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abideen
/
NexoNimbus-MoE-2x7B

Text Generation
Transformers
Safetensors
English
mixtral
Mixture of Experts
Merge
abideen/NexoNimbus-7B
mlabonne/NeuralMarcoro14-7B
conversational
text-generation-inference
Inference Endpoints
Model card Files Community
2
NexoNimbus-MoE-2x7B
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---
license: apache-2.0
tags:
- moe
- merge
- mergekit
- lazymergekit
- abideen/NexoNimbus-7B
- mlabonne/NeuralMarcoro14-7B
---

# NexoNimbus-MoE-2x7B

NexoNimbus-MoE-2x7B is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [abideen/NexoNimbus-7B](https://huggingface.co/abideen/NexoNimbus-7B)
* [mlabonne/NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B)

## 🧩 Configuration

```yaml
base_model: teknium/OpenHermes-2.5-Mistral-7B
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: abideen/NexoNimbus-7B
    positive_prompts:
    - "Mathematics"
    - "Physics"
    - "Chemistry"
    - "Biology"
    - "Medicine"
    - "Engineering"
    - "Computer Science"

    negative_prompts:
    - "History"
    - "Philosophy"
    - "Linguistics"
    - "Literature"
    - "Art and Art History"
    - "Music Theory and Composition"
    - "Performing Arts (Theater, Dance)"

  - source_model: mlabonne/NeuralMarcoro14-7B 
    positive_prompts:
    - "Earth Sciences (Geology, Meteorology, Oceanography)"
    - "Environmental Science"
    - "Astronomy and Space Science"
    - "Psychology"
    - "Sociology"
    - "Anthropology"
    - "Political Science"
    - "Economics"
    negative_prompts:
    - "Education"
    - "Law"
    - "Theology and Religious Studies"
    - "Communication Studies"
    - "Business and Management"
    - "Agricultural Sciences"
    - "Nutrition and Food Science"
    - "Sports Science"
```

## 💻 Usage

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "abideen/NexoNimbus-MoE-2x7B"

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"])
```