7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/CultriX_-_NeuralMona_MoE-4x7B-gguf/blob/main/NeuralMona_MoE-4x7B.Q8_0.gguf) | Q8_0 | 23.9GB |
+
+
+
+
+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](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
+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
+
+```python
+!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"])
+```
+